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This document lays out the key actions of the three year plan. It is specific and easy to read in that it lists each action separately, and then lists the competencies to be met underneath each heading. Also included was the prioritization of the strategies. The audience at the Summit was polled, so the strategies are listed with voting percentages which helps to see what is most important to achieving the plan. I found it to be very well broken down and to the point.

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Three-Year Roadmap: Prioritized Key Actions
The Summit participants created a collective 10-year vision and created "vision statements" for each pillar in the ideal, future state. The participants also created a number of strategies for the 3-year action plan, and the audience prioritized these strategies with the use of the electronic voting equipment provided by Audience Response System. The following lists the vision statements and prioritized key actions per pillar as agreed upon by the participants during the Summit. Vision Statement: Revolutionary leadership that drives, empowers, and executes the transformation of healthcare.

KEY ACTIONS: MANAGEMENT and LEADERSHIP

Publish TIGER outcomes and execute 3-year action plan. (40%)

Identify strategies for increasing the power, influence and presence of nursing through professional organizations in governmental and legal bodies. (35%)

Create specific leadership, management, education, and development strategies. (31%)

KEY ACTIONS: EDUCATION
Vision Statement: Collaborative learning communities that maximize the possibilities of technology toward knowledge development and dissemination, driving rapid deployment and implementation of best practices.

Seek funding to develop and implement learning innovations, foster faculty development, and ensure necessary infrastructure. (41%)

Identify, model, and implement collaborative partnerships among public and private academic, service, and industry enterprises. (40%)

Increase faculty acceptance of technology through education and training, incentives, and necessary supports. (38%)

Convene a national group to develop strategies for the recruitment, retention, and training of current and future workforces in informatics education, practice and research. (37%)

KEY ACTIONS: COMMUNICATION and COLLABORATION
Vision Statement: Standardized, person-centered, technology-enabled processes to facilitate teamwork and relationships across the continuum of care.

Localize TIGER vision by creating regional sharing (among practice, education, research, and informatics.) (36%)

Establish demonstration projects to model collaborative relationships across the continuum of care. (34%)

KEY ACTIONS: INFORMATICS DESIGN
Vision Statement: Evidence-based, interoperable intelligence systems that support education and practice to foster quality care and safety.

Develop guidelines for integrating informatics infrastructure: intelligence systems, IT hardware architecture, data documentation and warehousing, universal database and portals of knowledge. (24%)

Design systems that promote the mining and use of data for analysis, clinical decision-making, and measurement to improve the quality of care. (21%)

Create and implement multidisciplinary, multilingual standards. (20%)

KEY ACTIONS: INFORMATION TECHNOLOGY
Vision Statement: Smart, people-centered, affordable technologies that are universal, usable, useful, and standards-based.

Educate practice and education communities on IT standards and establish hard deadlines for adoption. (25%)

Strive for secure and reliable access to IT based on needs and standards. (21%)

KEY ACTIONS: POLICY
Vision Statement: Consistent, incentive-based initiatives (organizational and governmental) that support advocacy and coalition-building, achieving and resourcing an ethical culture of safety.

Establish consistent, agreed-upon IT standards. (50%)

Obtain funding for curriculum expansion, research, and practice in nursing informatics and information technology. (49%)

Identify incentives that support the adoption of innovative technologies. (35%)

Support a personal health record (PHR) for every person in the United States. (35%)

KEY ACTIONS: CULTURE
Vision Statement: A respectful, open system that leverages HIT across multiple disciplines in an environment where all stakeholders trust each other to work together toward the goal of high quality and safety.

National marketing campaign to promote the value of technology in a multidisciplinary way that supports an accepting culture.

Include HIT in every strategic plan, mission and vision statement.

Evaluate current processes and redesign as needed.

Use of HIT is embraced (and value is articulated) by executives, deans, all personnel (including point-of-care clinicians), and students with the acknowledged goal of high-quality care and safety (for patients, students, and staff).

Establish multidisciplinary teams that embrace a shared vision and operate cohesively to push for broad technology integration within/across the entire organization.

Develop mutual respect between/among clinicians who may bring different skills and knowledge; e.g., create/develop process in which experienced, veteran nurses mentor new nurses, and new nurses who know how to use HIT mentor the veteran nurses: "two-way mentoring."

The culture supports/promotes adoption of HIT and discourages "workarounds" by the users; culture is non-punitive in that when a user is doing their best to use HIT correctly and avoid errors, they are not "shamed" when they make a mistake and the organization can learn from that experience.

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Three-Year Roadmap: Prioritized Key Actions The Summit participants created a collective 10-year vision and created "vision statements" for each pillar in the ideal, future state. The participants also created a number of strategies for the 3-year action plan, and the audience prioritized these strategies with the use of the electronic voting equipment provided by Audience Response System.

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Perspectives from Nurse Managers on Informatics Competencies

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Perspectives from Nurse Managers on Informatics Competencies

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This article touched on research that had been completed on how well prepared nurse managers are to operate using informatics. Prior to reading this article, I had some ideas about the findings, and after reading it, I discovered my ideas were right on. Many nurse managers are wonderful at their jobs, but have been in the profession for 20 plus years, long before the age of technology overtook healthcare. Therefore many of these nurses lag behind in having informatics skills, even though they may have the knowledge. I think it would be beneficial for hospitals/healthcare systems to offer informatics CEs or certifications, especially for nurses in this situation, who have the knowledge and experience in nursing, but may just lack the technological skills.

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Background and Purpose. Nurse managers are in an excellent position for providing leadership and support within the institutions they serve and are often responsible for accessing information that is vital to the improvement of health facility processes and patients' outcomes. Therefore, competency in informatics is essential. The purposes of this study are to examine current informatics competency levels of nurse managers and to identify the variables that influence these competencies. Methods. A questionnaire designed to assess demographic information and nursing informatics competency was completed by 68 nurse managers. Multiple linear regression analysis was conducted to analyze the factors influencing informatics competency. Results. Descriptive analysis of the data revealed that informatics competency of these nurse managers was in the moderate range . Multiple linear regression analysis indicated that level of education, nursing administration experience, and informatics education/training were significant factors affecting competency levels. Conclusion. The factors identified in this study can serve as a reference for nurse managers who were wishing to improve their informatics competency, hospital administrators seeking to provide appropriate training, and nursing educators who were making decisions about nursing informatics curricula. These findings suggest that efforts to enhance the informatics competency of nurse managers have marked potential benefits.

1. Introduction

Since the introduction of computers in healthcare settings, the expected competency of nurses who work with information and communication technologies has been under review. In the past few years, the need for informatics competency has grown, driven by the use of technology in clinical practice. Informatics competency was defined as "the integration of knowledge, skills, and attitudes in the performance of various nursing informatics activities [ 1]." The importance of informatics competency for all nurses was articulated in the 2001 American Nurses Association's (ANA's) Scope and Standards of Practice for Nursing Informatics. This document divided informatics competency into three general categories: computer literacy skills, information literacy skills, and overall informatics competency [ 2].

In 2006, the Technology Informatics Guiding Education Reform (TIGER) Summit introduced a vision of nurses using informatics in practice and education to support patient safety and offer high quality care. The resulting TIGER action plan for healthcare delivery organizations included a recommendation to develop a professional nursing model that supports the attainment and development of competency in informatics. The TIGER recommendation is to (1) allow informatics tools, principles, theories, and practices to be used by nurses to make healthcare safer, effective, efficient, patient centered, timely, and equitable and (2) interweave enabling technologies transparently into nursing practice and education, making information technology the stethoscope for the 21st century [ 3, 4].

The necessary informatics competencies for nurses and nursing students have been studied in several projects. For example, in 2002, Staggers et al. published the results of a Delphi study that identified core informatics competencies for nurses at four levels of practice: [ 5] beginning nurse, experienced nurse, informatics specialist, and informatics innovator. Staggers' study was the first research-based master list of informatics competencies for nurses to span four levels of nursing practice and cover competencies in computer skills, informatics knowledge, and informatics skills; many researchers have used this master list. Curran [ 6] added competencies related to evidence-based practice and extracted items from the master list to propose a list of nurse practitioner competencies. Ornes and Gassert [ 7] used the master list to determine if informatics competencies were included in the curriculum of a baccalaureate program.

Another research on informatics includes a survey of graduating baccalaureate nurses, the results found that information technology skills were in moderate level [ 8]; this study also revealed that the students were most confident in their Internet, word processing, and systems operations skills and least confident in care documentation and planning, valuing informatics knowledge, skills development, and data entry competencies. Pravikoff et al. [ 9] provided useful insights into the state of information literacy in 760 US clinical nurses. The authors found that 82% did not use the hospital library, 72% had not evaluated research reports in the last year, approximately 60% never searched CINAHL and MEDLINE, and 77% had never been trained to search bibliographic databases. The study concluded that RNs in USA were not ready for evidence-based practice because of the gaps in their information literacy and computer skills, limited access, and attitudes toward research.

Most studies of informatics competency involve nurses or students; there are few studies addressing the informatics competencies of nurse managers in clinical practice. One of the few studies that focused on nurse managers was conducted by Hart [ 10] in 2010. Based on the research of Staggers et al., Hart performed a three-round Delphi study to determine core informatics competencies for generic nurse managers that resulted in a list of 49 core informatics competencies.

At present, the use of nursing informatics has just begun in China; nursing informatics education lags behind other countries and the core informatics competencies for nurses or nurse managers in mainland China have not been established. Therefore, the purpose of this study was to investigate the level of informatics competencies of nurse managers and examine the influencing factors.

2. Methods

2.1. Participants

The convenience sample included volunteer nurse managers in a general hospital affiliated with Harbin Medical University. In October 2012, we sent questionnaires to every nurse manager (). Participants were told the aim of the study and that participation was voluntary, their anonymity would be protected, data would be handled with confidentiality, and they could withdraw from the study at any time. Names were replaced by code numbers upon return of the completed questionnaires to assure anonymity, and the completed questionnaires were examined by only the study investigators.

Sixty-eight valid questionnaires were returned (90.67%) and sixty-seven of the respondents were female. The demographic data of the participants was shown in Table 1. Over one-half of the participants (58.8%) were below 40 years old, and 35.3% held master's degrees. Some respondents had worked at the Second Affiliated Hospital of Harbin Medical University for more than 20 years (45.6%), and the same percentage had been in an executive position for less than five years. 55.9% respondents had not received education or training of informatics knowledge and skills.

2.2. Measure

The first part of the questionnaire consisted of queries about the general characteristics of the respondents, including the respondents' gender, age, highest level of education, job experience, and years of experience in nursing administration. The second part investigated nurse managers' informatics competencies using 49 informatics competency items identified by Hart in three categories: computer skills (25 items), for example, using e-mail and using the Internet to locate and download items of interest; informatics knowledge (20 items), such as recognizing the use and/or importance of nursing data for improving practice and identifying the basic components of the current computer system; and informatics skills (4 items), for example, performing basic trouble-shooting in applications.

The 49 competencies were translated and reviewed for relevance by an expert panel of four administrative nurses skilled in informatics. Discussions between the researchers and the author of the original questionnaire were held to verify the cross-cultural equivalence of the Chinese version. Respondents rated their personal skill level on each competency item using a five-point Likert-type scale: 1 = no experience or no competence, 2 = minimal skills, 3 = some or moderate skills, 4 = good skills or competence, and 5 = expert skill or fully competency. The final standard score was final score/highest possible score *100%.

2.3. Ethical Considerations

The protocol for this study complies with the Helsinki declaration and was approved by the appropriate committees and officials. The study was briefly explained to potential participants and they were informed that involvement was completely voluntary and that they could withdraw at any time with no negative impact.

2.4. Statistical Analysis

Statistical analysis was performed using the software package (SPSS 13.0, Beijing, China). Continuous variables are expressed as mean ± SD; categorical variables are presented as frequencies. We performed three sets of analyses. First, we calculated Cronbach's α to establish the internal consistency reliability of the questionnaire in our study population. Second, we conducted ANOVA to assess the differences among computer skills, informatics knowledge, and informatics skills. Third, a multiple linear regression analysis was used to assess the independent effects of demographic variables on informatics competencies. A two-tailed α-value of 0.05 was set for significance.

3. Results

3.1. Instrument Validity and Reliability

The content validity of the questionnaire was approved by five experts who were experienced in both nursing and information management. To confirm the validity of the questionnaire, we invited ten nurse managers to participate in a pretest to ensure that the items were easily understood. Cronbach's α values were higher than 0.85, which indicated high internal consistency.

3.2. Informatics Competencies

The standard score of informatics competencies for all respondents was , which represents a medium level of informatics competency. The scores on informatics knowledge were significantly higher than computer and informatics skills scores (). However, the difference between computer and informatics skills scores was not significant () (Table 2 and Figure 1).

3.3. Multiple Linear Regression Analysis Models

To understand the influencing factors of informatics competencies, multiple linear regression analysis was conducted. The overall model showed a statistically significant relationship. Education level, experience of nursing administration, and informatics education/training accounted for 71.2% of total variability in informatics competencies (Table 3).

4. Discussion

Nurse managers, as administrators within their hospital or health facility, need the knowledge and skills to use databases and information systems for the collection and interpretation of statistical data on patient care, including length of stay, staff rosters, patient acuity/dependency, budgets, and resource ordering [ 11]. Information competency is essential for managers, and it will become more important in the future as more sophisticated clinical information techniques are developed.

In the present study, we found that education level had a significant impact on informatics competencies. One possible reason for this relationship is that those with higher education levels have learned information retrieval and evidence-based nursing systematically. For example, the preparation of a master's thesis requires proficiency in technological skills, including keyboarding, word processing, multimedia presentations, online literature searches, e-mail correspondence, and use of the Internet, which cover many of the informatics skill items in this study.

The results also revealed that a nurse manager's computer and informatics skills were lower than his/her informatics knowledge. Computer skills encompass the use of computer hardware and software and allow for basic technological interface (i.e., the use of e-mail and the Internet, online literature searches, and use of application, such as word processing). Informatics skills are the use of methods, tools, and techniques particular to informatics, while informatics knowledge is the theoretical and conceptual basis for the specialty. For example, informatics skills include techniques and tools used in systems analysis and project management, while informatics knowledge includes familiarity with nursing classification and reasons for systems slowness [ 5]. The low scores in computer skills and informatics skills should be increased through education or training.

The results indicate that respondents' information related to education or training had a significant impact on their overall informatics competencies. However, more than one-half of the respondents (38) lacked any informatics knowledge and skills education in school or at work. This finding suggests that providing more informatics courses and training in school and at work is important. Academic courses in computer skills, informatics skills, evidenced-based practice, effectiveness research, and nursing administration or operational business management, including a required course in nursing informatics for nurses preparing them for an administrative career, could be developed in tandem with continuing education opportunities offered by academic or health institutions.

Nursing administration experience is a factor that negatively affected informatics competencies scores in this study. In other words, more administrative experience was associated with a lower level of informatics competencies. This is closely connected with China's national conditions, and there are several possible explanations. The respondents with more administrative experience are from an era when computers were used less frequently in school or at work, trained at a secondary level vocational school (the primary training site for nurses in China before 2000), or were affected by conventional thinking and traditional views as their administrative experience grew. Therefore, nurse managers with administrative experience should be targeted for computer and informatics continuing education, which will allow them keeping pace with the rapid development of information technology, particularly in the field of patient safety and care.

4.1. Limitations

There are several limitations to this study. The participants were not randomly selected; therefore, the respondents may not adequately represent the population of interest. The other limitation is that the informatics competencies data was collected through a self-report questionnaire. Consequently, nurse managers could under- or overestimate their informatics competency. However, since the self-ratings were not considered in a performance appraisal, there was little motivation to overestimate. To objectively assess informatics competencies, future studies should use other tools to measure the true level of the participants' informatics competency.

4.2. Conclusion

In a field where technology changes on a daily basis, it is crucial for nurses and nurse managers to stay up-to-date on trends and information [ 12]. The results of this study confirmed that education level, experience in nursing administration, and information education/training were significant factors affecting informatics competency. Nursing informatics knowledge and skills are essential in modern nursing education (initial and continuing education programs) to ensure that nurses are proficient in technological and cutting-edge clinical applications [ 13]. Thereby, the nurses and nurse managers are able to offer the highest levels of patient care and safety.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors' Contribution

Li Yang and Dan Cui contributed equally to this work.

Acknowledgments

This project was supported by Ministry of Health of Heilongjiang Province (no. 2010-080). The authors acknowledge Michael D. Hart who provided the Informatics Competencies Questionnaire for nurse managers. They also thank all of the individuals who contributed to the study.

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Perspectives from Nurse Managers on Informatics Competencies

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Background and Purpose . Nurse managers are in an excellent position for providing leadership and support within the institutions they serve and are often responsible for accessing information that is vital to the improvement of health facility processes and patients’ outcomes. Therefore, competency in informatics is essential.

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This article gives some information on what Healthcare Informatics is and how it helps to improve the current state of healthcare. As new policies are put into place, healthcare informatics are more at the forefront than ever to keep up with an ever evolving state of healthcare, making providers more available to patients and allowing patients to take more of an active role in their own healthcare.

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Abstract

Healthcare Informatics is defined as "the integration of healthcare sciences, computer science, information science, and cognitive science to assist in the management of healthcare information" (Saba & McCormick, 2015, p. 232). Nursing Informatics is a subset of informatics, specific to the field and the role of the nurse in the healthcare setting. The American Nurses Association (ANA) identified nursing informatics as "a specialty that integrates nursing, science, computer science, and information science to manage and communicate data, information, and knowledge in nursing practice" (ANA, 2001, p.17). Healthcare and nursing informatics are both vastly growing fields within the medical field and are continuously incorporating new and evolving technology. Both have been around for the past three decades, at least. The technology boom at the turn of the century has helped informatics and information systems further evolve. Enhanced delivery of care, improved health outcomes, and advanced patient education are just a few aspects that have improved. With any new technology or innovation there are implications, some foreseeable and some that come to light after the unveiling of the new process or product: some impacts that are most notable are clinical, managerial, and policy implications. This paper explores the implications, (both constructive and adverse), that are the most notable in today's healthcare world within the healthcare and nursing informatics fields.

Introduction

Healthcare Informatics is defined as "the integration of healthcare sciences, computer science, information science, and cognitive science to assist in the management of healthcare information" (Saba & McCormick, 2015, Pg. 232). Nursing Informatics is a subset of informatics, specific to the field and the roll of the nurse in the healthcare setting. The American Nurses Association (ANA) identified nursing informatics as "a specialty that integrates nursing, science, computer science, and information science to manage and communicate data, information, and knowledge in nursing practice" (ANA, 2001, Pg.17). Healthcare and nursing informatics are both vastly growing fields within the medical field and are continuously incorporating new and evolving technology. Both have been around for the past three decades, at least. The technology boom at the turn of the century has helped informatics and information systems further evolve. Enhanced delivery of care, improved health outcomes, and advanced patient education are just a few aspects that have improved. With any new technology or innovation there are implications, some foreseeable and some that come to light after the unveiling of the new process or product. Some impacts that are most notable are clinical, managerial, and policy implications. This paper will explore the implications, both constructive and adverse, most notable in today's healthcare world within the healthcare informatics and nursing informatics fields.

Clinical Implications

The use of informatics is seen in a multitude of processes within the clinical setting. Whether inpatient or outpatient, clinicians and patients utilize online portal systems, electronic medical records, data collection devices such as vital sign machines and glucometers, as well as personal data devices and email, to name a few. When considering these systems and how they affect the process and flow of the clinical setting, it is important to not only consider the technology at hand but also the workflow and the data collection process. Norris, Hinrichs, & Brown, tell us "gaps are present between the technology and the process. Informatics can help bridge that gap. Skills needed include understanding of data collection, storage, and extraction, in addition to an appreciation for the power of data to drive and inform practice" (2015, p. 11-12). Healthcare informaticists, especially nursing informaticists, are the prime group to help bridge that gap. Without a strong clinician presence in the building and implementation process, gaps will remain. With healthcare informaticists involved in the development, a strong product can be delivered that is usable to all members of the healthcare team.

President Obama signed the American Recovery and Reinvestment Act (ARRA) into law in 2009. This law includes the Health Information and Technology for Economic Clinical Health Act (HITECH). HITECH formulated the Meaningful Use (MU) program under the Centers for Medicare and Medicaid Services (CMS). Meaningful Use incentivizes providers to use their Electronic Health Records (EHR) by financially rewarding them when demonstrating their use, but also poses a threat by penalizing them if not used in the future (Norris, Hinrichs, & Brown, 2015). These Acts greatly impact both nursing and the healthcare field clinically. Providers who do not typically use an EHR are now finding themselves in a learning curve while still maintaining their patient caseload, and providing comprehensive care. However this vast amount of data collection across a multitude of healthcare settings has provided opportunity in enhancements of care. While the initial rollout of these initiatives may cause chaos, the benefits of these initiatives will allow clinicians to provide comprehensive, safe, evidence-based care to all of their patients. Health care staff will be able to quickly and safely access pertinent information on their patients throughout the health institution and beyond.

In addition to enhancements in care and improved evidence-based practice, the increased amount of data collected by EHRs and other data systems has created a massive amount of data that hospitals and health care organizations now have to manage and analyze. "This has led to increased demand for professionals who are well versed in both informatics and medicine. To meet this demand, the American Medical Informatics Association spearheaded the establishment of professional-level education and certification for physicians in informatics" (Simpao, Ahumada, Galvez, & Rehman, 2014, p. 45). We are now seeing a new influx of healthcare professionals entering this sub-specialty. The needs for these professionals to collect, interpret, and study the data and the operation of information systems is crucial to the success and usability of these systems. Lehman, Shorte, & Gundlapalli (2013) stated that "it is reasonable to predict that the number of leadership positions in clinical informatics with titles and roles such as chief medical informatics officer, chief health informatics officer, directors of clinical informatics, and lead of EHR implementation, etc., will increase in the near future" (p. 528).

Managerial Implications

The use of healthcare informatics is not only prevalent in the clinical setting but also in the managerial setting. In our society, communication comes in a multitude of applications: verbal, physical, and now electronic. We often find that individuals can contact us by using various applications such as telephone, fax, pager, instant messenger, email, and so on. It is crucial for those in managerial positions to utilize these information systems to aid their work and the work of their staff while being mindful to set limits and standards. Time management is a prevalent issue in the healthcare setting, thus the use of informatics to aid and organize and not create barriers is essential. However, these expansive communication tools can create stress and feelings of intake overload. Marquis & Huston (2013) suggested "to reduce interruptions and distractions, individuals should shut off their email, isolate themselves, and make sure the environment around them is working to strengthen their willpower and focus" (p. 189). Healthcare professionals need to ensure that they are utilizing informatics to aid their work and time management, not impeding them. Marquis & Huston (2013) go on to note that "creating a workspace that has a desk with enough clear space to do your work, good lighting, and a comfortable chair" is crucial (p.190).

Information systems in the managerial role often consist of interpreting information and modifying data to be utilized in decision-making processes. Managerial programs facilitate payroll functions, streamline material control, and assist with financial and administrative factors of their role (Pacheco de Souza, Santiago & Izu, 2015, p.7284). As seen in clinical implications, Meaningful Use also plays a role in the role of the manager. It is important for "nurse managers to utilize information from the EHR to show Meaningful Use and are important to the process of determining how information is organized and categorized within the EHR" (Biddle & Milstead, 2016, p.12). Without a nurse manager and other administrator identification of how to best capture and report Meaningful Use information, the healthcare organization may not be compliant with this aspect of the HITECH Act.
The continuously growing field of informatics is of great benefit to healthcare managers at all levels. Using this growing technology can greatly benefit their role and improve the function of their staff. Pacheco de Souza, Santiago, & Izu (2015) go on to acknowledge that this advancing technology "should be utilized as a management tool, giving power and autonomy to nurse managers in more efficient use of available technological resources" (p.7285). One example of this is where a unit may identify incomplete documentation on a given point of care. The nurse manager can then construct a workgroup of colleagues who are involved in the process including an IT specialist. Once this group identifies the issues and improved processes, they can present their recommendations to administration, ultimately improving documentation in the EHR (Biddle & Milstead, 2016). Thus, healthcare information systems can improve communication, time-management, and delivery of information amongst staff and patients.

Policy Implications

As previously noted in clinical implications and managerial implications, the amount of data that is now available from EHRs and other forms of information systems, is larger than healthcare providers have ever dealt with. Policy makers are now able to utilize this data to inform their decision-making about public-health issues. It is crucial now, more than ever, that epublic health staff are available at various levels of the health system to develop skills and knowledge to better utilize existing datasets. Adair (2012) identified guidelines to help public health officials understand, interpret, and best utilize this influx of information. "These guidelines were developed to assist public health officials assess the quality of existing health data, and effectively utilize such data to compute indicators to inform health sector policy-making" (p. 53). If policymakers are not able to thoroughly understand data derived from the utilization of electronic health records, patient portals, and other data sources, then people cannot expect them to make sound judgments when voting on new laws.

In this age of technology it is important that healthcare providers, managers, and informaticists are mindful of adhering to HIPAA regulations to ensure that growing technology is incorporated in current policies. With the use of EHRs and mobile health applications there is an increased "liability for healthcare organizations if there is a breach in patient confidentiality or privacy, which is why organizations must have policies in place that guide the use of telehealth and communication" (Biddle & Milstead, 2016). Ensuring proper policy is in place and that changes are made as needed when new technology is introduced is crucial.

McGowan, Cusack, and Bloomrosen (2012) wrote, "since 2006, the American Medical Informatics Association (AMIA) has convened an annual investigational Health Policy meeting to examine Cutting edge issues in healthcare and health information technology (health IT) policy" (p. 460). These meetings will identify and discuss potential issues with health IT and informatics as well as develop a plan and report to present to our nation's policymakers so they are well informed when making and voting for pertinent policies and laws (McGowan, Cusack, & Bloomrosen, 2012). It is important that health professionals, information technology developers, and policymakers are able to communicate and work together in the interest of delivering the most efficient and safest patient care.

Meaningful Use is one example of health policy and health informatics working together. The initiative behind Meaningful Use was to encourage the use of Electronic Health Records in all health systems nationwide. However with this initiative, some providers may find themselves not being truthful in their documentation in order to meet the incentives set by the HITECH legislation. McGowan, Cusack, and Bloomrosen (2012), identified that "with the mandate for meaningful use of health IT by providers, there is ample opportunity for inappropriate and even fraudulent or illegal activities, ranging from lack of oversight to deliberate misrepresentation" (p. 461). Although dishonorable, some providers may lie to ensure their practice is able to receive the best monetary incentive by adhering to Meaningful Use measures and may alter their documentation to fit the criteria.

McGowan, Cusack, and Bloomrosen (2012) also identified policy concerns with health IT and informatics when discussing federal and state roles. Often federal and state regulations and health IT initiatives lack coordination. "Without explicit guidelines, proprietary state systems may be created, with many not being able to connect to the national health information infrastructure and some not acknowledging the healthcare systems that cross state lines" (McGowan, Cusack, and Bloomrosen, 2012, p.462). It is crucial that not only health professionals and government coordinate their objectives and policy, but also governing officials of the state and federal government. Conflicting or uncoordinated health initiatives may lead to patient and public mistrust as well as financial complications with both the government and health organizations.

Summary

Health informatics and nursing informatics are very relevant in evolving health systems. New technology and initiatives are constantly being developed. These new innovations do not go without implications in the clinical setting, managerial setting, and the policy setting. It is crucial that all participants whether it is the nurse, manager, provider, politician, lobbyist, or President of the United States remain coordinated. Multidisciplinary unity is crucial to ensure public trust in our health systems and to provide safe and effective patient care.

Citation: Sweeney, J. (Feb, 2017). Healthcare Informatics. Online Journal of Nursing Informatics (OJNI), 21(1).
The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.
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This paper explores the implications that are most notable in today's healthcare world within the nursing and healthcare informatics fields.

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How Healthcare Informatics is Transforming Healthcare

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How Healthcare Informatics is Transforming Healthcare

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There are many ways informatics benefit healthcare and this article highlights several of those ways. The addition of informatics to healthcare has the added bonus of saving money. It has allowed much more patient participation in their own healthcare. Knowledge can be shared much more easily with technology at the forefront. This increased technology also makes it much simpler to coordinate care, and to have much better continuity of care because providers can constantly be, and keep in touch concerning their patients. By allowing patients to participate in their own healthcare, better outcomes are achieved.

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The fact that technology is rapidly transforming health care should come as no surprise to anyone. From robotic arms that perform surgery to nanorobots that deliver drugs through the bloodstream , the days of being tended to by the human country doctor seem to have fully given way to machines and software more in keeping with the tools of Dr. McCoy from "Star Trek."

However, technology's evolutionary impact on health care isn't all shooting stars and bells and whistles. Some of health care's most important changes can slip beneath the radar due to their more pedestrian presentation, but that doesn't mean they aren't just as revolutionary as mini robots zipping through veins. Take the burgeoning field of health informatics , for example. A specialization that combines communications, information technology, and health care to improve patient care, it's at the forefront of the current technological shift in medicine. Here are six ways it's already transforming health care.

Health care isn't just expensive; it's wasteful. It's estimated that half of all medical expenditures are squandered on account of repeat procedures, the expenses associated with more traditional methods of sharing information, delays in care, errors in care or delivery, and the like. With an electronic and connected system in place, much of that waste can be curbed. From lab results that reach their destination sooner improving better an more timely care delivery to reduced malpractice claims, health informatics reduces errors, increases communication, and drives efficiency where before there was costly incompetence and obstruction.

There's a reason medicine is referred to as a "practice," and it's because health care providers are always learning more and honing their skills. Health informatics provides a way for knowledge about patients, diseases, therapies, medicines, and the like to be more easily shared. As knowledge is more readily passed back and forth between providers and patients, the practice of medicine gets better - something that aids everyone within the chain of care, from hospital administrators and physicians to pharmacists and patients.

3. Patient Participation

When patients have electronic access to their own health history and recommendations, it empowers them to take their role in their own health care more seriously. Patients who have access to care portals are able to educate themselves more effectively about their diagnoses and prognoses, while also keeping better track of medications and symptoms. They are also able to interact with doctors and nurses more easily, which yields better outcomes, as well. Health informatics allows individuals to feel like they are a valuable part of their own health care team, because they are.

4. The Impersonalization of Care

One criticism of approaching patient care through information and technology is that care is becoming less and less personal. Instead of a doctor getting to know a patient in real time and space in order to best offer care, the job of "knowing" is placed on data and algorithms.

As data is gathered regarding a patient, algorithms can be used to sort it in order to determine what is wrong and what care should be offered. It remains to be seen what effects this data-driven approach will have over time, but regardless, since care is getting less personal, having a valid and accurate record that the patient and his care providers can access remains vital.

5. Increased Coordination

Health care is getting more and more specialized, which means most patients receive care from as many as a dozen different people in one hospital stay. This increase in specialists requires an increase in coordination, and it's health informatics that provides the way forward. Pharmaceutical concerns, blood levels, nutrition, physical therapy, X-rays, discharge instructions - it's astonishing how many different conversations a single patient may have with a team of people regarding care, and unless those conversations and efforts are made in tandem with one another, problems will arise and care will suffer. Health informatics makes the necessary coordination possible.

The most important way in which informatics is changing health care is in improved outcomes. Electronic medical records result in higher quality care and safer care as coordinated teams provide better diagnoses and decrease the chance for errors. Doctors and nurses are able to increase efficiency, which frees up time to spend with patients, and previously manual jobs and tasks are automated, which saves time and money - not just for hospitals, clinics, and providers, but for patients, insurance companies, and state and federal governments, too.

Health care is undergoing a massive renovation thanks to technology, and health informatics is helping to ensure that part of the change results in greater efficiency, coordination, and improved care.

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6 Ways Health Informatics Is Transforming Health Care - HIT Consultant

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The fact that technology is rapidly transforming health care should come as no surprise to anyone. From robotic arms that perform surgery to nanorobots that deliver drugs through the bloodstream , the days of being tended to by the human country doctor seem to have fully given way to machines and software more in keeping with the tools of Dr. McCoy from "Star Trek."

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Perspectives from Nurse Managers on Informatics Competencies

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Perspectives from Nurse Managers on Informatics Competencies

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This article touched on how effective Nurse Managers are on mastering informatics competencies. Before I started reading this article, I had some ideas about how nurse managers scored in informatics competencies, and after reading it, discovered that my ideas were right on. Often, nurse managers are nurses who have been in the profession for 20 years or more, long before the age of computers and software systems conquering the healthcare industry. Many of these nurse managers are great at their job, but lag behind in technology. This article proves that this is true. I think it would be beneficial for hospitals to offer/require nurse managers to earn credit in continuing education in Informatics so that they can stay up to date with the current system of healthcare.

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Background and Purpose. Nurse managers are in an excellent position for providing leadership and support within the institutions they serve and are often responsible for accessing information that is vital to the improvement of health facility processes and patients' outcomes. Therefore, competency in informatics is essential. The purposes of this study are to examine current informatics competency levels of nurse managers and to identify the variables that influence these competencies. Methods. A questionnaire designed to assess demographic information and nursing informatics competency was completed by 68 nurse managers. Multiple linear regression analysis was conducted to analyze the factors influencing informatics competency. Results. Descriptive analysis of the data revealed that informatics competency of these nurse managers was in the moderate range . Multiple linear regression analysis indicated that level of education, nursing administration experience, and informatics education/training were significant factors affecting competency levels. Conclusion. The factors identified in this study can serve as a reference for nurse managers who were wishing to improve their informatics competency, hospital administrators seeking to provide appropriate training, and nursing educators who were making decisions about nursing informatics curricula. These findings suggest that efforts to enhance the informatics competency of nurse managers have marked potential benefits.

1. Introduction

Since the introduction of computers in healthcare settings, the expected competency of nurses who work with information and communication technologies has been under review. In the past few years, the need for informatics competency has grown, driven by the use of technology in clinical practice. Informatics competency was defined as "the integration of knowledge, skills, and attitudes in the performance of various nursing informatics activities [ 1]." The importance of informatics competency for all nurses was articulated in the 2001 American Nurses Association's (ANA's) Scope and Standards of Practice for Nursing Informatics. This document divided informatics competency into three general categories: computer literacy skills, information literacy skills, and overall informatics competency [ 2].

In 2006, the Technology Informatics Guiding Education Reform (TIGER) Summit introduced a vision of nurses using informatics in practice and education to support patient safety and offer high quality care. The resulting TIGER action plan for healthcare delivery organizations included a recommendation to develop a professional nursing model that supports the attainment and development of competency in informatics. The TIGER recommendation is to (1) allow informatics tools, principles, theories, and practices to be used by nurses to make healthcare safer, effective, efficient, patient centered, timely, and equitable and (2) interweave enabling technologies transparently into nursing practice and education, making information technology the stethoscope for the 21st century [ 3, 4].

The necessary informatics competencies for nurses and nursing students have been studied in several projects. For example, in 2002, Staggers et al. published the results of a Delphi study that identified core informatics competencies for nurses at four levels of practice: [ 5] beginning nurse, experienced nurse, informatics specialist, and informatics innovator. Staggers' study was the first research-based master list of informatics competencies for nurses to span four levels of nursing practice and cover competencies in computer skills, informatics knowledge, and informatics skills; many researchers have used this master list. Curran [ 6] added competencies related to evidence-based practice and extracted items from the master list to propose a list of nurse practitioner competencies. Ornes and Gassert [ 7] used the master list to determine if informatics competencies were included in the curriculum of a baccalaureate program.

Another research on informatics includes a survey of graduating baccalaureate nurses, the results found that information technology skills were in moderate level [ 8]; this study also revealed that the students were most confident in their Internet, word processing, and systems operations skills and least confident in care documentation and planning, valuing informatics knowledge, skills development, and data entry competencies. Pravikoff et al. [ 9] provided useful insights into the state of information literacy in 760 US clinical nurses. The authors found that 82% did not use the hospital library, 72% had not evaluated research reports in the last year, approximately 60% never searched CINAHL and MEDLINE, and 77% had never been trained to search bibliographic databases. The study concluded that RNs in USA were not ready for evidence-based practice because of the gaps in their information literacy and computer skills, limited access, and attitudes toward research.

Most studies of informatics competency involve nurses or students; there are few studies addressing the informatics competencies of nurse managers in clinical practice. One of the few studies that focused on nurse managers was conducted by Hart [ 10] in 2010. Based on the research of Staggers et al., Hart performed a three-round Delphi study to determine core informatics competencies for generic nurse managers that resulted in a list of 49 core informatics competencies.

At present, the use of nursing informatics has just begun in China; nursing informatics education lags behind other countries and the core informatics competencies for nurses or nurse managers in mainland China have not been established. Therefore, the purpose of this study was to investigate the level of informatics competencies of nurse managers and examine the influencing factors.

2. Methods

2.1. Participants

The convenience sample included volunteer nurse managers in a general hospital affiliated with Harbin Medical University. In October 2012, we sent questionnaires to every nurse manager (). Participants were told the aim of the study and that participation was voluntary, their anonymity would be protected, data would be handled with confidentiality, and they could withdraw from the study at any time. Names were replaced by code numbers upon return of the completed questionnaires to assure anonymity, and the completed questionnaires were examined by only the study investigators.

Sixty-eight valid questionnaires were returned (90.67%) and sixty-seven of the respondents were female. The demographic data of the participants was shown in Table 1. Over one-half of the participants (58.8%) were below 40 years old, and 35.3% held master's degrees. Some respondents had worked at the Second Affiliated Hospital of Harbin Medical University for more than 20 years (45.6%), and the same percentage had been in an executive position for less than five years. 55.9% respondents had not received education or training of informatics knowledge and skills.

2.2. Measure

The first part of the questionnaire consisted of queries about the general characteristics of the respondents, including the respondents' gender, age, highest level of education, job experience, and years of experience in nursing administration. The second part investigated nurse managers' informatics competencies using 49 informatics competency items identified by Hart in three categories: computer skills (25 items), for example, using e-mail and using the Internet to locate and download items of interest; informatics knowledge (20 items), such as recognizing the use and/or importance of nursing data for improving practice and identifying the basic components of the current computer system; and informatics skills (4 items), for example, performing basic trouble-shooting in applications.

The 49 competencies were translated and reviewed for relevance by an expert panel of four administrative nurses skilled in informatics. Discussions between the researchers and the author of the original questionnaire were held to verify the cross-cultural equivalence of the Chinese version. Respondents rated their personal skill level on each competency item using a five-point Likert-type scale: 1 = no experience or no competence, 2 = minimal skills, 3 = some or moderate skills, 4 = good skills or competence, and 5 = expert skill or fully competency. The final standard score was final score/highest possible score *100%.

2.3. Ethical Considerations

The protocol for this study complies with the Helsinki declaration and was approved by the appropriate committees and officials. The study was briefly explained to potential participants and they were informed that involvement was completely voluntary and that they could withdraw at any time with no negative impact.

2.4. Statistical Analysis

Statistical analysis was performed using the software package (SPSS 13.0, Beijing, China). Continuous variables are expressed as mean ± SD; categorical variables are presented as frequencies. We performed three sets of analyses. First, we calculated Cronbach's α to establish the internal consistency reliability of the questionnaire in our study population. Second, we conducted ANOVA to assess the differences among computer skills, informatics knowledge, and informatics skills. Third, a multiple linear regression analysis was used to assess the independent effects of demographic variables on informatics competencies. A two-tailed α-value of 0.05 was set for significance.

3. Results

3.1. Instrument Validity and Reliability

The content validity of the questionnaire was approved by five experts who were experienced in both nursing and information management. To confirm the validity of the questionnaire, we invited ten nurse managers to participate in a pretest to ensure that the items were easily understood. Cronbach's α values were higher than 0.85, which indicated high internal consistency.

3.2. Informatics Competencies

The standard score of informatics competencies for all respondents was , which represents a medium level of informatics competency. The scores on informatics knowledge were significantly higher than computer and informatics skills scores (). However, the difference between computer and informatics skills scores was not significant () (Table 2 and Figure 1).

3.3. Multiple Linear Regression Analysis Models

To understand the influencing factors of informatics competencies, multiple linear regression analysis was conducted. The overall model showed a statistically significant relationship. Education level, experience of nursing administration, and informatics education/training accounted for 71.2% of total variability in informatics competencies (Table 3).

4. Discussion

Nurse managers, as administrators within their hospital or health facility, need the knowledge and skills to use databases and information systems for the collection and interpretation of statistical data on patient care, including length of stay, staff rosters, patient acuity/dependency, budgets, and resource ordering [ 11]. Information competency is essential for managers, and it will become more important in the future as more sophisticated clinical information techniques are developed.

In the present study, we found that education level had a significant impact on informatics competencies. One possible reason for this relationship is that those with higher education levels have learned information retrieval and evidence-based nursing systematically. For example, the preparation of a master's thesis requires proficiency in technological skills, including keyboarding, word processing, multimedia presentations, online literature searches, e-mail correspondence, and use of the Internet, which cover many of the informatics skill items in this study.

The results also revealed that a nurse manager's computer and informatics skills were lower than his/her informatics knowledge. Computer skills encompass the use of computer hardware and software and allow for basic technological interface (i.e., the use of e-mail and the Internet, online literature searches, and use of application, such as word processing). Informatics skills are the use of methods, tools, and techniques particular to informatics, while informatics knowledge is the theoretical and conceptual basis for the specialty. For example, informatics skills include techniques and tools used in systems analysis and project management, while informatics knowledge includes familiarity with nursing classification and reasons for systems slowness [ 5]. The low scores in computer skills and informatics skills should be increased through education or training.

The results indicate that respondents' information related to education or training had a significant impact on their overall informatics competencies. However, more than one-half of the respondents (38) lacked any informatics knowledge and skills education in school or at work. This finding suggests that providing more informatics courses and training in school and at work is important. Academic courses in computer skills, informatics skills, evidenced-based practice, effectiveness research, and nursing administration or operational business management, including a required course in nursing informatics for nurses preparing them for an administrative career, could be developed in tandem with continuing education opportunities offered by academic or health institutions.

Nursing administration experience is a factor that negatively affected informatics competencies scores in this study. In other words, more administrative experience was associated with a lower level of informatics competencies. This is closely connected with China's national conditions, and there are several possible explanations. The respondents with more administrative experience are from an era when computers were used less frequently in school or at work, trained at a secondary level vocational school (the primary training site for nurses in China before 2000), or were affected by conventional thinking and traditional views as their administrative experience grew. Therefore, nurse managers with administrative experience should be targeted for computer and informatics continuing education, which will allow them keeping pace with the rapid development of information technology, particularly in the field of patient safety and care.

4.1. Limitations

There are several limitations to this study. The participants were not randomly selected; therefore, the respondents may not adequately represent the population of interest. The other limitation is that the informatics competencies data was collected through a self-report questionnaire. Consequently, nurse managers could under- or overestimate their informatics competency. However, since the self-ratings were not considered in a performance appraisal, there was little motivation to overestimate. To objectively assess informatics competencies, future studies should use other tools to measure the true level of the participants' informatics competency.

4.2. Conclusion

In a field where technology changes on a daily basis, it is crucial for nurses and nurse managers to stay up-to-date on trends and information [ 12]. The results of this study confirmed that education level, experience in nursing administration, and information education/training were significant factors affecting informatics competency. Nursing informatics knowledge and skills are essential in modern nursing education (initial and continuing education programs) to ensure that nurses are proficient in technological and cutting-edge clinical applications [ 13]. Thereby, the nurses and nurse managers are able to offer the highest levels of patient care and safety.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors' Contribution

Li Yang and Dan Cui contributed equally to this work.

Acknowledgments

This project was supported by Ministry of Health of Heilongjiang Province (no. 2010-080). The authors acknowledge Michael D. Hart who provided the Informatics Competencies Questionnaire for nurse managers. They also thank all of the individuals who contributed to the study.

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Perspectives from Nurse Managers on Informatics Competencies

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Background and Purpose . Nurse managers are in an excellent position for providing leadership and support within the institutions they serve and are often responsible for accessing information that is vital to the improvement of health facility processes and patients’ outcomes. Therefore, competency in informatics is essential.

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This article discusses how interoperability in nursing could help to decrease confusion, improve efficiency and put a stop to the redundancy of asking the same questions at every visit and at every patient "stop" (admission, triage, treatment, discharge). By decreasing this redundancy, it also decreases the chance of confusion, of repeating tests that have already been done, and costing the patient more money. This article makes the argument of not letting "perfect" get in the way of progress, therefore accepting some data with errors to facilitate interoperability. Personally, I think this article nails it on how imperative interoperability is to excellence in nursing and healthcare in general.

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Estimated read time: 5 minutes

When a patient arrives at a health care organization, they are greeted by a nurse who asks a series of questions and collects a number of documents as part of the admission process. While providing this information is crucial for receiving quality care, chances are the patient has been through this process already at least once during a previous care encounter.

This is just one example of where interoperability, the capacity of different information systems, devices or applications to access and exchange data within and across organizational boundaries, could play a role in eliminating the redundant, time-consuming and potentially life-threatening aspects of health care.

In this Q&A, Becky Fox, chief nursing informatics officer at Atrium Health, discusses why interoperability is key to decreasing documentation burden, clinician and provider burnout, and improving the overall health care experience.

The importance of interoperability for nurses and patients
Why is interoperability important to nursing staff, and how does it impact their day-to-day work?
Fox: We want to leverage all the data we have about patients to drive the best treatment and outcomes possible. Through mining the data with analytics, we can make the data more meaningful to clinicians, so they can focus on patient care rather than having to pull information together. In the health care industry, we are at a great place right now, where we can gather information from multiple sources, quickly synthesize to get to understanding and make data actionable.

Let me share an example. When a patient comes into a health care organization or an ambulatory setting, the nurse goes through a series of questions during the visit or admission process to gather information about the patient. Wouldn't it be great if the nurse already had detailed information from all the patient's previous health experiences and didn't have to go through the exercise of asking a lot of repetitive questions? Not only data from prior visits or encounters, but also information about the patient themselves and their own health management-from wearables, to health apps, and of course, demographic and social information. It would be great if all of that information could be taken in, digested, and presented to help the clinicians focus their questions and assessments. The end result would be quicker and better care.

That is what interoperability is all about: bringing multiple sources of information together and using health care technology, innovations and analytics to apply it more meaningfully to the care we provide to patients. It's also about being able to use the data to engage patients and consumers in changing behaviors in a positive way along their health care journey. Interoperability, coupled with usability, can help remove noise and mundane tasks from the workflow. It's an opportunity to collectively leverage information that has already been collected and contribute to the bigger picture of decreasing the burden of documentation, clinician burnout and improving the patient/consumer experience.
How does interoperability impact patient care?
There are still gaps that happen in health care. For example, when a patient goes into a care setting and fills out a series of questions, screening tools and has discussions with care providers, and then they go to a different health care setting, and they have to do it all again. Anytime you are repeating processes, things can be overlooked, missed or not understood. It's also annoying to the patient! They can end up with redundant testing, procedures and assessments, and all these examples have a financial cost to the patient, the institution and payors. There is also the cost of time and potential delay in care and decision-making when, for instance, a lab test has to be repeated.

We, as health care leaders, all collectively must address this by working to improve interoperability, the usability of data and how to better share all data across all systems in meaningful ways.

Overcoming roadblocks to health care interoperability
What are the biggest challenges that nurses face with regards to data access and exchange across systems?
While health care systems collect and document similar information about patients, it is not standardized. If all health care systems, regardless of their EHR vendor, collected and documented the same data, sharing information across hospitals and health systems would be significantly easier. There are some industry leaders working together to establish standards of such, and I think in the near future, you are going to see movement in this direction.

We are also at a place in the health IT journey in which technology and analytics are really making great strides, with even bigger strides to come. We realize we must collect data from multiple places, but we need data normalization to occur in the process, so that we are all speaking a common language. The implementation of standards that we can all agree upon is also extremely important.

The other thing that is shifting is our mindset in how we view and share our data.As we move in the direction of managing the health of large populations and look at data from more outside entities, we will need to be more accepting of data which may have errors in it.We can't be purists. We must look at data in terms of themes, directions and aggregation. We know there are going to be lessons learned. And, we can't let perfect get in the way of progress. We have to participate in this transformation and not be afraid of moving forward. As an industry we all need to work together to make sure we are sharing, streamlining and consolidating data. That is what will contribute to changing health care in the U.S. and around the world.
What are your recommendations to other health care systems on how to support their nurses through advancing interoperability?
At Atrium Health, we have a significant focus on our population health strategy. Looking at sources of data across different systems is another step in the journey of pulling information together and providing the best health care for our patients. If a health care system isn't yet on a population health journey and isn't sharing information with other health systems, then this would be a good place to start. We're all in this together, and our patients are going to expect that their health information is being shared, reviewed, analyzed and leveraged to give them the best care possible.

Health systems need to make sure they are participating in all avenues of interoperability and implementing standard tools, work and documentation. Nursing leaders must participate in the conversations of how-to bring interoperability to the beside and to ambulatory clinicians, so they can be their best for their patients.

Interoperability is not just for physicians, it's for all clinicians, and nursing needs to be at the table to make sure that all of the right data sources are being brought to the forefront in meaningful ways.
At Cerner, we're doing our part to innovate and transform health information technology for nurses. Learn more here.

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Why Interoperability Progress is Significant for Nursing

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Estimated read time: 5 minutes When a patient arrives at a health care organization, they are greeted by a nurse who asks a series of questions and collects a number of documents as part of the admission process.

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This article relates the development of the nursing information model to the achievement of interoperability. This model utilized research on hospital acquired pressure ulcers to research the standardizations that would help to lay out the groundwork for achieving interoperability. The research proved that it would take an incredible amount of rigor to standardize data responses. Current EHR systems would need the assistance of middleware to align responses enough to achieve interoperability. However, this model did provide a clear view for improving patient care coordination and efficiency.

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nursing information model process for interoperability

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The ability to share nursing data across organizations and electronic health records is a key component of improving care coordination and quality outcomes. Currently, substantial organizational and technical barriers limit the ability to share and compare essential patient data that inform nursing care. Nursing leaders at Kaiser Permanente and the U.S.

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This article breaks down the DIKW Hierarchy and explains what each level represents. It discusses how each level of the pyramid builds on the previous level. The most basic data becomes more valuable with information to give it context. That information is then synthesized into knowledge. Then finally it is transformed into wisdom in order to drive decisions to achieve a goal.

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Knowledge Pyramid, Wisdom Hierarchy and Information Hierarchy are some of the names referring to the popular representation of the relationships between data, information, knowledge and wisdom in the Data, Information, Knowledge, Wisdom (DIKW) Pyramid.

Like other hierarchy models, the Knowledge Pyramid has rigidly set building blocks - data comes first, information is next, then knowledge follows and finally wisdom is on the top.

Each step up the pyramid answers questions about the initial data and adds value to it. The more questions we answer, the higher we move up the pyramid. In other words, the more we enrich our data with meaning and context, the more knowledge and insights we get out of it. At the top of the pyramid, we have turned the knowledge and insights into a learning experience that guides our actions.

How to Scale Data Up the Knowledge Pyramid

So, let's have a look at the individual components of the Knowledge Pyramid and how we move from one to the next.

Data

Data is a collection of facts in a raw or unorganized form such as numbers or characters.

However, without context, data can mean little. For example, 12012012 is just a sequence of numbers without apparent importance. But if we view it in the context of 'this is a date', we can easily recognize 12th of January, 2012. By adding context and value to the numbers, they now have more meaning.

In this way, we have transformed the raw sequence of numbers into

Information

Information is the next building block of the DIKW Pyramid. This is data that has been "cleaned" of errors and further processed in a way that makes it easier to measure, visualize and analyze for a specific purpose.

Depending on this purpose, data processing can involve different operations such as combining different sets of data (aggregation), ensuring that the collected data is relevant and accurate (validation), etc. For example, we can organize our data in a way that exposes relationships between various seemingly disparate and disconnected data points. More specifically, we can analyze the Dow Jones index performance by creating a graph of data points for a particular period of time, based on the data at each day's closing.

By asking relevant questions about 'who', 'what', 'when', 'where', etc., we can derive valuable information from the data and make it more useful for us.

But when we get to the question of 'how', this is what makes the leap from information to

Knowledge

"How" is the information, derived from the collected data, relevant to our goals? "How" are the pieces of this information connected to other pieces to add more meaning and value? And, maybe most importantly, "how" can we apply the information to achieve our goal?

When we don't just view information as a description of collected facts, but also understand how to apply it to achieve our goals, we turn it into knowledge. This knowledge is often the edge that enterprises have over their competitors. As we uncover relationships that are not explicitly stated as information, we get deeper insights that take us higher up the DIKW pyramid.

But only when we use the knowledge and insights gained from the information to take proactive decisions, we can say that we have reached the final - 'wisdom' - step of the Knowledge Pyramid.

Wisdom

Wisdom is the top of the DIKW hierarchy and to get there, we must answer questions such as 'why do something' and 'what is best'. In other words, wisdom is knowledge applied in action.

We can also say that, if data and information are like a look back to the past, knowledge and wisdom are associated with what we do now and what we want to achieve in the future.

How Enterprises and Organizations Move Up the Knowledge Pyramid

One easy and fast way for enterprises to take the steps from data to information to knowledge and wisdom is to use Semantic Technologies such as Linked Data and Semantic Graph Databases. These technologies can create links between disparate and heterogeneous data and infer new knowledge out of existing facts.

Armed with this new knowledge, enterprises can climb up the mountain of wisdom and gain a competitive advantage by supporting their business decisions with data-driven analytics.

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What is the Data, Information, Knowledge, Wisdom (DIKW) Pyramid?

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Knowledge Pyramid, Wisdom Hierarchy and Information Hierarchy are some of the names referring to the popular representation of the relationships between data, information, knowledge and wisdom in the Data, Information, Knowledge, Wisdom (DIKW) Pyramid. Like other hierarchy models, the Knowledge Pyramid has rigidly set building blocks - data comes first, information is next, then knowledge follows and finally wisdom is on the top.

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This article covers how informatics relates to transforming data into wisdom that can be used to make decisions in the healthcare atmosphere. Following the use of the DIKW model, informatics can be used to process and combine all of that data into manageable bites that can be used to drive decisions concerning healthcare goals. This ultimately improves patient goals, by helping all of the pieces fit together to achieve goals and overcome challenges.

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If you had all the components to build the best car in the world but no plan to understand how they fit and work together, chances are you're not going to go very far. However, when correctly assembled, the full value will exceed the cost of the separate parts. And you will have a car to take you anywhere you want to go.
Health informatics is like that.
Each element of health informatics on its own is a valuable component or skill set. This includes information technology, data management, statistics and reporting, visual storytelling with data, clinical operations, health care policy and regulation, human resources, financial management, facilities management, communications, provider practice, and, of course, patients.

However, until or unless these components work together to help identify best practices, improve health outcomes, and control costs, they are just a collection of separate parts-no matter how sophisticated each may be.

Achieving that synergy is the definition of applied health informatics. This multi-disciplinary approach to organizational improvement involves every aspect of operations and leverages every department's interconnectedness and expertise. Its fundamental purpose is to facilitate informed decision making to achieve an organization's strategic or tactical goals. Its four essential elements are data, information, knowledge, and wisdom (DIKW). While the DIKW pyramid's origin is subject to debate, its structure is proven and trustworthy.
Data
The primary element of applied health information is raw data. By itself, data is essentially useless. Every health care organization produces reams of data comprised of facts or figures derived from daily operations, surveys, or measurements. Until or unless it is "applied," it just takes up computer storage space.
Information
Information is what the data tells us-the who, where, what, when, and how many. Data becomes useful and relevant information when we begin to organize and give it structure.
Knowledge
We gain knowledge when information is processed or framed by previous experiences and context. When we evaluate organized information and bring it to life with impactful, visual presentations, we begin to convey its meaning and purpose to build a body of standardized understanding and expert insight.
Wisdom
Returning to the metaphor of building a car, wisdom is where the rubber meets the road. It is where knowledge becomes integrated into practice, becoming the "right thing to do" in a given situation. It is more profound than knowledge because it includes an understanding of "why" and is supported by conscious, ethical decision making.
Implementing an applied health informatics approach
Any organization will have to overcome some barriers to developing an applied informatics program. This may include outmoded technology or insufficient data storage or processing capabilities. Where most health care organizations operate on tight budgets, there may be financial constraints. There may also be cultural barriers, or "we've never done that before" syndrome-essentially failing the imagination to see how an organization, including its staff and patients or members, might benefit.

Commonwealth Medicine has been leading the way in health care informatics since 1999. Our team of applied informatics experts provides a range of clinically-focused solutions to health and human services agencies and organizations, helping them reach their program goals of providing the appropriate care for the populations they serve.

We will evaluate your data sets to reveal what it can tell you. We collaborate with your team members to build a body of information, together with clear, strong visual storytelling, so that your data becomes relevant and usable to achieve your goals and overcome challenges.

We draw on our deep experience to design, deliver, and evaluate clinical programming with a particular focus in the areas of pharmacy, long-term services and supports, disability determination and medical review, managed care technical assistance, and laboratory testing. Our team includes clinicians, pharmacists, and technical experts.

In the end, wisdom gained enables our client organizations to understand how all the pieces fit together to take them where they want to go.

Learn more about Mark Sugrue and our Clinical Delivery & Informatics Solution.

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How Applied Informatics Improves Care and Strengthens Health Care

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If you had all the components to build the best car in the world but no plan to understand how they fit and work together, chances are you're not going to go very far. However, when correctly assembled, the full value will exceed the cost of the separate parts.

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Meaningful Use: What's it All About, and Why should Nurses Care? 

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Meaningful Use: What's it All About, and Why should Nurses Care? 

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This article by the American Journal of Nursing, is a brief article covering the "why" behing meaningful use for nurses. It explains why we should care and lays out the great things about moving to electronic health records. Some of those include having information readily available so that we can cut down on tests, and cutting down on filling out the same forms for patients. Tracking outcomes is another added benefit mentioned in this article. This article encourages nurses to get involved, educate ourselves about meaningful use, and make sure our voice is heard.

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By Susan McBride, PhD, RN, professor at Texas Tech University Health Science Center School of Nursing. McBride and fellow nurse informaticists Mari Tietze and John Delaney will be blogging here on the intersection of nursing and informatics in the coming days.

Share This Story, Choose Your Platform!

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'Meaningful Use': What's It All About, And Why Should Nurses Care?

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By Susan McBride, PhD, RN, professor at Texas Tech University Health Science Center School of Nursing. McBride and fellow nurse informaticists Mari Tietze and John Delaney will be blogging here on the intersection of nursing and informatics in the coming days.

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Meaningful Use: What's it all about , and why should nurses care? 

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Meaningful Use: What's it all about , and why should nurses care? 

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This article by the American Journal of Nursing discusses meaningful use as it applies to the implementation of electronic health records. It talks about the benefits of electronic health records and why we should want to implement them. Some of the benefits mentioned are having information readily available, cutting down on unnecessary testing, and not filling out the same forms over and over. It encourages nurses to make sure their voice is heard when it comes to implementing EHRs and meaningful use.

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By Susan McBride, PhD, RN, professor at Texas Tech University Health Science Center School of Nursing. McBride and fellow nurse informaticists Mari Tietze and John Delaney will be blogging here on the intersection of nursing and informatics in the coming days.

Share This Story, Choose Your Platform!

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'Meaningful Use': What's It All About, And Why Should Nurses Care?

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By Susan McBride, PhD, RN, professor at Texas Tech University Health Science Center School of Nursing. McBride and fellow nurse informaticists Mari Tietze and John Delaney will be blogging here on the intersection of nursing and informatics in the coming days.

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Electronic Health Records and Clinical Decision Support

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Electronic Health Records and Clinical Decision Support

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This is a brief article touching on many of the advantages of Electronic Health Records as they relate to Clinical Decision Support. Some of the advantages mentioned include a decrease in the use of inappropriate diagnostic testing and in legibility errors. Other advantages include the ability to set clinical parameters that will notify the physician, and trigger an appropriate order set. There are many alerts that can be put into place in EHRs, however this is a double-edged sword due to the alert/alarm fatigue that plagues many healthcare workers. Disadvantages discusssed include that the visit often becomes more about documentation, and less about the actual patient. EHRs can also increase the amount of documentation that providers have to do, as well.

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The electronic health record (EHR) has catalyzed change for clinicians by making available vast amounts of patient data and other information that can be used for clinical decision support (CDS).

Legislation in the US has created financial incentives to adopt an EHR and, more importantly, to derive meaningful use from the EHR. Although adoption of the EHR has been brisk in the US, its ultimate impact on patient care is unclear and will continue to evolve for many years as new uses and problems are discovered.

The sheer amount of patient information available in a single, often digitally searchable, electronic location rather than in volumes of paper can assist clinical decision making, even if the EHR serves as nothing more than a repository of information and images that can be searched, reviewed, and compared.

Following are some potential benefits of the EHR:

Clinical parameters (eg, vital signs, test results) contained within the EHR can be used to create alerts that notify the clinician or even trigger predetermined orders or order sets, diagnostic and therapeutic bundles, or clinical pathways. As an example, in a recent study, pediatric patients between the ages of 10 and 17 were randomized to usual care or care bolstered by EHR-linked clinical decision support (CDS) during visits where blood pressure was measured at each encounter. The CDS tool displayed blood pressures and percentiles, identified incident hypertension, and offered tailored order sets. Hypertension was identified in 1.7% of 31,579 patients over a 2-year period. When CDS was available, 17.1% of hypertensive patients were referred for weight loss and exercise counseling; 9.4% had additional testing for hypertension. However, when usual care was provided, only 3.9% of hypertensive patients were referred for counseling and 4.2% had additional testing. The authors conclude that EHR-linked clinical decision support had a significant and beneficial effect on the recognition of pediatric hypertension ( 1 References The electronic health record (EHR) has catalyzed change for clinicians by making available vast amounts of patient data and other information that can be used for clinical decision support ... read more ).

Providing EHR-linked CDS at the time of diagnostic test ordering may decrease the use of low yield or inappropriate diagnostic testing. In a recent study, clinicians who, at the time of order entry, were presented with appropriateness criteria for imaging studies promulgated by the American College of Radiology (ACR) ordered fewer low utility MRI, CT, nuclear medicine, PET, and ultrasound studies. Clinicians were prompted to select a structured indication for the desired test. The selected indication was mapped by a commercially available CDS tool (ACR Select) to the ACR criteria which characterized the test selected as: low utility or usually not appropriate, marginal or intermediate utility, and well indicated or usually appropriate. This CDS support reduced the frequency of low utility tests from 11% to 5.4% and increased the frequency of high utility tests from 64.5% to 84%. Improvement was seen among attending physicians, resident physicians, and advanced practice providers (nurse practitioners and physician assistants) and was most pronounced in resident physicians ( 2 References The electronic health record (EHR) has catalyzed change for clinicians by making available vast amounts of patient data and other information that can be used for clinical decision support ... read more ).

In general, these measures aim to reduce variation in clinical practice by guiding clinicians to what is considered best practice as determined by expert panels, professional associations, insurance providers, or healthcare institutions. Because the EHR records when such triggers or prompts are provided to clinicians, adherence to protocols or guidelines can be measured. These examples illustrate the yet-to-be realized potential of the EHR in CDS. Maturing EHR systems and refined EHR-CDS will augment traditional medical decision making in the future.

On the other hand, potential detrimental effects of the EHR have also been noted:

NOTE: This is the Professional Version. CONSUMERS: Click here for the Consumer Version

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Electronic Health Records (EHR) and Clinical Decision Support - Special Subjects - Merck Manuals Professional Edition

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The electronic health record (EHR) has catalyzed change for clinicians by making available vast amounts of patient data and other information that can be used for clinical decision support (CDS). Legislation in the US has created financial incentives to adopt an EHR and, more importantly, to derive meaningful use from the EHR.

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This article is a great introduction to Clinical Decision Support, and what it entails. It is a very basic and brief introduction to CDS, but supplies a great place to start for those who do not know much about it. This article explains how CDS works, how computable data is analyzed and it prompts specific related alerts and order sets to autopopulate so that the clinician can select what they would like to order. It argues that CDS systems provide a way to decrease the information overload for clinicians, and increase the ability to integrate evidence based knowledge into clinical decision making. I thought it was a well written basic article on Clinical Decision Support.

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This is a website with a wealth of information about the health information exchange for the state in which I live, called the Georgia Health Information Network. It includes information about the history of the GaHIN, and how it took almost two decades to become a reality. It was started in 2013, and is connected to other exchanges in other states, federal organizations and national exchanges. It also lays out the roles of the partners of the GaHIN, which are the Georgia Department of Community Health, and the Georgia Health Information Technology Regional Extension Center.

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GaHIN's History

Nearly 20 years ago, healthcare stakeholders from across Georgia conceived of a groundbreaking idea. It was a concept so basic, yet so challenging, that it would take most of the next two decades to make it a reality. Simply put, the idea called for the secure electronic exchange of patient health data across healthcare settings, giving providers a view of their patient's health information at the point of care.

Instrumental in GaHIN's formation were the Georgia Department of Community Health (DCH) and the Georgia Health Information Technology Extension Center (GA-HITEC). These organizations, plus GaHIN's dedicated Board of Directors, other state and federal agencies, and a variety of strategic partners worked together to develop and implement a technology, business and policy infrastructure to sustain a statewide network-of-networks health information transport model.

Since its inception in 2013, GaHIN has worked closely with its founding partners, DCH and GA-HITEC, to attain a sustainable business model and secure financial status while serving the needs of Georgians statewide.

Today GaHIN meets the needs of a wide range of organizations including state departments and divisions, major health systems, regional health information exchanges and providers across the state. In addition, GaHIN is connected to other state HIEs, federal organizations and national exchanges.

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Nearly 20 years ago, healthcare stakeholders from across Georgia conceived of a groundbreaking idea. It was a concept so basic, yet so challenging, that it would take most of the next two decades to make it a reality.

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Health Info Exchange- why are we doing it, and what are we doing? 

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Health Info Exchange- why are we doing it, and what are we doing? 

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This is an editor's choice article from Perspective Journal covering the Health Information Exchange and explaining why it has been implemented. Some of the reasons given as to why HIEs have been implemented are to increase interoperability, improve response in individual and public health emergencies, decrease the likelihood of adverse events, reduce unnecessary cost, and improve patient's self management capabilities. In addition to this, and from a political standpoint, (President Bush had established the Office of National Coordinator of Health Information Technology in 2004), it justified the broad adoption and use of electronic health records.

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This is an article from the New England Journal of Medicine that describes and defines Telehealth and what it can be used for. Telehealth can be used to accomplish a multitude of healthcare tasks. From general appointments with your primary care provider, to specialty appointments, much can be accomplished using Telehealth. This article also touches on the reimbursement for Telehealth. There are guidelines for Telehealth that vary for each state, along with how telehealth can be implemented, and who can utilize it to provide care. Telehealth provides the gateway for the future of medicine and the opportunity for patients to be involved in their own care.

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This article appeared in NEJM Catalyst prior to the launch of the NEJM Catalyst Innovations in Care Delivery journal. Learn more.
Telehealth is defined as the delivery and facilitation of health and health-related services including medical care, provider and patient education, health information services, and self-care via telecommunications and digital communication technologies. Live video conferencing, mobile health apps, "store and forward" electronic transmission, and remote patient monitoring (RPM) are examples of technologies used in telehealth.
Telehealth and Telemedicine
The terms telehealth and telemedicine are often used interchangeably, but telehealth has evolved to encapsulate a broader array of digital healthcare activities and services. To understand the juxtaposition of telehealth and telemedicine, it is essential first to define telemedicine.
What is Telemedicine?
Oxford's telemedicine definition is " the remote diagnosis and treatment of patients by means of telecommunications technology." Telemedicine encompasses the use of technologies and telecommunication systems to administer healthcare to patients who are geographically separated from providers. For example, a radiologist may read and interpret the imaging results for a patient in a different county whose hospital does not currently have a radiologist on staff. Or a physician may conduct an urgent-care consultation via video for a non-life-threatening condition.

Where telemedicine refers specifically to the practice of medicine via remote means, telehealth is a blanket term that covers all components and activities of healthcare and the healthcare system that are conducted through telecommunications technology. Healthcare education, wearable devices that record and transmit vital signs, and provider-to-provider remote communication are examples of telehealth activities and applications that extend beyond remote clinical care.
Telehealth Technology
Several technologies are being deployed for telehealth including mHealth (or mobile health), video and audio technologies, digital photography, remote patient monitoring (RPM), and store and forward technologies.
mHealth-Using Smartphones and Tablets for Telehealth
Today, 95 percent of Americans own cell phones and 77 percent own smartphones. These and other mobile devices can be leveraged to promote better health outcomes and increased access to care. mHealth or mobile health refers to healthcare applications and programs patients use on their smartphones, tablets, or laptops. These applications allow patients to track health measurements, set medication and appointment reminders, and share information with clinicians. Users can access hundreds of mHealth applications including asthma and diabetes management tools as well as weight loss or smoking cessation apps. Additionally, mobile devices allow users to schedule appointments and communicate with providers via video conference and text message.

Wyoming Medicaid conducted a study measuring engagement and post-birth outcomes for patients who used a mobile health app called, "Due Date Plus." Use of the app, which allowed women to record pregnancy milestones, access medical services, and find symptom-related information was associated with increased compliance with prenatal care and decreased occurrence of babies born with low birth weights.
Video Conferencing, Video-Scopes, and High-Resolution Cameras in Telehealth
Clinicians are conquering distance and providing access to patients who are not able to travel by providing appointments utilizing real-time video communication platforms. Video conferencing technology has been utilized to provide care for inmates, military personnel, and patients located in rural locations for some time. Also, suppliers of both care and financing such as Kaiser Permanente, the Defense Department, and the Department of Veterans Affairs have been exploiting telehealth modalities to increase access to healthcare services and promote better care quality. In another example, S.C. Department of Corrections and the Medical University of South Carolina are using video scopes and high-resolution cameras to diagnose and treat inmates remotely. They are also conducting virtual appointments using video/audio communication applications to reduce prisoner transportation costs and increase safety by keeping inmates in and providers out of correctional facilities.
Remote Patient Monitoring (RPM)
Remote Patient Monitoring involves the reporting, collection, transmission, and evaluation of patient health data through electronic devices such as wearables, mobile devices, smartphone apps, and internet-enabled computers. RPM technologies remind patients to weigh themselves and transmit the measurements to their physicians. Wearables and other electronic monitoring devices are being used to collect and transfer vital sign data including blood pressures, cardiac stats, oxygen levels, and respiratory rates.

Devices are also being used to track blood glucose levels and report high or low levels to patients and providers. In partnership with Stanford, Apple is testing whether its Apple Watch can be used to detect irregular heart patterns, and AliveCor's KardiaBand allows Apple Watch wearers to perform electrocardiograms in 30 seconds that can easily be transmitted to physicians. Patients often go months without seeing their providers. RPM can allow for earlier detection of complications and identify patients who need to seek medical attention prior to in-person appointments. Moreover, chronic conditions can be more readily and efficiently managed resulting in higher quality care and outcomes as well as reduced costs.

Patients often go months without seeing their providers. RPM can allow for earlier detection of complications and identify patients who need to seek medical attention prior to in-person appointments.

According to this 2015 Cardiac Implantable Electronic Device (CIED) study, patients whose implantation included remote monitoring capabilities had a higher rate of survival than patients without it. " Furthermore, according to the Center for Technology and Aging, patients who participated in RPM were less likely to experience hospital stays, incurred fewer ED and urgent-care visits, and reported better management of their symptoms. They also indicated increased physical stamina as well as greater overall patient satisfaction and emotional well-being.
Store and Forward
Store and forward telehealth refers to the capture, storage, and transmittal of patient health information for asynchronous healthcare delivery using data storage and transmission technology. CAT Scans, MRIs, X-rays, photos, videos, and text-based patient data are gathered and sent to specialists and other members of a care team to evaluate patients and assist in their treatment. Technologies used for store and forward telehealth include secure servers and routers that temporarily house incoming packets of information and then route them to the appropriate end users. Secure email platforms are also used for store and forward telehealth.
Telehealth Services and Applications
Since the internet and mobile devices now pervade our lives, it is natural that people want to leverage telehealth technologies to improve care, offer convenience, promote access, and support sustainability. Telehealth services range from consultations and video conference mental health sessions to public health broadcast text messaging and on-demand provider education.

Telehealth Addresses Primary Care Physician Shortages/Specialist Scarcity: Telehealth is allowing patients at smaller, less-resourced hospitals to gain access to specialists based at larger regional facilities. Undeniably, lack of access and hard-to-reach populations are drivers of telehealth innovations as supported by this 2014 MUSC study on the use of telehospitalists to address physician shortages. Telehealth is being implemented to treat prison populations, as well as being deployed in rural communities and underserved urban areas to improve healthcare availability.

Telehealth for Education and Training: Numerous organizations provide healthcare education with the help of digital telehealth technologies including Harvard's Safety, Quality, Informatics and Leadership (SQIL) program which takes a blended learning approach. SQIL uses on-demand content combined with in-person training to create a new medical education model that uses "information technology (IT), data, and a culture of continuous improvement to enable healthcare organizations to evolve into true learning systems." Time-crunched physicians are increasingly using online and mobile platforms to meet their CME and MOC requirements, and to prepare for Board Exams.

Telehealth and Patient Engagement: With telehealth technologies, patients are taking more control of their well-being. Educational videos, health management apps for mobile devices, and online health learning and support communities empower patients to manage chronic conditions, lose weight, increase physical activity levels, and gain emotional support. Diabetes patients are benefiting from carbohydrate tracking apps and are using glucose monitoring devices to document and report their blood sugar measurements. Other patients are interacting with their providers and scheduling appointments through secure online communication portals. Additionally, they are accessing health education content via smartphones and computers to add to their self-care toolboxes. They are also using wearables and monitoring systems to gain knowledge about their sleep patterns, vital signs, and activity levels.

Telehealth and Provider Communication: A significant telehealth development is the increased communication via digital and telecommunications platforms among care providers. Care teams are enabled through telehealth technologies to more easily share information and collaborate in the treatment of their patients. PCPs are using telehealth platforms to consult with specialists and other providers to promote access for their patients in low provider availability areas.

Telehealth Reimbursement
Significant hurdles for more wide-spread telehealth adoption are the limits on reimbursement and the inconsistent payer landscape. In a KLAS-CHIME study from October of last year, over 50 percent of respondents from 104 health care organizations indicated that limits on reimbursement constrict their ability to expand telehealth services for patients. Medicare and Medicaid offer disparate degrees of flexibility while private payers also represent varying levels of funding.

Telehealth Reimbursement Medicare: Medicare, which finances care for patients who can most benefit from telehealth, will only pay if the originating site (service location of the patient) is either in a non-Metropolitan Statistical Area (MSA) or a Health Professional Shortage Area (HPSA). Medicare also limits the types of providers and facilities that can provide telehealth services. For more information, the Telehealth Resource Center (TRC) has furnished lists of covered providers, sites, and services.

Telehealth Reimbursement Medicaid: According to Chiron Health, Medicaid systems in 48 states will reimburse for telehealth provided via live video systems while 19 state Medicaid programs will pay for RPM. 12 state programs will finance store and forward telehealth and seven states allow payment for all three telehealth categories. But even though Medicaid is more accommodating of telehealth than Medicare, rules governing payment through state Medicaid programs vary considerably. For instance, some states require patients to be in a medical facility and not at home while receiving telehealth care, and others require a licensed provider to be co-located with patients while they are receiving telehealth services.

Telehealth Private Payers Reimbursement: There is no federal mandate requiring private payers to reimburse for telehealth services, but several states have enacted telehealth parity laws. Parity laws compel payers to cover the same types of services provided through telehealth as those that are provided face-to-face. They also require payers to reimburse telehealth services at the same payment rate as in-clinic services.

More widespread use and success of telehealth applications might spur the resolution of these reimbursement issues. CVS has been providing clinical services via telehealth since 2015. According to their study in the Journal of General Internal Medicine, 95 percent of patients "were highly satisfied with the quality of care they received, the ease with which telehealth technology was integrated into the visit, and the timeliness and convenience of their care." If CVS's merger with Aetna is finalized, increased competition may motivate other payers to find ways to offer telehealth services and, by extension, levels of reimbursement.

95 percent of patients were highly satisfied with the quality of care they received, the ease with which telehealth technology was integrated into the visit, and the timeliness and convenience of their care.
Telehealth and the Future of Healthcare
Despite the current reimbursement challenges, there are numerous benefits to increasing the use of telehealth to meet the nation's demand for health care. Convenience of care, increased access, improved worker productivity from not having to take time off and travel to appointments, decreased costs, and clinician time savings are a few. For these reasons, providers, payers, and employers alike are moving forward with more and more telehealth solutions.

With the recent news that Amazon's Jeff Bezos, Berkshire Hathaway's Warren Buffet, and J.P. Morgan Chase's Jamie Dimon have teamed up to disrupt healthcare, it's easy to speculate that telehealth technology will be a key strategy in efforts to bring down costs. Other employers are seeking to bring down prices as well with the help of telehealth. According to the Society for Human Resource Management (SHRM), not only are employers encouraging the use of telehealth services, their employees, many of whom are digital natives, are quite comfortable using these services. Because of remote healthcare's lower costs and increased worker productivity and satisfaction, organizations will likely seek telehealth solutions. Moreover, payers, like employers, may be lured by decreased medical expenditures and consumers may be motivated by the convenience and promptness of care that it offers.

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This article appeared in NEJM Catalyst prior to the launch of the NEJM Catalyst Innovations in Care Delivery journal. Learn more. Telehealth is defined as the delivery and facilitation of health and health-related services including medical care, provider and patient education, health information services, and self-care via telecommunications and digital communication technologies.

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This article is a super helpful and definitive article on the differences between Telehealth and Telemedicine. Telehealth is basically referred to as any use of electronic information to support and promote long distance healthcare. On the other hand, telemedicine is the actual delivery of some type of healthcare. It's the actual practice of medicine to promote healthcare.

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The terms "telehealth" and "telemedicine" are often used interchangeably. You can quickly find many articles and posts and even large healthcare organizations that say this is fine.

But the terms have different meanings, and after you finish reading this article, you'll know how - and why - to use the words correctly.

Telehealth and Telemedicine Overview

In 1906, Dr. Willem Einthoven transmitted an ECG report over a telephone line from a physiology lab to a hospital a mile away. That event marked the first baby steps of the use of telecommunications technology to deliver a healthcare service from a distance.

In 1948, clinicians sent radiology images over 24 miles of telephone lines from West Chester, Pennsylvania, to Philadelphia. Toddler steps.

In 1959, the University of Nebraska clinicians set up a two-way television connection to transmit neurological exams to medical students on campus. Five years later, they performed video consults and speech therapy services to patients at Norfolk State Hospital, 112 miles away. Our child is walking steadily now.

The prefix "tele" means "from a distance," so we'd be accurate labeling all these remote healthcare services as telehealth.

Or should we call them telemedicine, since they represent the practice of medicine over a distance?

And here we see the confusion. When is it telehealth, and when is it telemedicine?

It doesn't help that we have no standard, everybody-agrees definitions for the terms telehealth and telemedicine. Both terms refer to the use of electronic communications to help people become and stay healthy from a distance.

So what's the difference? Let's look closer at each word and figure this out.

What is Telehealth?

The Department of Health and Human Services' (HHS) Health Resources and Services Administration (HRSA) says telehealth is "the use of electronic information and telecommunications technologies to support and promote long-distance clinical health care, patient and professional health-related education, public health, and health administration."

Let's break it down.

"Electronic information" includes digital images, videos, or text files stored on a computer.

"Telecommunications Technologies" could be Smartphones, wearable devices, the Internet, and video conferencing, streaming media, and landlines and wireless communications.

"Long-distance" could be one mile or 1,000 miles. The idea is that the two parties involved in the interaction cannot see each other in person, but physical walls are no barrier.

The services provided are both clinical and non-clinical.

The services provided can be between a clinician and a patient and their family, between multiple clinicians, or even between two medical devices.

The HRSA also defines four ways telehealth is delivered:

1. Video Conferencing - A real-time, two-way interaction that supports health care services. Since all participants in a videoconference can interact with each other, this method is considered synchronous. One example is a dermatologist examining a suspicious mole over a live video feed. Another is a medical consult. A third example is 30 doctors at 30 locations attending a remote training session broadcast from an academic medical center.

2. Store and Forward - The transmission of digital images, pictures, video, or text that was recorded and stored before being sent. Since there is no real-time interaction between the participants, this method is considered asynchronous. An example is a patient taking a picture of a tooth injury and texting it to their dentist.

3. Remote Patient Monitoring (RPM) - The transmission of health or medical data such as blood pressure, glucose readings, and blood oxygen. RPM technologies require varying levels of intervention by the patient and can send data on a predetermined schedule or as needed. Some of the conventional measurement devices are:

Digital stethoscopes

Ophthalmoscopes (eye exams)

Otoscopes (ear exams)

Smartphone cameras

Vital sign monitoring devices

Wearable biosensors such as wristbands and heart rate monitors

Mobile Health - Also known as mHealth, it is the use of mobile devices such as tablets and Smartphones to transmit health information. An example is preoperative instructions texted to a patient to prepare for a surgical procedure.

By now, you see the term telehealth covers a lot of ground. Telehealth includes:

A neurology consult

Remote monitoring of vital signs

A remote physical therapy session

Ongoing education for medical professionals

A patient logging into a portal to review their lab results.

A scheduled text message reminding a patient to get a flu shot

Telehealth reaches across most disciplines, including psychiatry, dentistry, social work, home health, cardiology, and physical therapy. It's all about providing better clinical services to patients.

With so many applications handled by telehealth, you might be wondering, what's left for telemedicine? Let's find out.

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What Is Telemedicine?

Telemedicine is easier to understand because it's limited to the delivery of clinical care. Think of it as everything a patient would accomplish during an in-person visit with a clinician. It's practicing medicine using telecommunication technology to deliver care from a distance. The primary difference is that patient and clinician are not in the same room, but rather are connected by telecommunications technology.

With that simple definition in mind, here are examples of telemedicine:

Remote videoconference to support diagnosis, treatment, and prevention of diseases and injuries

Transmission of a photo of a long gash in the patient's leg to the patient's doctor

Post-op follow-up visit through text message, phone call, or videoconference

Remote monitoring of a patient's condition after therapy or surgery

Post-op transmission of heart sounds or blood pressure data

Remote videoconference consult with a medical specialist

Remote collection and transmission of vital signs

Remote management of a chronic condition

You may have noticed the overlap between these examples and the discussion of telehealth. You'd be correct. All telemedicine is also telehealth.

So what's the difference between telehealth and telemedicine? And if all telemedicine is also telehealth, why do we need the term telemedicine?

Well, I'm glad I asked.

Differences between Telehealth and Telemedicine

Some people think of telemedicine as a subset of telehealth, as if we can carve out a clean piece from the telehealth pie. That seems right, but it's not the best way to think of it because it's not a clean cut.

The best way to understand the difference between these terms is to think of telemedicine in terms of the four ways telehealth is delivered. Again, they are video conferencing, Store and Forward, RPM, and mHealth, and these four delivery methods represent ALL telehealth events.

Some - but not all - video conferencing is telemedicine. If the videoconference is between Elroy Jetson and his doctor, it's telemedicine. If it's between a world-class thoracic surgeon and medical students, it's non-clinical and therefore not telemedicine, but instead telehealth.

Some - but not all - Store and Forward events are telemedicine. If it's a patient's X-Ray related to an injury currently being diagnosed, it's telemedicine. If it's an infographic or academic publication used in a presentation to surgical residents, it's not telemedicine.

Most RPM is telemedicine because it's directly related to clinical patient care, such as the management of a chronic condition. But if a researcher is collecting daily step counts from a wearable device solely to support a research project, it's not telemedicine.

Similarly, most mHealth is telemedicine because it's an interaction between a clinician and a patient to support diagnosis, treatment, or disease prevention. However, if it's a community-wide alert about a disease outbreak or a text message to notify all Emergency Room staff of a 23-car pile-up on the local highway, then it's not distinctly clinical care for each recipient. It's not telemedicine.

And that's why it's not a clear-cut distinction from the telehealth pie. It's the portion of each of the four telehealth delivery methods that address direct clinical patient care.

In short, telemedicine refers to clinical patient care, while telehealth refers to a broader collection of educational, administrative, and other non-clinical healthcare activities.

Now, in each of the above examples, I said if it's not telemedicine, then it's telehealth.

At this point, your brain may be asking this question: "But if it's both telemedicine and telehealth, what do we call it? Telemedicine or telehealth?"

Let's answer your brain. We take the most specific term. We call it telemedicine.

It's like a snowstorm being a weather condition, but when it's happening, we don't say, "Hey Jimmy, there's a nasty weather condition happening out there." We use the most specific term available.

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Pop Quiz

Here's a quick test to see if you've got it.

1. Sally is at home with a sore throat. She calls her PCP's office using video conferencing for a diagnosis and (possibly) a prescription. Telehealth or telemedicine?

2. While John is in the hospital, his attending physician initiates a videoconference with a remote specialist to help diagnose John's fatigue. The specialist asks John a series of questions, then asks him to trot down the hallway, paying close attention to his gait and balance before announcing his findings. Telehealth or telemedicine?

3. Michelle receives an email from her doctor's office. It includes attachments of pictures of bull's eye rashes found on four separate patients in the past week, warning of a possible outbreak of Lyme disease in the community.

4. Rodney has Chronic Heart Failure. His care team sent him a tablet and a digital stethoscope so he can monitor his blood pressure, heart rate, and other vital signs. The system automatically transmits data to his care team.

5. Three days after surgery, Jamie receives a post-op follow-up secure text message from a nurse asking how the incision has healed and asking if the pain is subsiding.

Answers:

1. Telemedicine.

2. Telemedicine.

3. Telehealth. Sure, it's related to the prevention of disease, but it's not related to a specific patient. This scenario falls under the broader scope of remote patient education, and therefore, telehealth.

4. Telemedicine.

5. Telemedicine.

Why does it matter?

Ok, we've got it now. We know when it's telehealth and when it's telemedicine.

But why does it matter that we use the correct term?

In casual conversation, it doesn't matter that much.

But when the terms are used to craft health policy and determine reimbursement schedules, it matters.

For example, if your state law requires private insurance companies to reimburse telehealth service at the same rate as they pay for in-person visits, do they have a broad enough definition of telehealth?

Because if they restrict telehealth to patient-centered care, then there's no reimbursement for community-wide initiatives that use communication technologies to educate patients about the dangers of risky behavior. There's no support for innovative telehealth campaigns. And it's challenging to develop national consensus for telehealth policy.

Conclusion

All telemedicine is telehealth, but not all telehealth is telemedicine. Still, both support the broader goal to expand access to remote clinical services, simplify health management for patients, and improve patient health and the efficiency of our healthcare delivery system.

And as we watch the continuing rapid advancement of the supporting telecommunication solutions, there's no doubt we're witnessing an exciting reinvention of healthcare delivery that connects patients with their care teams, unobstructed by distance or mobility.

Whether it's telehealth or telemedicine, it's a win for all of us.

Tags: electronic communications, telemedicine refers, health care services, technology to deliver, refers to a broader, patient health, telecommunications technologies to support, real time, communication technologies, clinical services to patients, term telehealth, remote healthcare services, broader scope of remote, telehealth and telemedicine, telehealth includes, person visit, remote clinical services, clinical health care, public health, health education, health information

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Telehealth and Telemedicine Overview: What's the Difference?

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The terms "telehealth" and "telemedicine" are often used interchangeably. You can quickly find many articles and posts and even large healthcare organizations that say this is fine. But the terms have different meanings, and after you finish reading this article, you'll know how - and why - to use the words correctly.

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This article from the Health Science Journal covers the barriers to the adoption of Consumer Health Informatics (CHI). The primary barriers are related to privacy and security concerns and costs. Consumer Health informatics offers patients a way to be more involved in their own health, rather than only depending on a clinician to manage their health. Using CHI, patients can take a front seat to not only management of their health, but health promotion, which in turn decreases healthcare costs.

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Why is it so difficult to govern mobile apps in healthcare? 

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Why is it so difficult to govern mobile apps in healthcare? 

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This article touches on some very real and identifiable reasons that it is difficult to govern mobile apps in healthcare. Poor quality information and gaps in software functionality are among the top reasons. Because mobile apps are a specialized and popular form of technology, many people/companies can develop them quickly. This can cause many issues with safety and security because the people developing them are are not necessarily qualified technology engineers. Another issue is that these applications are being developed in many different diverse settings, which does not lend itself to easily being monitored or governed. A professional organization would be a giant step in the regulation of healthcare applications.

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Apps are highly versatile

The versatility of apps means they can be used to support a wide variety of health information or communication services at all levels of care delivery and in the community. As apps can support a single service or a combination of services, their function may not be clear at the outset to designers. A fundamental problem here is that there is no standard use cases for apps of any type and therefore the implementation of information and communication services is ad hoc. 7 While NICE's guidelines for digital health technologies distinguish broad categories of functionality, 8 their implementation is left to developers. For instance, asthma management: the use case for what specific functions apps must support, including information provision, peak flow and symptom diaries, and medication tracking, is fluid and currently evolving with developments in technology and medical research. While the functionality of apps, or any other software to support health services, should be based on clinical standards that provide evidence-based requirements for information and communication, the slow rate at which such standards are produced presents a challenge for clinical governance because without standardised use cases, apps are providing functionality where there is no clear understanding of risk.

The lack of standard use cases makes it difficult to lay down clear rules for clinical governance of apps. With current regulations for medical devices, there is only a subtle difference between apps which are considered medical devices and those that are not. For example, symptom checkers are considered to be a medical device if they provide a subset of medical conditions matching the symptoms entered by users, indicate the likelihood of a match, or provide treatment recommendations. 9 Yet, those that list all conditions matching the symptoms entered by users and provide signposts to suitable care are not considered medical devices. 9

Apps also pose challenges for applying clinical governance processes because they are used at all levels of care delivery and in the community. For instance, consumer health information is the purview of public health where the reliability and credibility of information is a prime consideration. In contrast, prevention and management of disease involve formal care delivery settings often with treatment by health professionals. This raises complex issues in processes to assess and manage risk because across these contexts, there is no consensus about the level of acceptable safety among health service providers, clinicians and regulators.

Anyone can build an app

The sheer number of health apps and their ubiquity is a consequence of how easy it is to develop and publish them. It is estimated that there are over 325 000 health apps available in the leading app stores. Apps can be created using online platforms that automate development, distribution and maintenance; such platforms (eg, Appy Pie) do not require any software programming skills. While there are no data about the use of such platforms for health apps, in a recent study of 2.3 million apps on Google Play, 11% were created using such online platforms. 10 Both clinicians and developers with little or no formal software engineering background are, therefore, rapidly developing apps. Such an ad hoc approach to development means that developers are unaware of underlying safety and security issues that can ultimately pose serious risks to data security and privacy. The lack of accountability is a serious issue because qualified engineers are professionals who can be held accountable for their actions while this may not be the case for clinicians and citizen developers ('citizen developer' is a new term implying a mix of skill and informality, even naivety compared with professional practice). 8

Another rapid method for development is via mobile application frameworks which provide many pre-made components that can be used as building blocks for most health information and communication functions (eg, Ionic and React Native). This hybrid approach to build apps does not require developers to have extensive coding expertise or software development skills compared with the process of building native apps. The availability of hybrid approaches has no doubt contributed to the growing numbers of health apps, but the quality is not guaranteed because these tools have opened up development to citizen developers, clinicians and professional developers with varying levels of software engineering expertise. Worse, these building blocks were not originally developed for clinical use.

Development occurs in diverse settings

Because the entry barrier to app creation is low, they are being developed in formal and informal settings. The sociotechnical disparity of these contexts poses significant challenges when it comes to formalising processes for software development and clinical governance. 4 For large-scale health IT systems, software development is typically undertaken by an IT business entity. For an electronic medical records system that is developed by a large multinational company, the organisation provides a formal setting for software engineering processes to be undertaken. The company will have clinical and technical expertise, including a large and well-organised IT group as well as organisational structures, policies and procedures to govern the quality, safety and security of the software it produces. 11 An app developed in such business settings should be subject to the same rigorous engineering and clinical governance processes not just for regulatory reasons but also for reputation reasons. In contrast, smaller health IT businesses and new businesses, such as start-up companies, may not provide the infrastructure conducive to formal software engineering and clinical governance processes-and they typically have smaller budgets available for the development compared to a large business entity.

Another setting for app development is within healthcare organisations, including providers, government agencies and not-for-profit organisations. While these settings have deep clinical expertise, those without a large and well-organised IT group are unlikely to have technical expertise and established software engineering processes for building and maintaining apps in-house; they may rely on third-party developers which reduces accountability. Another issue with healthcare organisations is that their clinical governance structures and processes are generally not set up to provide oversight for apps. With citizen developers, the informal context means that formal software development and clinical governance processes may altogether be absent. Because of a lack of skills and experience, citizen developers may be unaware of best-practice development methods and standards. Development may proceed without engaging users, apps may not be maintained and updated in a timely manner, quality control may be missing and postmarket surveillance is often omitted-and so on. These problems also occur in apps developed bottom-up by clinicians on their own. Many apps are unplanned and grow 'organically': some are by-products of local clinical system improvement projects (eg, handover or antibiotic prescribing apps). While a bottom-up approach is advantageous for innovation and clinical engagement, apps developed in this manner may not be sustainable beyond pilot testing. Clinicians worried about excessive bureaucracy may not engage with formal organisational processes for software development and clinical governance. Ironically, some apps start life through clinicians working around perceived problems with organisational policies and processes. Strategies to formalise app development, therefore, need to account for the diverse contexts where they are being developed. It is noteworthy that citizen development and marketing of pharmaceuticals is illegal.

No gate at implementation, little formal evaluation and no monitoring of use

As there are no formal processes to specifically govern the deployment of apps for health, they can be deployed easily and widely. When publishing via app stores, developers are provided with general guidelines for safety, privacy and performance. There are no requirements to comply with standards for software development and quality processes which are an important clinical governance strategy to ensure sound end products. A more fundamental problem is that there are no agreed standards for developing health apps, 12 and efforts are underway to collate all the current standards, frameworks, best practices and guidelines. 13 Where guidelines and standards exist, such as NICE's guidelines for digital health technologies, they are not specifically intended for apps that are downloaded or purchased directly by users. 8

Despite apps having a global market, thanks to the Internet and app stores, there is no global regulator; regulatory requirements for software as a medical device are not uniform worldwide. 14 Take, for instance, a symptom checker that would be a class 1 medical device in the UK, but is not considered a medical device in Australia, and in the USA would not have regulations enforced by the Food and Drug Administration. Because developers are not required to demonstrate performance and show effectiveness, the quality of evaluation studies is poor, 8 and there is little attention to safety. 15 Current European medical device regulation (CE marking) presumes apps are clinically effective, and therefore pushes liabilities for use errors, even if caused by software bugs, onto end users. Again, this contrasts with pharmaceutical development, which includes registering experiments and a mature peer-reviewed publication culture.

Once apps are downloaded, their postmarket use goes largely unchecked, and even those apps that report use data have no awareness of the wider clinical condition or outcome. While agencies and institutions may monitor their apps as part of their internal clinical governance processes, there is no ongoing surveillance and oversight of apps that are published by citizen developers (nor are citizen developers resourced to provide postmarket support to users-note that successful apps can have millions of users across all time zones). App stores might remove apps in response to user feedback; however, studies show that ratings are not correlated with quality or safety, and therefore, user feedback is not relevant. 16 17

Apps directly reach users

Apps that are accessed by consumers and patients outside formal care delivery increase risks because they can be used to make decisions without having to consult health professionals. Compared to a clinician who is a learned intermediary, when using a software system apps are a direct-to-consumer channel for health information and services that increases clinical risk and is vulnerable to data exploitation (eg, sharing patient data). 18 Consumers and patients who use apps are a heterogenous user base with varying levels of health literacy and IT skills. Users lack the ability to search for and appraise the quality and trustworthiness of apps and their content. 19 They may not have the skills to use an app in the manner that was intended by the designers. Their understanding, skills and physical state (especially given they are likely to be ill) may also affect their ability to detect, manage and report errors. Software updates are another responsibility for users but developers have no control over whether or not users instal updates: therefore, bug fixes and security updates might not be installed. There could be many different versions of an app in use, making it difficult to manage risks. While strategies to assist users in selecting and using apps, such as grading labels, may go some way towards reducing risks, 20 the types of apps that are made available directly to consumers needs to be controlled.

Discussion

Health apps are perhaps the most extreme example of challenges for clinical governance in the digital health ecosystem. While current efforts are usefully directed at curating the growing numbers of apps, 20 they are not sustainable. A parallel strategy could focus on the preceding stages of the IT system life cycle to stem the growing tide of apps, by controlling their design, build and distribution.

As with any other digital technology, we need to consider apps from a problem-driven perspective by the health information or communication service they support. For example, consumer health information services should be based on standards for clinical content irrespective of whether they are delivered on web sites or via apps. Similarly, information and communication for patient self-monitoring in asthma should be clinically driven, based on standards where they exist or best practice guidelines. Requirements relating to the channel of service delivery can be separately addressed via technical standards that are channel-specific. For instance, privacy and data security considerations need to be channel-specific because apps have more direct access to personal information compared to websites. Other channel-specific aspects that need to be covered via technical standards are usability, availability, interoperability and maintenance, including updates. Such an approach is being considered for digital mental health services in Australia and could be applied to other health domains. 21 A major advantage of this approach is that it does not consider apps in isolation but in context of the health services they support or the community. Ultimately, this might become a necessary way forward as we move to the omnichannel age where there will be seamless delivery of health information and communication across different channels. 22 For instance, symptom checkers can be downloaded as apps or accessed on the web; a symptom checker service may be operated alongside a telephone helpline and a health information service.

Clinical and technical standards will provide a common framework for managing risks throughout the IT system life cycle and for operational oversight whether it is via developers self-certifying themselves, independent certification or regulation as medical devices. One possible avenue for common standards is via initiatives like the WHO and ITU processes for benchmarking artificial intelligence in health. 23 Common evaluation standards underpin an evidence-based approach to digital health, 24 and are necessary as each level of oversight can provide a foundation for the next. 25 With symptom checkers, self-certification by developers can form the basis of safety cases presented to regulators. The implementation and use of such apps in formal care delivery settings needs to be driven by appropriate guidelines and standards, 11 26 and operational oversight should be provided by organisational processes for clinical governance. 11 For example, a symptom checker operated by a public health information service should be subject to internal clinical governance processes of the government agency operating the service.

The level of oversight needs to be proportional to the degree of risks that apps pose to users. An evidence-based approach that is informed by the current landscape of health apps is required. At present, there is no publicly available information about the number and types of health apps, who developed them, how they were developed and for how long have they been available, let alone the risks they incur. If the majority of low-risk health and wellness apps are being developed by credible health organisations and government agencies, then self-certification could be a viable option. These mechanisms for oversight will need to be examined alongside regulations for software as a medical device. Operational oversight and surveillance can also be considered at a national and regional level using common frameworks so that it is possible to compare patterns over time and between settings, and to develop and prioritise preventive and corrective strategies. 27

Our key conclusion is that the area of health apps is immature; this is unsurprising as apps are a radical and new development, which relies on sophisticated technology that has a record of frequent innovation. Patients, clinicians, developers and regulators are inevitably beholden to this current immaturity. Unlike established healthcare fields (eg, pharmaceuticals, radiotherapy, anaesthetics, etc) there is-as yet-no professional foundation, such as requiring certified and registered developers. Once there is a professional foundation, we envisage professional developers moving to regulation, just as registered pharmacists move into pharma regulation-this is a slow process, but without it, regulation is likely to remain behind the curve. The role of citizen developers in this ecosystem needs to be carefully considered. Given IT's record of continual radical innovation, regulatory lag is likely to be permanent, and therefore, an effective clinical governance response must be, or partly be, of a different sort than conventional regulation. This paper has set out some of the issues that must be addressed. Ultimately, apps should not be considered just by their form but by their function and as part of the digital health ecosystem.

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Why is it so difficult to govern mobile apps in healthcare?

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Mobile apps have become a convenient way to provide health information and communication services directly in the hands of clinicians and consumers. Apps can be used to support consumers in a variety of health tasks to manage chronic diseases, support lifestyle changes and in self-diagnosis. For

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Anticipating and addressing the unintended consequences of health information technology and policy

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Anticipating and addressing the unintended consequences of health information technology and policy

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This article is from the Journal of Informatics in Health and Medicine and discusses what the unintended consequences are of implementing health information technology. HIT is meant to improve patient care, reduce errors, make healthcare more efficient and available, and reduce costs. While the implementation of HIT is intended to improve healthcare, there are many unintended consequences to making healthcare more technology based and less human.

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Abstract

Federal legislation (Health Information Technology for Economic and Clinical Health (HITECH) Act) has provided funds to support an unprecedented increase in health information technology (HIT) adoption for healthcare provider organizations and professionals throughout the U.S. While recognizing the promise that widespread HIT adoption and meaningful use can bring to efforts to improve the quality, safety, and efficiency of healthcare, the American Medical Informatics Association devoted its 2009 Annual Health Policy Meeting to consideration of unanticipated consequences that could result with the increased implementation of HIT. Conference participants focused on possible unintended and unanticipated, as well as undesirable, consequences of HIT implementation. They employed an input-output model to guide discussion on occurrence of these consequences in four domains: technical, human/cognitive, organizational, and fiscal/policy and regulation. The authors outline the conference's recommendations: (1) an enhanced research agenda to guide study into the causes, manifestations, and mitigation of unintended consequences resulting from HIT implementations; (2) creation of a framework to promote sharing of HIT implementation experiences and the development of best practices that minimize unintended consequences; and (3) recognition of the key role of the Federal Government in providing leadership and oversight in analyzing the effects of HIT-related implementations and policies.

Introduction

As part of efforts to improve healthcare delivery and reduce healthcare costs, the U.S. has invested considerable resources in health information technology (HIT) and electronic health records (EHRs). The unprecedented availability of funding provided by the American Recovery and Reinvestment Act (ARRA)/Health Information Technology for Economic and Clinical Health (HITECH) legislation 1 is a key factor in spurring a flurry of HIT implementations. The federal Office of the National Coordinator (ONC) for HIT highlights the enormous potential of HIT to yield improvements in patient care, prevention of medical errors, an increase in the efficiency of care provision and a reduction in unnecessary costs, expansion of access to affordable care, and improvements in population health. 2 While expectations are high, experience shows that unplanned and unexpected consequences have resulted from major policy and technological changes. Studies have demonstrated that HIT is not immune from this phenomenon. 3-9 Thus, it is important to consider the potential unintended consequences that may be engendered by the accelerated adoption of technology.

The American Medical Informatics Association (AMIA) dedicated its 2009 Annual Health Policy Meeting to an examination of the unintended consequences of HIT and related policies. The conference convened stakeholders representing a variety of backgrounds, disciplines, and work environments.

Combining a summary of conference discussions with findings from research, this article presents an overview of the factors that can lead to negative unintended consequences as a result of HIT implementations and related policies, and outlines approaches to identifying and avoiding (or mitigating) unwanted outcomes. The article concludes with recommendations aimed at maximizing HIT's benefits while minimizing negative consequences.

Previous study of unintended consequences

The idea that endeavors can have outcomes other than those originally planned is not novel. Robert K Merton is credited with popularizing the concept of unanticipated consequences. In his 1936 paper, Merton listed possible causes of unanticipated consequences: ignorance, error, overriding of long-term interest by immediate interest, basic values that require or prohibit action, and self-defeating prophecy. 10

Discussions about unintended consequences resulting from public policy and technological innovation are found in the healthcare domain as well as in other fields. In 2000, the Robert Wood Johnson (RWJ) Health Policy Fellowships Program of the Institute of Medicine conducted a workshop on the subject of unintended consequences of health programs and policies. 11 Examples of unplanned outcomes of health-related policies include the linking of a cap on Medicare drug benefits with lower drug consumption and unfavorable clinical outcomes, 12 the impact of federal regulations and hospital policies on indebtedness of patients, 13 and unintended consequences of tobacco policies affecting women and girls of low socioeconomic status. 14

Researchers have identified and categorized unintended consequences that can accompany the use of patient care information systems, 3 with specific attention to computerized provider order entry (CPOE) systems, 4-6, 19, 20 clinical decision support systems, 7, 8 and bar code medication administration technology. 21, 22 (It may be noted that studies of unintended consequences related to CPOE are disproportionately represented in the above list of references. While ample anecdotal examples exist for unintended consequences in other HIT-related areas, to date there have been few formal studies of these. Indeed, CPOE has been particularly fertile ground for study due to the fact that errors in medications and procedures are among the easiest to connect directly to patient harm.)

Studies have highlighted conflicts between the cognitive demands of HIT systems and limits of the human information processing system, 23-25 drawn attention to EHR usage patterns and usability issues, 26, 27 and summarized requirements necessary for safe use of EHRs. 28 They have discussed the challenges associated with the introduction of HIT applications into organizational structures. 9, 29-32 Currently, researchers are beginning to focus on the challenges that accompany current government incentives that promote widespread implementation of HIT. 33

Definitional issues

Clarification of terminology is provided by Ash et al: "The terms 'unintended consequences' and 'unanticipated consequences' are not synonymous. The 'unintended' implies lack of purposeful action or causation, while the 'unanticipated' means an inability to forecast what eventually occurred. Either kind of consequence can be adverse or beneficial. Unanticipated beneficial consequences are actually happy surprises. Unanticipated, unintended adverse consequences capture news headlines and are often what people imagine when they hear the term 'unintended consequences.'" 34

Desirable or undesirable: is the outcome positive, negative, or mixed? Or good in some ways and bad in other ways? For example, structured EHR data entry allows for organization of data into discrete categories so that these data can be efficiently processed. However, this can result in loss of elements of the clinical narrative that serve as a record of potentially important points in the patient's history. 25

Anticipated or unanticipated: can the consequences be predicted or anticipated, and if so, by whom? This is a scale ranging from events that are easily anticipated, through those that are anticipatable by an average practitioner with effort, to those that are only anticipatable by experts, and culminating in events that are completely unanticipatable by anyone or a total surprise. This 'anticipatablity scale' applies not only to the presence or absence of an event, but also to the magnitude of an event. An example of an anticipated unintended consequence is the weight gain/loss associated with many medications. Even so, a given individual may have a weight change so large that the magnitude was unanticipated, even though some weight change was expected. In another example, investigators found, unexpectedly, that introduction of EHRs had a profound and enduring impact on the organization of information by doctors. 25

Direct versus indirect: does the input cause the consequence directly or is there a chain of events leading to it? It is worth noting that unanticipated unintended consequences are frequently the result of an indirect causal chain. For example, in the study referenced above, EHR use led to use of discrete categories of information which led to information reorganization, which resulted in a change in the direction of reasoning based on the reorganization, and use of decision support. 25

Latent versus obvious: is the consequence easily visible or does it become obvious only in another context or environment or at a later time?

Figure 1 depicts the potential relationships across and among types of consequences. This depiction draws upon earlier work, particularly that of Ash et al. 5, 20, 34 If a consequence is undesirable but anticipated, it can be addressed and managed as a tradeoff. Before accepting a tradeoff, its effect is estimated as closely as possible, but it is possible to underestimate the impact of the tradeoff. For example, hospitals implement drug interaction alerts with the intention of interrupting a physician's workflow, having made an assessment that this tradeoff might annoy the physician but stop an error. However, an overabundance of alerts can generate an unintended and also unanticipated consequence-ignoring all alerts or refusing to use the system.

The most troubling unintended consequences are those that are undesirable as well as unanticipated, with no mitigation plan in place.

The AMIA 2009 health policy conference

As noted above, federal legislation is stimulating numerous HIT implementation efforts around the country. While the potential benefit of these efforts is broad, experience has shown that major technological changes can bring about negative unintended consequences that can jeopardize the success of implementations. Consequently, AMIA dedicated its 2009 Annual Health Policy Meeting to an exploration of unintended consequences of HIT and related policies.

The goals of AMIA's 2009 health policy conference were as follows:

Explore and outline approaches to recognizing, anticipating, and addressing unintended consequences of HIT and HIT-related policies and legislation.

Identify areas for further study and research in the above areas.

The recommendations of the meeting (described at the end of the paper) were formulated during facilitated breakout discussion sessions attended by conference participants; these sessions are described in detail below.

Two conference speakers, representing other safety-conscious industries, shared their experience relating to HIT and unintended consequences, providing critical knowledge that informed the breakout discussions. Dr Nancy Leveson, an expert in aviation software safety, presented highlights of the experience of the aviation industry in addressing safety issues. Dr Leveson focused on factors contributing to previous IT disasters, noting that a high percentage of famous aviation disasters involved software that had been adapted from another product or use, rather than developed de novo. She discussed mistakes commonly made in introducing technology: attempting to do too much too fast, building technology-centered automation that is unusable or prone to error and necessitates unacceptable changes in workflow, and failing to build in safety at the start of design. Rodeina Davis, CIO of the Blood Center of Wisconsin, discussed the history and experience of blood banking software under FDA regulation. Keynote speakers, Dr David Blumenthal, ONC Director, and Aneesh Chopra, Federal Chief Technology Officer of the U.S., addressed national issues in the HIT arena.

First, AMIA initiated the meeting's discussions with the following questions:

What do we know about unintended consequences that are related to HIT design, implementation, and use? To what extent can we anticipate, describe, categorize, and prevent (or mitigate) unintended consequences and what approaches have been effective? To what extent are unintended consequences undesirable? Desirable (conferring benefits or efficiencies)? What do we still need to learn?

Who is responsible for anticipating unintended consequences?

What unintended consequences may arise from policies engendered by current/pending legislation and regulation related to HIT?

What lessons can we learn from other industries and how can we leverage them in addressing unintended consequences related to HIT?

The meeting's Steering Committee developed a working definition of unintended consequences: "Unintended consequences are outcomes of actions that are not originally intended in a particular situation (eg, HIT implementation)." In particular, the focus of the meeting was on those undesirable outcomes that are rarely, if ever, foreseen. For the sake of brevity, they are referred to subsequently in this paper simply as unintended consequences.

Breakout sessions focused on four intersecting domains

The breakout sessions were planned by the Steering Committee to stimulate in-depth consideration of HIT-related topics through an open exchange of knowledge and experience by meeting participants. Participants chose among breakout sessions focused on four domains: technology, human factors and cognition, organization, and fiscal/policy and regulation.

While unintended consequences from health IT are frequently considered in terms of a single domain (eg, technology), when addressing unintended consequences of HIT implementations and policy, there are intersections across the domains. For example, a recent qualitative meta-analysis of HIT implementations found that organizational efficiency is not automatically increased just by implementing a technology solution; actions that were needed to promote success include management involvement, integration of the system in clinical workflow, establishment of compatibility between software and hardware, and user involvement, education and training. 36 These actions straddle the domains.

Decisions made about the technical design of an HIT system (eg, organization and display of information) can affect the ease or difficulty of its use due to limitations of human cognitive abilities. Failure to consider the complexities and dynamism of clinical workflow when designing and implementing HIT solutions can impact their effectiveness and may lead to unintentional errors that can impact patient safety. Unintended consequences can arise in the organization domain as a result of regulation/legislation that mandates meaningful use of HIT by a certain deadline, whereby some practitioners are not yet included in the definition of meaningful users, and thus are not eligible for payment incentives.

Therefore, AMIA introduced an input-output model to characterize unintended consequences, with several factors serving as inputs:

Technology: hardware and software systems that are implemented and the constraints they impose.

Human factors and cognition: the thought processes, habits of behavior, and mental capabilities that humans bring to the use of HIT tools and processes.

Organization: the embedding of technology in the complex environment of healthcare organizations.

Fiscal/policy and regulation: the legislative and regulatory environment governing the design, implementation, and use of HIT.

There are multi-dimensional implications of HIT-related unintended consequences arising in these domains. Figure 2 outlines relationships among the types of consequences and how they affect stakeholders. (The model shows Fiscal, and Policy and Regulation separately, but breakout discussions about these topics were combined because of their overlap.)

Technology domain

The conversion from a paper-based system to an electronic system results in inevitable challenges. Some problems are caused by attributes or characteristics of specific HIT systems, while others are the result of the general change process or may be related to the existing level of computerization in an organization. New types of errors may be generated when performing a task using a computer rather than paper, and communication via electronic means differs from face-to-face communication among clinicians and between clinicians and patients.

The transition from paper to an electronic system can result in errors due to problems with technical design, confusion about system features by users, and workflow mismatches. 20 Examples include:

Users of CPOE will often select an item from a pick list that is close to their desired choice but not technically correct or less precise than intended. The list of choices may be limited by tool design (eg, no facility for an 'other' type choice) or the terminology used to specify list choices.

The quality of clinical documentation may be affected by a feature of EHRs that allows multiple parties to access and edit the same records. A positive consequence is that multiple providers can double check and refine summaries of clinical problems or medications; providers may correct entries, reconcile with patient lists, or add their own insights. However, different providers may disagree on the quantity and specificity of documentation to include on shared lists. When conflicts such as these are not managed, shared lists may develop duplicate, or even contradictory, entries that lead to confusion as different providers attempt to document in a manner that matches their needs and cognitive workflow.

Experts recommend that organizations use rigorous approaches to ensure the quality of data in HIT systems. These include manual checking of results, development of measurable data quality benchmarks and procedures for identifying deviations from benchmarks, and training of users to correctly enter data in electronic forms. 37

Human factors and cognition domain

Knowledge of the principles of human-computer interaction and an appreciation of the importance of human and cognitive factors are critical to HIT design and implementation. The study of human factors related to HIT systems focuses on the systematic application of knowledge about human sensory, perceptual, mental, psychomotor, and other characteristics to the design of these systems. Human and cognitive factors place emphasis on the mental (memory, knowledge, strategies) and social properties characteristic of humans. 23 Horsky et al noted, "Cognition is considered to be a process of coordinating, mediating, and redistributing knowledge representations that are internal (ie, in the mind) and external (eg, visual displays, written instructions, etc). Environmental, social, cultural, organizational and regulatory factors contribute to the complexity of these systems that stretch over human beings and the technology they work with. Computing technology and artifacts are integral parts of this cognitive process and should be designed to correspond to human characteristics of reasoning, memory, attention, and constraints (human-centered design)." 41

Electronic health records have enabled the collection of large quantities of data and text regarding patient encounters, hospitalizations, procedures, and test results. Increasingly voluminous repositories of electronic records that have been converted from paper can lead to unintended consequences such as difficulty in finding data that are relevant to a specific clinical need. Coupled with certain aspects of system design, these vast quantities of data not adequately organized can result in increased cognitive load, a term originating in cognitive science that refers to the load on working memory during information processing.

Other concerns have arisen from the inherent properties of HIT systems. Patel et al report that exposure to EHRs' tightly structured format has been shown to be associated with changes in physicians' information gathering (more efficient) and reasoning strategies (hypothesis driven) compared to their use of paper-based records (slow and data driven). The support provided by the system to guide and narrow down the search that changed the directionality of reasoning from data-driven to hypothesis-based, resulted in errors which were anticipated. With other intended changes, there were also unintended changes: loss of the narrative thread of the patient history and the additional cognitive effort needed to complete a patient history based on discrete information, leading to different types of errors. 25

Alert and reminder systems are frequently characterized by linear, rigid rules, an approach that is a poor fit for the inherent complexity of medical decision making and is inconsistent with the way in which people tend to make decisions as shown by the classical decision making literature on heuristics and biases. 43 This formalization of rules to manage decisions that were previously managed informally entails loss of flexibility, leading to loss of resilience, with the danger of generating medical error as an unintended consequence. Such examples indicate that a deeper understanding of the cognitive properties of a system prior to its implementation is needed to help planners anticipate and pre-empt many unintended consequences.

Organization domain

Peter Drucker described the modern hospital as "altogether the most complex human organization ever devised." 44 As increasingly complex HIT systems are implemented in complex environments, they will affect larger, more heterogeneous groups of people and organizations in a variety of settings. Major implementation challenges for an organization tend to be behavioral, sociological, cultural, and financial rather than strictly technical. An HIT implementation can lead to physical, mental, and emotional exhaustion of an organization and its workforce, thus rendering the organization reluctant (or unable) to move forward with further implementation efforts. The critical importance of managing the power and organizational conflicts inherent in information system development is being increasingly understood. 17 To create an effective foundation for organizational transformation, there is a need for strong support by both management and future users during HIT implementations. 32

Discussing factors contributing to HIT implementation challenges, Lorenzi et al focus on the organization's capacity for change and its recognition of the importance of context: "The implementation of an IT system... requires a detailed plan that is driven by both capacity for change and context of change. Capacity represents the ability of the organization to invest in high-quality training, extensive support at go-live, and managers who can respond flexibly to changes in the environment so that patient safety is maintained as the highest priority. Context is the environment to which the implementation plan must adapt. A rollout schedule, for example, must take into account the many interdependencies that exist among clinical units as well as organizational changes that are occurring during the implementation." Examples of aspects of implementation that are embedded in organizational structures, supports, and processes and may become sources of frustration include workflow changes, difficulty getting technical help at the time when it is needed, perceived (or actual) disassociation of IT staff from operational needs, and conflicting organizational priorities. 31

A systematic literature review outlined lessons learned from HIT implementations in seven countries and found that strong project leadership using appropriate project management techniques, the establishment of standards, and staff training are needed to avoid risks that could compromise success. The review described ways in which HIT technical features interact with the social features of the healthcare work environment, and how this juxtaposition may contribute to complications of HIT deployment. 45 Harrison et al described unintended consequences resulting from the interaction between HIT and the healthcare organization's socio-technical system (workflows, culture, social interactions, and technologies), and offered the ISTA (interactive socio-technical analysis) model to help study these consequences and their causes. 9

The Institute of Medicine report, To Err is Human, noted that for optimum use, technology must be a 'member' of the work team. 46 Considering an organization's workflow and procedures and the roles of its clinical teams during system planning, design, and implementation is critical. Workflow problems after CPOE implementation were rated high on a list of concerns of representatives from 176 U.S. hospitals responding to a recent survey. 34 Indeed, HIT implementations are opportunities to review existing workflow processes to make sure that all are effective and up to date, and identify those that are unique to the institution; information gained from these reviews can guide modifications that need to be made to off-the-shelf HIT products. Implementation of HIT may blur the distinctions among traditional role lines, such as those of clinicians and information technology providers and administrators. When developing HIT solutions, it is necessary to balance system standardization with flexibility: standardization allows for a consistent approach to clinical care, information exchange, and related processes, while flexibility allows for customization to patient individuality, distinct clinical workflows, and HIT users' preferences.

Ongoing evaluating and monitoring of HIT systems to measure implementation success and pinpoint unintended consequences that occur during system usage is important. Sittig et al recommended measures to assess system availability, use, benefits, and potential hazards. 19 Campbell et al addressed the pervasive problem of system downtime which can throw an organization's HIT-dependent operations into chaos. They advised healthcare organizations to prepare and test contingency plans so that operations can continue during system downtimes; these plans should include requirements for paper backup systems, procedures for operating without electronic resources, training for employees, and periodic drills to assure that these procedures function as planned and that all staff are thoroughly familiar with them. 37

Fiscal/policy and regulatory domain

The widespread, accelerated diffusion of technology resulting from recent legislation may engender unintended consequences manifested in various ways throughout those organizations under pressure to accommodate the changes. Legislative and regulatory changes are moving health IT from a voluntary initiative to a highly regulated activity. Examples include revised privacy and security obligations for practices covered by the Health Insurance Portability and Accountability Act of 1996 (HIPAA), modifications to definitions of covered entities, and the inclusion of new ramifications for HIPAA violations. 47

The group did not reach consensus on whether formal regulation of EHR software would, on the whole, be beneficial or harmful. Plenary speaker, Rodeina Davis, CIO of the Blood Center of Wisconsin, discussed the experiences of the blood bank community with regulated software. She reported that the consensus of that community was that although regulation was necessary when it originally occurred, the advancements in software development methodologies have made formal regulation less advantageous.

Although meeting participants agreed that current EHR software, like all software, contains errors, there was debate about the impact of the rigidity and loss of nimbleness that formal software regulation entails. One anecdotal example given was that regulated software, such as that in some radiology systems or blood banking, is tied to a specific operating system version. As a result, vendors are prohibited from rapidly patching systems when the underlying operating system (eg, Windows) is compromised by a new virus or exploit. Given the evolving meaningful use requirements, and the mandate for interconnection, this inability to respond to novel threats concerned some participants. A loss of nimbleness can also impact the pace of innovation. While breakthrough innovation is possible in a highly regulated environment, conventional wisdom is that the regulation slows innovation. All of the participants agreed that the current generation of EHR software was less than optimal, and that significant innovation would be necessary before EHRs could achieve the promise of radical transformation of the healthcare process. In the time available, participants could not reach closure on an optimal tradeoff between regulation and nimbleness.

Recommendations

Oversimplifying the challenges inherent in HIT design, development, and implementation can lead to potentially avoidable unintended consequences. There is an increasing body of knowledge indicating that errors can result from HIT systems' failure to support the inherent complexity and dynamism of clinical workflow. It is also critical to understand the cognitive factors at the individual and social levels that come into play when human beings interact with technology and ensure that these factors are taken into account early in the design and implementation of systems.

We present several recommendations aimed at helping stakeholders anticipate and avoid (or mitigate) the unintended consequences of HIT implementations and related policies. Three overarching themes of these recommendations are: the need for additional research into the causes, manifestations, and mitigation of unintended consequences of HIT implementation; the importance of coordination of efforts and sharing of results by the various stakeholders working in the field of HIT design and implementation; and the major role that the Federal Government plays in providing oversight and leadership. While several of the recommendations for additional research are multi-year projects, suggestions in the subsection focusing on Federal Government oversight and leadership specifically address studying the impact of HIT deployment as a result of ARRA/HITECH. These activities may require a shorter timeframe than other research activities proposed below in order to take advantage of the window of opportunity to study the impact of meaningful use and factors relating to the fast pace of upcoming HIT deployments generated by the federal funds.

Research agenda

Create a taxonomy to improve understanding of and develop consensus around terminology related to unintended consequences of HIT implementations

While acknowledging the inherent difficulty in unambiguously classifying unintended consequences, a taxonomy (including cognitive concepts) which documents a broad (albeit not exhaustive) array of these consequences will assist implementers and users of HIT as well as policymakers to view the range of potential unexpected, negative effects related to HIT implementations. It would be a useful tool to help build consensus around definitions of terms that are related (and potentially misused or misunderstood); for example, consequences that are anticipated versus unanticipated, direct versus indirect, desirable versus undesirable, etc. This, in turn, will aid in the development of workable approaches for identifying and mitigating unwanted consequences.

Conduct research to improve the ability to identify, anticipate, and avoid/mitigate unintended consequences

The implementation of complex HIT systems in complex healthcare organizations remains too much an art and too little a science. Research is needed to support ongoing identification of unintended consequences of HIT design and implementation efforts and the situations in which they are most likely to occur. These examinations would enable a better understanding of the risks inherent in applying technological solutions in clinical care settings, how these risks can be more effectively anticipated, and the actions that could be taken to reduce the chances of failure due to the risks. A first step would be to develop a list of common risk factors, drawn from the experience of HIT implementers in a wide variety of settings. Research may also be able to aid in the development of a predictive model that could help determine the extent to which unintended consequences are contributing factors to HIT success/failure.

Conduct cognitive-based research and development studies to relate HIT system design to management of unintended consequences

Although multiple studies have documented that the use of HIT changes care provider cognition, current knowledge is limited to the extent to which we know how to anticipate and manage these consequences in the natural chaotic and complex health system. Understanding and mitigating the unintended consequences that can arise as a result of human interaction with complex technology can help guide strategies to reduce some of these consequences, and facilitate improved management of those that cannot be reduced, leading to enhanced performance and patient safety, and increased user acceptance. Current HIT systems have been found to often violate usability heuristics; thus, research is needed on the human-computer interface to address reported and anecdotal usability issues related to system design problems. For example, research is needed on the best ways to display, synthesize, and process the increasingly large amounts of health data that can be stored in HIT systems (eg, EHRs) so that the cognitive abilities of the humans using these systems are not overtaxed and the best possible practical use can be made of the data without generating additional problems.

Determine and disseminate best practices for HIT design

Efforts are needed to synthesize the results of existing and future studies on unintended consequences in order to capture, compile, and disseminate best practices and guidelines for designing HIT systems; these should include usability guidelines, as well as proven technical and organizational safeguards. Comparing the usability of different HIT solutions would help identify those system features and factors that contribute the most to the success of HIT, thus providing data needed for best practices.

Determine and disseminate optimal organizational strategies for HIT system implementation

Implementation of an HIT solution in organizations with complex, clinical workflows brings with it many challenges and threats, including the risk of unintended consequences. A critically important component of an organization's preparation for an HIT implementation is a thorough review of its workflow processes, procedures, and role assignments; yet the complexity of the healthcare workflow makes it resistant to many conventional workflow modeling and automation approaches. Organizational strategies are needed to help prepare managers and users to anticipate, prevent, mitigate, or overcome negative consequences if they occur in the course of the implementation. For example, before embarking on a major implementation, an organizational readiness assessment and risk reduction plan should be carried out to determine if the organization is fully prepared, and cognizant of the possible consequences. A readiness assessment instrument/toolkit should be developed that addresses concerns in the multiple domains affected by the implementation.

Coordination and knowledge sharing

Develop a framework for sharing of experiences

The themes of 'reinventing the wheel' and 'rediscovering best practices' were raised with regard to learning about and applying knowledge of HIT-related unintended consequences. One model, described by Pronovost et al, proposes that the healthcare field coordinate national efforts to move patient safety forward by learning from the experience of the aviation industry; the Commercial Aviation Safety Team, a public/private partnership of safety officials and technical experts created after a 1995 plane crash to reduce fatal accidents, has been responsible for decreasing the average rate of fatal aviation accidents. 49 Sittig et al have suggested the formation of a central clearinghouse to facilitate the creation and dissemination of national EHR safety benchmarks. 50 While there was general agreement on the value of sharing HIT experiences, with Dr Blumenthal calling for the creation of a "learning community" by HIT practitioners during his plenary address, concern was also expressed about potential liability and litigation. The Aviation Safety Reporting System ( http://asrs.arc.nasa.gov/) was mentioned repeatedly as a potential model system. That system has three critical factors that need to be considered for any system designed to address patient safety issues in HIT: third party reporting, confidentiality, and limited liability projection. The open exchange of ideas is critical to the development and maintenance of optimal systems. Methods for identifying flaws in our current systems that take a punitive or regulatory approach will stifle this open exchange and will, ultimately, lead to self-protective behavior and inferior systems.

Federal Government oversight and leadership

Acknowledge the role and limitations of HIT

The Federal Government should take a leadership role in assuring that HIT is viewed as a strategic enabler of health system strengthening, but not the entire solution. Federal efforts should avoid fostering either a 'technology for technology's sake,' attitude, or a belief that technology will somehow 'fix' all of the healthcare systems ills. Rather, the Federal Government should encourage system designers and implementers to focus on the use of HIT to contribute to the ultimate goals of improvements in efficiency and outcomes. This approach should include an evidence-based assessment of HIT itself for both potential benefits and potential harms (eg, unintended consequences).

Undertake comparative effectiveness studies of HIT systems and implementations

Resources should be allocated to develop and implement the critical evaluative efforts noted above for systems purchased with ARRA-designated funds. For example, the Federal Government could fund the development and dissemination of validated approaches to measure implementation impact and help identify needed changes.

Identify and analyze effects of HIT-related policies

Analysis of intended and unintended consequences of ARRA/HITECH-related policies should be an integral component of Department of Health and Human Services (DHHS)/ONC efforts to promote HIT deployment in the U.S. It remains to be seen whether new Medicare and Medicaid incentives for meaningful use of HIT will ultimately result in quality improvements in health systems as well as patient outcomes. Thus, funding is needed to support research aimed at understanding the benefits and risks of these policies. This research will need to include rigorous monitoring and evaluation mechanisms to determine whether HIT meaningful use program goals are achieved, and also whether unintended consequences result at the population, patient, or provider level (eg, administrative burden on physicians and hospitals). The earlier that information is available about policy outcomes the better, with respect to reducing unintended consequences. 11

Create a framework and designate official groups to help ensure the safety and effectiveness of HIT systems

The Federal Government, building on existing models and working with organizations active in the patient safety and quality of care areas, should lead in the development of processes, systems, and entities to ensure the safe and effective use of HIT. For example, experts argue that it will be necessary to develop a comprehensive monitoring and evaluation framework and the infrastructure to oversee EHR use and implementation. This framework could include features such as a system to report adverse events or potential safety hazards; a national investigative board, created to look into them and make findings public; a self-assessment tool for EHR users and implementing organizations; and enhanced EHR certification and onsite accreditation of EHRs via periodic inspections. 50 Additionally, the interconnectivity of HIT that is envisioned and promoted by current legislation and regulation carries with it the need for HIT to be able to respond rapidly to evolving cyber-threats.

Promote additional information dissemination

Enhanced communication among multiple stakeholders in different sectors and disciplines will strengthen the collective ability to identify and address unintended consequences of HIT. Federal leadership is required to create incentives so that organizations will be more willing and able to share information about technical and organizational safeguards that address unintended consequences. Further, mechanisms are needed to facilitate sharing of the findings of HIT system implementers so that data captured by individual organizations can have broader impact.

Conclusions

Research on the challenges posed by implementing HIT in various care settings has already yielded much valuable data and many recommendations for identifying and ameliorating some of the unintended consequences related to these implementations. However, this knowledge has not sufficiently permeated the practice of HIT nationally; there has not been a concerted effort to collect, refine, and systematically disseminate these research findings to HIT implementers and users. Of further concern is the fact that this research has been conducted during a period of relatively slow adoption of HIT; with the current speeded-up trajectory of uptake of HIT, there may be new unintended consequences engendered by the rapidity of this technological and organizational change. The need for additional research into the unintended consequences of health IT implementations and the widespread dissemination of these findings is even more pressing now as, spurred by federal incentives, the nation moves into uncharted territory.

Weiner et al have called attention to and coined a term for what they describe as the ultimate of unintended consequences associated with HIT: 'e-iatrogenesis,' defined as "patient harm caused at least in part by the application of health information technology." They urge the healthcare industry to acknowledge that HIT represents "a new 21st century vector for medical care-system induced harm-something we must all work to understand, measure, and mitigate." 51

Healthcare is a complex socio-technical system, and it is the nature of such systems that failures and other undesirable outcomes may be unavoidable. 52 Understanding the limits and failures of decisions as we interact with HIT is important if we are to build robust systems and manage the risks of unintended consequences. Detection and correction of potential errors is an integral part of cognitive work in the complex, healthcare workplace. Ongoing research, usability, and evaluation efforts should also focus on developing approaches and strategies to enhance the ability to recover from or manage any unintended adverse consequences. While harmful unintended consequences, like many errors deriving from complex systems, can never be completely eliminated, the consensus of the meeting was that they can and should be reduced from their present levels.

Response and action by the AMIA Board of Directors (BOD)

By convening the 2009 conference and disseminating this paper, AMIA has further delineated critical issues related to unintended consequences of HIT and related policy. The AMIA BOD reviewed the article and endorsed its findings, conclusions, and recommendations. The BOD will continue to encourage other organizations to work collaboratively to continue this important public discourse. In addition, AMIA will forward the article and its recommendations to DHHS organizations for their review and consideration.

Acknowledgments

AMIA would like to acknowledge the input of the many participants and presenters from the 2009 AMIA Health Policy Meeting on which this article is based. Nancy Lorenzi and Justin Starren co-chaired the Steering Committee for the meeting. Joan Ash, David Bates, Meryl Bloomrosen, Trevor Cohen, Don Detmer, Richard Dykstra, Vojtech Huser, Julie McGowan, Ebele Okwumabua, Vimla Patel, Doug Peddicord, Josh Peterson, David Pieczkiewicz, Trent Rosenbloom, Ted Shortliffe, Freda Temple, and Adam Wright served as members of the Steering Committee. They were actively involved in and provided valuable input to all aspects of the planning processes. The authors also wish to express their thanks to Freda Temple for her careful review and editing of all versions of this manuscript.

Funding

The following organizations generously supported the 2009 meeting upon which this article is based: Cattails Software, GSK, IMO, Lilly, MedAssurant, Netezza, Security Health Plan, and Westat.

Competing interests

None.

Provenance and peer review

Not commissioned; externally peer reviewed.

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# data[].attributes.attachment_link.title
Anticipating and addressing the unintended consequences of health IT and policy: a report from the AMIA 2009 Health Policy Meeting

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Federal legislation (Health Information Technology for Economic and Clinical Health (HITECH) Act) has provided funds to support an unprecedented increase in health information technology (HIT) adoption for healthcare provider organizations and professionals throughout the U.S.

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The unintended consequences of for-profit virtual care

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The unintended consequences of for-profit virtual care

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This article discusses the for-profit virtual healthcare in Canada. Virtual healthcare can be a great asset in providing care to those who need it with a regular healthcare provider. It can provide continuity of care, great access and a way to closely monitor patients with serious conditions. However, the for-profit virtual healthcare is geared more with monetary gains in mind, so often the healthcare outcome suffers. This article considers some of the unintended consequences that come with for-profit systems, such as unnecessary tests, duplication of services, inequity, and patient privacy.

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This article touches on the privacy and security of electronic health records. It takes a holistic approach to the privacy and security discussion surrounding EHRs and how far we have come. It also touches on the authorized use of information in EHRs and the exchange of information from physician to physician and how that information is transferred from one to the other. Many physicians use their mobile devices to exchange patient information and there is no way to monitor this. More research needs to be done to determine changes that need to be made.

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This article covers privacy and security in relation to the position taken by the American College of Physicians. This paper discusses the state of privacy legislation, concerns in current privacy protections, as well as recommendations going forward.

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If the address matches an existing account you will receive an email with instructions to reset your password.

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This article from the Journal of Big Data talks about where the data comes from and how it is obtained. Massive amounts of data are obtained through technological means, especially now that we have come so far in the field of technology. Literally almost everything is available online. This massive amount of data has created a data overload. Though all of this data is helpful, we have more than we know what to do with.

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Information has been the key to a better organization and new developments. The more information we have, the more optimally we can organize ourselves to deliver the best outcomes. That is why data collection is an important part for every organization. We can also use this data for the prediction of current trends of certain parameters and future events. As we are becoming more and more aware of this, we have started producing and collecting more data about almost everything by introducing technological developments in this direction. Today, we are facing a situation wherein we are flooded with tons of data from every aspect of our life such as social activities, science, work, health, etc. In a way, we can compare the present situation to a data deluge. The technological advances have helped us in generating more and more data, even to a level where it has become unmanageable with currently available technologies. This has led to the creation of the term 'big data' to describe data that is large and unmanageable. In order to meet our present and future social needs, we need to develop new strategies to organize this data and derive meaningful information. One such special social need is healthcare. Like every other industry, healthcare organizations are producing data at a tremendous rate that presents many advantages and challenges at the same time. In this review, we discuss about the basics of big data including its management, analysis and future prospects especially in healthcare sector.

The data overload

Every day, people working with various organizations around the world are generating a massive amount of data. The term "digital universe" quantitatively defines such massive amounts of data created, replicated, and consumed in a single year. International Data Corporation (IDC) estimated the approximate size of the digital universe in 2005 to be 130 exabytes (EB). The digital universe in 2017 expanded to about 16,000 EB or 16 zettabytes (ZB). IDC predicted that the digital universe would expand to 40,000 EB by the year 2020. To imagine this size, we would have to assign about 5200 gigabytes (GB) of data to all individuals. This exemplifies the phenomenal speed at which the digital universe is expanding. The internet giants, like Google and Facebook, have been collecting and storing massive amounts of data. For instance, depending on our preferences, Google may store a variety of information including user location, advertisement preferences, list of applications used, internet browsing history, contacts, bookmarks, emails, and other necessary information associated with the user. Similarly, Facebook stores and analyzes more than about 30 petabytes (PB) of user-generated data. Such large amounts of data constitute ' big data '. Over the past decade, big data has been successfully used by the IT industry to generate critical information that can generate significant revenue.

These observations have become so conspicuous that has eventually led to the birth of a new field of science termed ' Data Science '. Data science deals with various aspects including data management and analysis, to extract deeper insights for improving the functionality or services of a system (for example, healthcare and transport system). Additionally, with the availability of some of the most creative and meaningful ways to visualize big data post-analysis, it has become easier to understand the functioning of any complex system. As a large section of society is becoming aware of, and involved in generating big data, it has become necessary to define what big data is. Therefore, in this review, we attempt to provide details on the impact of big data in the transformation of global healthcare sector and its impact on our daily lives.

Defining big data

As the name suggests, 'big data' represents large amounts of data that is unmanageable using traditional software or internet-based platforms. It surpasses the traditionally used amount of storage, processing and analytical power. Even though a number of definitions for big data exist, the most popular and well-accepted definition was given by Douglas Laney. Laney observed that (big) data was growing in three different dimensions namely, volume, velocity and variety (known as the 3 Vs) [ 1]. The 'big' part of big data is indicative of its large volume. In addition to volume, the big data description also includes velocity and variety. Velocity indicates the speed or rate of data collection and making it accessible for further analysis; while, variety remarks on the different types of organized and unorganized data that any firm or system can collect, such as transaction-level data, video, audio, text or log files. These three Vs have become the standard definition of big data. Although, other people have added several other Vs to this definition [ 2], the most accepted 4th V remains 'veracity'.

The term " big data" has become extremely popular across the globe in recent years. Almost every sector of research, whether it relates to industry or academics, is generating and analyzing big data for various purposes. The most challenging task regarding this huge heap of data that can be organized and unorganized, is its management. Given the fact that big data is unmanageable using the traditional software, we need technically advanced applications and software that can utilize fast and cost-efficient high-end computational power for such tasks. Implementation of artificial intelligence (AI) algorithms and novel fusion algorithms would be necessary to make sense from this large amount of data. Indeed, it would be a great feat to achieve automated decision-making by the implementation of machine learning (ML) methods like neural networks and other AI techniques. However, in absence of appropriate software and hardware support, big data can be quite hazy. We need to develop better techniques to handle this 'endless sea' of data and smart web applications for efficient analysis to gain workable insights. With proper storage and analytical tools in hand, the information and insights derived from big data can make the critical social infrastructure components and services (like healthcare, safety or transportation) more aware, interactive and efficient [ 3]. In addition, visualization of big data in a user-friendly manner will be a critical factor for societal development.

Healthcare as a big-data repository

Healthcare is a multi-dimensional system established with the sole aim for the prevention, diagnosis, and treatment of health-related issues or impairments in human beings. The major components of a healthcare system are the health professionals (physicians or nurses), health facilities (clinics, hospitals for delivering medicines and other diagnosis or treatment technologies), and a financing institution supporting the former two. The health professionals belong to various health sectors like dentistry, medicine, midwifery, nursing, psychology, physiotherapy, and many others. Healthcare is required at several levels depending on the urgency of situation. Professionals serve it as the first point of consultation (for primary care), acute care requiring skilled professionals (secondary care), advanced medical investigation and treatment (tertiary care) and highly uncommon diagnostic or surgical procedures (quaternary care). At all these levels, the health professionals are responsible for different kinds of information such as patient's medical history (diagnosis and prescriptions related data), medical and clinical data (like data from imaging and laboratory examinations), and other private or personal medical data. Previously, the common practice to store such medical records for a patient was in the form of either handwritten notes or typed reports [ 4]. Even the results from a medical examination were stored in a paper file system. In fact, this practice is really old, with the oldest case reports existing on a papyrus text from Egypt that dates back to 1600 BC [ 5]. In Stanley Reiser's words, the clinical case records freeze the episode of illness as a story in which patient, family and the doctor are a part of the plot" [ 6].

With the advent of computer systems and its potential, the digitization of all clinical exams and medical records in the healthcare systems has become a standard and widely adopted practice nowadays. In 2003, a division of the National Academies of Sciences, Engineering, and Medicine known as Institute of Medicine chose the term " electronic health records" to represent records maintained for improving the health care sector towards the benefit of patients and clinicians. Electronic health records (EHR) as defined by Murphy, Hanken and Waters are computerized medical records for patients any information relating to the past, present or future physical/mental health or condition of an individual which resides in electronic system(s) used to capture, transmit, receive, store, retrieve, link and manipulate multimedia data for the primary purpose of providing healthcare and health-related services" [ 7].

Electronic health records

It is important to note that the National Institutes of Health (NIH) recently announced the "All of Us" initiative ( https://allofus.nih.gov/) that aims to collect one million or more patients' data such as EHR, including medical imaging, socio-behavioral, and environmental data over the next few years. EHRs have introduced many advantages for handling modern healthcare related data. Below, we describe some of the characteristic advantages of using EHRs. The first advantage of EHRs is that healthcare professionals have an improved access to the entire medical history of a patient. The information includes medical diagnoses, prescriptions, data related to known allergies, demographics, clinical narratives, and the results obtained from various laboratory tests. The recognition and treatment of medical conditions thus is time efficient due to a reduction in the lag time of previous test results. With time we have observed a significant decrease in the redundant and additional examinations, lost orders and ambiguities caused by illegible handwriting, and an improved care coordination between multiple healthcare providers. Overcoming such logistical errors has led to reduction in the number of drug allergies by reducing errors in medication dose and frequency. Healthcare professionals have also found access over web based and electronic platforms to improve their medical practices significantly using automatic reminders and prompts regarding vaccinations, abnormal laboratory results, cancer screening, and other periodic checkups. There would be a greater continuity of care and timely interventions by facilitating communication among multiple healthcare providers and patients. They can be associated to electronic authorization and immediate insurance approvals due to less paperwork. EHRs enable faster data retrieval and facilitate reporting of key healthcare quality indicators to the organizations, and also improve public health surveillance by immediate reporting of disease outbreaks. EHRs also provide relevant data regarding the quality of care for the beneficiaries of employee health insurance programs and can help control the increasing costs of health insurance benefits. Finally, EHRs can reduce or absolutely eliminate delays and confusion in the billing and claims management area. The EHRs and internet together help provide access to millions of health-related medical information critical for patient life.

Digitization of healthcare and big data

Similar to EHR, an electronic medical record (EMR) stores the standard medical and clinical data gathered from the patients. EHRs, EMRs, personal health record (PHR), medical practice management software (MPM), and many other healthcare data components collectively have the potential to improve the quality, service efficiency, and costs of healthcare along with the reduction of medical errors. The big data in healthcare includes the healthcare payer-provider data (such as EMRs, pharmacy prescription, and insurance records) along with the genomics-driven experiments (such as genotyping, gene expression data) and other data acquired from the smart web of internet of things (IoT) (Fig. 1). The adoption of EHRs was slow at the beginning of the 21st century however it has grown substantially after 2009 [ 7, 8]. The management and usage of such healthcare data has been increasingly dependent on information technology. The development and usage of wellness monitoring devices and related software that can generate alerts and share the health related data of a patient with the respective health care providers has gained momentum, especially in establishing a real-time biomedical and health monitoring system. These devices are generating a huge amount of data that can be analyzed to provide real-time clinical or medical care [ 9]. The use of big data from healthcare shows promise for improving health outcomes and controlling costs.

Big data in biomedical research

A biological system, such as a human cell, exhibits molecular and physical events of complex interplay. In order to understand interdependencies of various components and events of such a complex system, a biomedical or biological experiment usually gathers data on a smaller and/or simpler component. Consequently, it requires multiple simplified experiments to generate a wide map of a given biological phenomenon of interest. This indicates that more the data we have, the better we understand the biological processes. With this idea, modern techniques have evolved at a great pace. For instance, one can imagine the amount of data generated since the integration of efficient technologies like next-generation sequencing (NGS) and Genome wide association studies (GWAS) to decode human genetics. NGS-based data provides information at depths that were previously inaccessible and takes the experimental scenario to a completely new dimension. It has increased the resolution at which we observe or record biological events associated with specific diseases in a real time manner. The idea that large amounts of data can provide us a good amount of information that often remains unidentified or hidden in smaller experimental methods has ushered-in the '- omics' era. The ' omics' discipline has witnessed significant progress as instead of studying a single ' gene' scientists can now study the whole ' genome' of an organism in ' genomics' studies within a given amount of time. Similarly, instead of studying the expression or ' transcription' of single gene, we can now study the expression of all the genes or the entire ' transcriptome' of an organism under ' transcriptomics' studies. Each of these individual experiments generate a large amount of data with more depth of information than ever before. Yet, this depth and resolution might be insufficient to provide all the details required to explain a particular mechanism or event. Therefore, one usually finds oneself analyzing a large amount of data obtained from multiple experiments to gain novel insights. This fact is supported by a continuous rise in the number of publications regarding big data in healthcare (Fig. 2). Analysis of such big data from medical and healthcare systems can be of immense help in providing novel strategies for healthcare. The latest technological developments in data generation, collection and analysis, have raised expectations towards a revolution in the field of personalized medicine in near future.

Big data from omics studies

NGS has greatly simplified the sequencing and decreased the costs for generating whole genome sequence data. The cost of complete genome sequencing has fallen from millions to a couple of thousand dollars [ 10]. NGS technology has resulted in an increased volume of biomedical data that comes from genomic and transcriptomic studies. According to an estimate, the number of human genomes sequenced by 2025 could be between 100 million to 2 billion [ 11]. Combining the genomic and transcriptomic data with proteomic and metabolomic data can greatly enhance our knowledge about the individual profile of a patient-an approach often ascribed as "individual, personalized or precision health care". Systematic and integrative analysis of omics data in conjugation with healthcare analytics can help design better treatment strategies towards precision and personalized medicine (Fig. 3). The genomics-driven experiments e.g., genotyping, gene expression, and NGS-based studies are the major source of big data in biomedical healthcare along with EMRs, pharmacy prescription information, and insurance records. Healthcare requires a strong integration of such biomedical data from various sources to provide better treatments and patient care. These prospects are so exciting that even though genomic data from patients would have many variables to be accounted, yet commercial organizations are already using human genome data to help the providers in making personalized medical decisions. This might turn out to be a game-changer in future medicine and health.

Internet of Things (IOT)

Healthcare industry has not been quick enough to adapt to the big data movement compared to other industries. Therefore, big data usage in the healthcare sector is still in its infancy. For example, healthcare and biomedical big data have not yet converged to enhance healthcare data with molecular pathology. Such convergence can help unravel various mechanisms of action or other aspects of predictive biology. Therefore, to assess an individual's health status, biomolecular and clinical datasets need to be married. One such source of clinical data in healthcare is 'internet of things' (IoT).

In fact, IoT is another big player implemented in a number of other industries including healthcare. Until recently, the objects of common use such as cars, watches, refrigerators and health-monitoring devices, did not usually produce or handle data and lacked internet connectivity. However, furnishing such objects with computer chips and sensors that enable data collection and transmission over internet has opened new avenues. The device technologies such as Radio Frequency IDentification (RFID) tags and readers, and Near Field Communication (NFC) devices, that can not only gather information but interact physically, are being increasingly used as the information and communication systems [ 3]. This enables objects with RFID or NFC to communicate and function as a web of smart things. The analysis of data collected from these chips or sensors may reveal critical information that might be beneficial in improving lifestyle, establishing measures for energy conservation, improving transportation, and healthcare. In fact, IoT has become a rising movement in the field of healthcare. IoT devices create a continuous stream of data while monitoring the health of people (or patients) which makes these devices a major contributor to big data in healthcare. Such resources can interconnect various devices to provide a reliable, effective and smart healthcare service to the elderly and patients with a chronic illness [ 12].

Advantages of IoT in healthcare

Using the web of IoT devices, a doctor can measure and monitor various parameters from his/her clients in their respective locations for example, home or office. Therefore, through early intervention and treatment, a patient might not need hospitalization or even visit the doctor resulting in significant cost reduction in healthcare expenses. Some examples of IoT devices used in healthcare include fitness or health-tracking wearable devices, biosensors, clinical devices for monitoring vital signs, and others types of devices or clinical instruments. Such IoT devices generate a large amount of health related data. If we can integrate this data with other existing healthcare data like EMRs or PHRs, we can predict a patients' health status and its progression from subclinical to pathological state [ 9]. In fact, big data generated from IoT has been quiet advantageous in several areas in offering better investigation and predictions. On a larger scale, the data from such devices can help in personnel health monitoring, modelling the spread of a disease and finding ways to contain a particular disease outbreak.

The analysis of data from IoT would require an updated operating software because of its specific nature along with advanced hardware and software applications. We would need to manage data inflow from IoT instruments in real-time and analyze it by the minute. Associates in the healthcare system are trying to trim down the cost and ameliorate the quality of care by applying advanced analytics to both internally and externally generated data.

Mobile computing and mobile health (mHealth)

In today's digital world, every individual seems to be obsessed to track their fitness and health statistics using the in-built pedometer of their portable and wearable devices such as, smartphones, smartwatches, fitness dashboards or tablets. With an increasingly mobile society in almost all aspects of life, the healthcare infrastructure needs remodeling to accommodate mobile devices [ 13]. The practice of medicine and public health using mobile devices, known as mHealth or mobile health, pervades different degrees of health care especially for chronic diseases, such as diabetes and cancer [ 14]. Healthcare organizations are increasingly using mobile health and wellness services for implementing novel and innovative ways to provide care and coordinate health as well as wellness. Mobile platforms can improve healthcare by accelerating interactive communication between patients and healthcare providers. In fact, Apple and Google have developed devoted platforms like Apple's ResearchKit and Google Fit for developing research applications for fitness and health statistics [ 15]. These applications support seamless interaction with various consumer devices and embedded sensors for data integration. These apps help the doctors to have direct access to your overall health data. Both the user and their doctors get to know the real-time status of your body. These apps and smart devices also help by improving our wellness planning and encouraging healthy lifestyles. The users or patients can become advocates for their own health.

Nature of the big data in healthcare

EHRs can enable advanced analytics and help clinical decision-making by providing enormous data. However, a large proportion of this data is currently unstructured in nature. An unstructured data is the information that does not adhere to a pre-defined model or organizational framework. The reason for this choice may simply be that we can record it in a myriad of formats. Another reason for opting unstructured format is that often the structured input options (drop-down menus, radio buttons, and check boxes) can fall short for capturing data of complex nature. For example, we cannot record the non-standard data regarding a patient's clinical suspicions, socioeconomic data, patient preferences, key lifestyle factors, and other related information in any other way but an unstructured format. It is difficult to group such varied, yet critical, sources of information into an intuitive or unified data format for further analysis using algorithms to understand and leverage the patients care. Nonetheless, the healthcare industry is required to utilize the full potential of these rich streams of information to enhance the patient experience. In the healthcare sector, it could materialize in terms of better management, care and low-cost treatments. We are miles away from realizing the benefits of big data in a meaningful way and harnessing the insights that come from it. In order to achieve these goals, we need to manage and analyze the big data in a systematic manner.

Management and analysis of big data

Big data is the huge amounts of a variety of data generated at a rapid rate. The data gathered from various sources is mostly required for optimizing consumer services rather than consumer consumption. This is also true for big data from the biomedical research and healthcare. The major challenge with big data is how to handle this large volume of information. To make it available for scientific community, the data is required to be stored in a file format that is easily accessible and readable for an efficient analysis. In the context of healthcare data, another major challenge is the implementation of high-end computing tools, protocols and high-end hardware in the clinical setting. Experts from diverse backgrounds including biology, information technology, statistics, and mathematics are required to work together to achieve this goal. The data collected using the sensors can be made available on a storage cloud with pre-installed software tools developed by analytic tool developers. These tools would have data mining and ML functions developed by AI experts to convert the information stored as data into knowledge. Upon implementation, it would enhance the efficiency of acquiring, storing, analyzing, and visualization of big data from healthcare. The main task is to annotate, integrate, and present this complex data in an appropriate manner for a better understanding. In absence of such relevant information, the (healthcare) data remains quite cloudy and may not lead the biomedical researchers any further. Finally, visualization tools developed by computer graphics designers can efficiently display this newly gained knowledge.

Heterogeneity of data is another challenge in big data analysis. The huge size and highly heterogeneous nature of big data in healthcare renders it relatively less informative using the conventional technologies. The most common platforms for operating the software framework that assists big data analysis are high power computing clusters accessed via grid computing infrastructures. Cloud computing is such a system that has virtualized storage technologies and provides reliable services. It offers high reliability, scalability and autonomy along with ubiquitous access, dynamic resource discovery and composability. Such platforms can act as a receiver of data from the ubiquitous sensors, as a computer to analyze and interpret the data, as well as providing the user with easy to understand web-based visualization. In IoT, the big data processing and analytics can be performed closer to data source using the services of mobile edge computing cloudlets and fog computing. Advanced algorithms are required to implement ML and AI approaches for big data analysis on computing clusters. A programming language suitable for working on big data (e.g. Python, R or other languages) could be used to write such algorithms or software. Therefore, a good knowledge of biology and IT is required to handle the big data from biomedical research. Such a combination of both the trades usually fits for bioinformaticians. The most common among various platforms used for working with big data include Hadoop and Apache Spark. We briefly introduce these platforms below.

Hadoop

Loading large amounts of (big) data into the memory of even the most powerful of computing clusters is not an efficient way to work with big data. Therefore, the best logical approach for analyzing huge volumes of complex big data is to distribute and process it in parallel on multiple nodes. However, the size of data is usually so large that thousands of computing machines are required to distribute and finish processing in a reasonable amount of time. When working with hundreds or thousands of nodes, one has to handle issues like how to parallelize the computation, distribute the data, and handle failures. One of most popular open-source distributed application for this purpose is Hadoop [ 16]. Hadoop implements MapReduce algorithm for processing and generating large datasets. MapReduce uses map and reduce primitives to map each logical record' in the input into a set of intermediate key/value pairs, and reduce operation combines all the values that shared the same key [ 17]. It efficiently parallelizes the computation, handles failures, and schedules inter-machine communication across large-scale clusters of machines. Hadoop Distributed File System (HDFS) is the file system component that provides a scalable, efficient, and replica based storage of data at various nodes that form a part of a cluster [ 16]. Hadoop has other tools that enhance the storage and processing components therefore many large companies like Yahoo, Facebook, and others have rapidly adopted it. Hadoop has enabled researchers to use data sets otherwise impossible to handle. Many large projects, like the determination of a correlation between the air quality data and asthma admissions, drug development using genomic and proteomic data, and other such aspects of healthcare are implementing Hadoop. Therefore, with the implementation of Hadoop system, the healthcare analytics will not be held back.

Apache Spark

Apache Spark is another open source alternative to Hadoop. It is a unified engine for distributed data processing that includes higher-level libraries for supporting SQL queries ( Spark SQL), streaming data ( Spark Streaming), machine learning ( MLlib) and graph processing ( GraphX) [ 18]. These libraries help in increasing developer productivity because the programming interface requires lesser coding efforts and can be seamlessly combined to create more types of complex computations. By implementing Resilient distributed Datasets (RDDs), in-memory processing of data is supported that can make Spark about 100× faster than Hadoop in multi-pass analytics (on smaller datasets) [ 19, 20]. This is more true when the data size is smaller than the available memory [ 21]. This indicates that processing of really big data with Apache Spark would require a large amount of memory. Since, the cost of memory is higher than the hard drive, MapReduce is expected to be more cost effective for large datasets compared to Apache Spark. Similarly, Apache Storm was developed to provide a real-time framework for data stream processing. This platform supports most of the programming languages. Additionally, it offers good horizontal scalability and built-in-fault-tolerance capability for big data analysis.

Machine learning for information extraction, data analysis and predictions

In healthcare, patient data contains recorded signals for instance, electrocardiogram (ECG), images, and videos. Healthcare providers have barely managed to convert such healthcare data into EHRs. Efforts are underway to digitize patient-histories from pre-EHR era notes and supplement the standardization process by turning static images into machine-readable text. For example, optical character recognition (OCR) software is one such approach that can recognize handwriting as well as computer fonts and push digitization. Such unstructured and structured healthcare datasets have untapped wealth of information that can be harnessed using advanced AI programs to draw critical actionable insights in the context of patient care. In fact, AI has emerged as the method of choice for big data applications in medicine. This smart system has quickly found its niche in decision making process for the diagnosis of diseases. Healthcare professionals analyze such data for targeted abnormalities using appropriate ML approaches. ML can filter out structured information from such raw data.

Extracting information from EHR datasets

Emerging ML or AI based strategies are helping to refine healthcare industry's information processing capabilities. For example, natural language processing (NLP) is a rapidly developing area of machine learning that can identify key syntactic structures in free text, help in speech recognition and extract the meaning behind a narrative. NLP tools can help generate new documents, like a clinical visit summary, or to dictate clinical notes. The unique content and complexity of clinical documentation can be challenging for many NLP developers. Nonetheless, we should be able to extract relevant information from healthcare data using such approaches as NLP.

AI has also been used to provide predictive capabilities to healthcare big data. For example, ML algorithms can convert the diagnostic system of medical images into automated decision-making. Though it is apparent that healthcare professionals may not be replaced by machines in the near future, yet AI can definitely assist physicians to make better clinical decisions or even replace human judgment in certain functional areas of healthcare.

Image analytics

Some of the most widely used imaging techniques in healthcare include computed tomography (CT), magnetic resonance imaging (MRI), X-ray, molecular imaging, ultrasound, photo-acoustic imaging, functional MRI (fMRI), positron emission tomography (PET), electroencephalography (EEG), and mammograms. These techniques capture high definition medical images (patient data) of large sizes. Healthcare professionals like radiologists, doctors and others do an excellent job in analyzing medical data in the form of these files for targeted abnormalities. However, it is also important to acknowledge the lack of specialized professionals for many diseases. In order to compensate for this dearth of professionals, efficient systems like Picture Archiving and Communication System (PACS) have been developed for storing and convenient access to medical image and reports data [ 22]. PACSs are popular for delivering images to local workstations, accomplished by protocols such as digital image communication in medicine (DICOM). However, data exchange with a PACS relies on using structured data to retrieve medical images. This by nature misses out on the unstructured information contained in some of the biomedical images. Moreover, it is possible to miss an additional information about a patient's health status that is present in these images or similar data. A professional focused on diagnosing an unrelated condition might not observe it, especially when the condition is still emerging. To help in such situations, image analytics is making an impact on healthcare by actively extracting disease biomarkers from biomedical images. This approach uses ML and pattern recognition techniques to draw insights from massive volumes of clinical image data to transform the diagnosis, treatment and monitoring of patients. It focuses on enhancing the diagnostic capability of medical imaging for clinical decision-making.

A number of software tools have been developed based on functionalities such as generic, registration, segmentation, visualization, reconstruction, simulation and diffusion to perform medical image analysis in order to dig out the hidden information. For example, Visualization Toolkit is a freely available software which allows powerful processing and analysis of 3D images from medical tests [ 23], while SPM can process and analyze 5 different types of brain images (e.g. MRI, fMRI, PET, CT-Scan and EEG) [ 24]. Other software like GIMIAS, Elastix, and MITK support all types of images. Various other widely used tools and their features in this domain are listed in Table 1. Such bioinformatics-based big data analysis may extract greater insights and value from imaging data to boost and support precision medicine projects, clinical decision support tools, and other modes of healthcare. For example, we can also use it to monitor new targeted-treatments for cancer.

Table 1 Bioinformatics tools for medical image processing and analysis

Big data from omics

The big data from "omics" studies is a new kind of challenge for the bioinformaticians. Robust algorithms are required to analyze such complex data from biological systems. The ultimate goal is to convert this huge data into an informative knowledge base. The application of bioinformatics approaches to transform the biomedical and genomics data into predictive and preventive health is known as translational bioinformatics. It is at the forefront of data-driven healthcare. Various kinds of quantitative data in healthcare, for example from laboratory measurements, medication data and genomic profiles, can be combined and used to identify new meta-data that can help precision therapies [ 25]. This is why emerging new technologies are required to help in analyzing this digital wealth. In fact, highly ambitious multimillion-dollar projects like " Big Data Research and Development Initiative" have been launched that aim to enhance the quality of big data tools and techniques for a better organization, efficient access and smart analysis of big data. There are many advantages anticipated from the processing of ' omics' data from large-scale Human Genome Project and other population sequencing projects. In the population sequencing projects like 1000 genomes, the researchers will have access to a marvelous amount of raw data. Similarly, Human Genome Project based Encyclopedia of DNA Elements (ENCODE) project aimed to determine all functional elements in the human genome using bioinformatics approaches. Here, we list some of the widely used bioinformatics-based tools for big data analytics on omics data.

8.
BlueSNP is an R package based on Hadoop platform used for genome-wide association studies (GWAS) analysis, primarily aiming on the statistical readouts to obtain significant associations between genotype-phenotype datasets. The efficiency of this tool is estimated to analyze 1000 phenotypes on 10 ⁶ SNPs in 10 ⁴ individuals in a duration of half-an-hour.

The past few years have witnessed a tremendous increase in disease specific datasets from omics platforms. For example, the ArrayExpress Archive of Functional Genomics data repository contains information from approximately 30,000 experiments and more than one million functional assays. The growing amount of data demands for better and efficient bioinformatics driven packages to analyze and interpret the information obtained. This has also led to the birth of specific tools to analyze such massive amounts of data. Below, we mention some of the most popular commercial platforms for big data analytics.

Commercial platforms for healthcare data analytics

In order to tackle big data challenges and perform smoother analytics, various companies have implemented AI to analyze published results, textual data, and image data to obtain meaningful outcomes. IBM Corporation is one of the biggest and experienced players in this sector to provide healthcare analytics services commercially. IBM's Watson Health is an AI platform to share and analyze health data among hospitals, providers and researchers. Similarly, Flatiron Health provides technology-oriented services in healthcare analytics specially focused in cancer research. Other big companies such as Oracle Corporation and Google Inc. are also focusing to develop cloud-based storage and distributed computing power platforms. Interestingly, in the recent few years, several companies and start-ups have also emerged to provide health care-based analytics and solutions. Some of the vendors in healthcare sector are provided in Table 2. Below we discuss a few of these commercial solutions.

Table 2 List of some of big companies which provide services on big data analysis in healthcare sector

AYASDI

Ayasdi is one such big vendor which focuses on ML based methodologies to primarily provide machine intelligence platform along with an application framework with tried & tested enterprise scalability. It provides various applications for healthcare analytics, for example, to understand and manage clinical variation, and to transform clinical care costs. It is also capable of analyzing and managing how hospitals are organized, conversation between doctors, risk-oriented decisions by doctors for treatment, and the care they deliver to patients. It also provides an application for the assessment and management of population health, a proactive strategy that goes beyond traditional risk analysis methodologies. It uses ML intelligence for predicting future risk trajectories, identifying risk drivers, and providing solutions for best outcomes. A strategic illustration of the company's methodology for analytics is provided in Fig. 4.

Linguamatics

It is an NLP based algorithm that relies on an interactive text mining algorithm (I2E). I2E can extract and analyze a wide array of information. Results obtained using this technique are tenfold faster than other tools and does not require expert knowledge for data interpretation. This approach can provide information on genetic relationships and facts from unstructured data. Classical, ML requires well-curated data as input to generate clean and filtered results. However, NLP when integrated in EHR or clinical records per se facilitates the extraction of clean and structured information that often remains hidden in unstructured input data (Fig. 5).

IBM Watson

This is one of the unique ideas of the tech-giant IBM that targets big data analytics in almost every professional sector. This platform utilizes ML and AI based algorithms extensively to extract the maximum information from minimal input. IBM Watson enforces the regimen of integrating a wide array of healthcare domains to provide meaningful and structured data (Fig. 6). In an attempt to uncover novel drug targets specifically in cancer disease model, IBM Watson and Pfizer have formed a productive collaboration to accelerate the discovery of novel immune-oncology combinations. Combining Watson's deep learning modules integrated with AI technologies allows the researchers to interpret complex genomic data sets. IBM Watson has been used to predict specific types of cancer based on the gene expression profiles obtained from various large data sets providing signs of multiple druggable targets. IBM Watson is also used in drug discovery programs by integrating curated literature and forming network maps to provide a detailed overview of the molecular landscape in a specific disease model.

In order to analyze the diversified medical data, healthcare domain, describes analytics in four categories: descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics refers for describing the current medical situations and commenting on that whereas diagnostic analysis explains reasons and factors behind occurrence of certain events, for example, choosing treatment option for a patient based on clustering and decision trees. Predictive analytics focuses on predictive ability of the future outcomes by determining trends and probabilities. These methods are mainly built up of machine leaning techniques and are helpful in the context of understanding complications that a patient can develop. Prescriptive analytics is to perform analysis to propose an action towards optimal decision making. For example, decision of avoiding a given treatment to the patient based on observed side effects and predicted complications. In order to improve performance of the current medical systems integration of big data into healthcare analytics can be a major factor; however, sophisticated strategies need to be developed. An architecture of best practices of different analytics in healthcare domain is required for integrating big data technologies to improve the outcomes. However, there are many challenges associated with the implementation of such strategies.

Challenges associated with healthcare big data

Methods for big data management and analysis are being continuously developed especially for real-time data streaming, capture, aggregation, analytics (using ML and predictive), and visualization solutions that can help integrate a better utilization of EMRs with the healthcare. For example, the EHR adoption rate of federally tested and certified EHR programs in the healthcare sector in the U.S.A. is nearly complete [ 7]. However, the availability of hundreds of EHR products certified by the government, each with different clinical terminologies, technical specifications, and functional capabilities has led to difficulties in the interoperability and sharing of data. Nonetheless, we can safely say that the healthcare industry has entered into a 'post-EMR' deployment phase. Now, the main objective is to gain actionable insights from these vast amounts of data collected as EMRs. Here, we discuss some of these challenges in brief.

Storage

Storing large volume of data is one of the primary challenges, but many organizations are comfortable with data storage on their own premises. It has several advantages like control over security, access, and up-time. However, an on-site server network can be expensive to scale and difficult to maintain. It appears that with decreasing costs and increasing reliability, the cloud-based storage using IT infrastructure is a better option which most of the healthcare organizations have opted for. Organizations must choose cloud-partners that understand the importance of healthcare-specific compliance and security issues. Additionally, cloud storage offers lower up-front costs, nimble disaster recovery, and easier expansion. Organizations can also have a hybrid approach to their data storage programs, which may be the most flexible and workable approach for providers with varying data access and storage needs.

Cleaning

The data needs to cleansed or scrubbed to ensure the accuracy, correctness, consistency, relevancy, and purity after acquisition. This cleaning process can be manual or automatized using logic rules to ensure high levels of accuracy and integrity. More sophisticated and precise tools use machine-learning techniques to reduce time and expenses and to stop foul data from derailing big data projects.

Unified format

Patients produce a huge volume of data that is not easy to capture with traditional EHR format, as it is knotty and not easily manageable. It is too difficult to handle big data especially when it comes without a perfect data organization to the healthcare providers. A need to codify all the clinically relevant information surfaced for the purpose of claims, billing purposes, and clinical analytics. Therefore, medical coding systems like Current Procedural Terminology (CPT) and International Classification of Diseases (ICD) code sets were developed to represent the core clinical concepts. However, these code sets have their own limitations.

Accuracy

Some studies have observed that the reporting of patient data into EMRs or EHRs is not entirely accurate yet [ 26, 27, 28, 29], probably because of poor EHR utility, complex workflows, and a broken understanding of why big data is all-important to capture well. All these factors can contribute to the quality issues for big data all along its lifecycle. The EHRs intend to improve the quality and communication of data in clinical workflows though reports indicate discrepancies in these contexts. The documentation quality might improve by using self-report questionnaires from patients for their symptoms.

Image pre-processing

Studies have observed various physical factors that can lead to altered data quality and misinterpretations from existing medical records [ 30]. Medical images often suffer technical barriers that involve multiple types of noise and artifacts. Improper handling of medical images can also cause tampering of images for instance might lead to delineation of anatomical structures such as veins which is non-correlative with real case scenario. Reduction of noise, clearing artifacts, adjusting contrast of acquired images and image quality adjustment post mishandling are some of the measures that can be implemented to benefit the purpose.

Security

There have been many security breaches, hackings, phishing attacks, and ransomware episodes that data security is a priority for healthcare organizations. After noticing an array of vulnerabilities, a list of technical safeguards was developed for the protected health information (PHI). These rules, termed as HIPAA Security Rules, help guide organizations with storing, transmission, authentication protocols, and controls over access, integrity, and auditing. Common security measures like using up-to-date anti-virus software, firewalls, encrypting sensitive data, and multi-factor authentication can save a lot of trouble.

Meta-data

To have a successful data governance plan, it would be mandatory to have complete, accurate, and up-to-date metadata regarding all the stored data. The metadata would be composed of information like time of creation, purpose and person responsible for the data, previous usage (by who, why, how, and when) for researchers and data analysts. This would allow analysts to replicate previous queries and help later scientific studies and accurate benchmarking. This increases the usefulness of data and prevents creation of "data dumpsters" of low or no use.

Querying

Metadata would make it easier for organizations to query their data and get some answers. However, in absence of proper interoperability between datasets the query tools may not access an entire repository of data. Also, different components of a dataset should be well interconnected or linked and easily accessible otherwise a complete portrait of an individual patient's health may not be generated. Medical coding systems like ICD-10, SNOMED-CT, or LOINC must be implemented to reduce free-form concepts into a shared ontology. If the accuracy, completeness, and standardization of the data are not in question, then Structured Query Language (SQL) can be used to query large datasets and relational databases.

Visualization

A clean and engaging visualization of data with charts, heat maps, and histograms to illustrate contrasting figures and correct labeling of information to reduce potential confusion, can make it much easier for us to absorb information and use it appropriately. Other examples include bar charts, pie charts, and scatterplots with their own specific ways to convey the data.

Data sharing

Patients may or may not receive their care at multiple locations. In the former case, sharing data with other healthcare organizations would be essential. During such sharing, if the data is not interoperable then data movement between disparate organizations could be severely curtailed. This could be due to technical and organizational barriers. This may leave clinicians without key information for making decisions regarding follow-ups and treatment strategies for patients. Solutions like Fast Healthcare Interoperability Resource (FHIR) and public APIs, CommonWell (a not-for-profit trade association) and Carequality (a consensus-built, common interoperability framework) are making data interoperability and sharing easy and secure. The biggest roadblock for data sharing is the treatment of data as a commodity that can provide a competitive advantage. Therefore, sometimes both providers and vendors intentionally interfere with the flow of information to block the information flow between different EHR systems [ 31].

The healthcare providers will need to overcome every challenge on this list and more to develop a big data exchange ecosystem that provides trustworthy, timely, and meaningful information by connecting all members of the care continuum. Time, commitment, funding, and communication would be required before these challenges are overcome.

Big data analytics for cutting costs

To develop a healthcare system based on big data that can exchange big data and provides us with trustworthy, timely, and meaningful information, we need to overcome every challenge mentioned above. Overcoming these challenges would require investment in terms of time, funding, and commitment. However, like other technological advances, the success of these ambitious steps would apparently ease the present burdens on healthcare especially in terms of costs. It is believed that the implementation of big data analytics by healthcare organizations might lead to a saving of over 25% in annual costs in the coming years. Better diagnosis and disease predictions by big data analytics can enable cost reduction by decreasing the hospital readmission rate. The healthcare firms do not understand the variables responsible for readmissions well enough. It would be easier for healthcare organizations to improve their protocols for dealing with patients and prevent readmission by determining these relationships well. Big data analytics can also help in optimizing staffing, forecasting operating room demands, streamlining patient care, and improving the pharmaceutical supply chain. All of these factors will lead to an ultimate reduction in the healthcare costs by the organizations.

Quantum mechanics and big data analysis

Big data sets can be staggering in size. Therefore, its analysis remains daunting even with the most powerful modern computers. For most of the analysis, the bottleneck lies in the computer's ability to access its memory and not in the processor [ 32, 33]. The capacity, bandwidth or latency requirements of memory hierarchy outweigh the computational requirements so much that supercomputers are increasingly used for big data analysis [ 34, 35]. An additional solution is the application of quantum approach for big data analysis.

Quantum computing and its advantages

The common digital computing uses binary digits to code for the data whereas quantum computation uses quantum bits or qubits [ 36]. A qubit is a quantum version of the classical binary bits that can represent a zero, a one, or any linear combination of states (called superpositions) of those two qubit states [ 37]. Therefore, qubits allow computer bits to operate in three states compared to two states in the classical computation. This allows quantum computers to work thousands of times faster than regular computers. For example, a conventional analysis of a dataset with n points would require 2n processing units whereas it would require just n quantum bits using a quantum computer. Quantum computers use quantum mechanical phenomena like superposition and quantum entanglement to perform computations [ 38, 39].

Quantum algorithms can speed-up the big data analysis exponentially [ 40]. Some complex problems, believed to be unsolvable using conventional computing, can be solved by quantum approaches. For example, the current encryption techniques such as RSA, public-key (PK) and Data Encryption Standard (DES) which are thought to be impassable now would be irrelevant in future because quantum computers will quickly get through them [ 41]. Quantum approaches can dramatically reduce the information required for big data analysis. For example, quantum theory can maximize the distinguishability between a multilayer network using a minimum number of layers [ 42]. In addition, quantum approaches require a relatively small dataset to obtain a maximally sensitive data analysis compared to the conventional (machine-learning) techniques. Therefore, quantum approaches can drastically reduce the amount of computational power required to analyze big data. Even though, quantum computing is still in its infancy and presents many open challenges, it is being implemented for healthcare data.

Applications in big data analysis

Quantum computing is picking up and seems to be a potential solution for big data analysis. For example, identification of rare events, such as the production of Higgs bosons at the Large Hadron Collider (LHC) can now be performed using quantum approaches [ 43]. At LHC, huge amounts of collision data (1PB/s) is generated that needs to be filtered and analyzed. One such approach, the quantum annealing for ML (QAML) that implements a combination of ML and quantum computing with a programmable quantum annealer, helps reduce human intervention and increase the accuracy of assessing particle-collision data. In another example, the quantum support vector machine was implemented for both training and classification stages to classify new data [ 44]. Such quantum approaches could find applications in many areas of science [ 43]. Indeed, recurrent quantum neural network (RQNN) was implemented to increase signal separability in electroencephalogram (EEG) signals [ 45]. Similarly, quantum annealing was applied to intensity modulated radiotherapy (IMRT) beamlet intensity optimization [ 46]. Similarly, there exist more applications of quantum approaches regarding healthcare e.g. quantum sensors and quantum microscopes [ 47].

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Big data in healthcare: management, analysis and future prospects

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'Big data' is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide.

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I found this article from Datapine to be especially enlightening because it gave real examples of big data in healthcare and how it can be used. Improved patient outcomes, as well as planning for staffing, telemedicine, preventing unnecessary ER visits are just a few of the ways data is analyzed to improve healthcare for patients.

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BI Blog | Data Visualization & Analytics Blog | datapine

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Big data has changed how we manage, analyze, and leverage data across industries. One of the most notable areas where data analysis is making big changes is healthcare.

In fact, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general. The average human lifespan is increasing across the world population, which poses new challenges to today's treatment delivery methods. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and looking for the best strategies to use these numbers.

In this article, we're going to address the need for big data in healthcare and hospital big data: why and how can it help? What are the obstacles to its adoption? We will then look at 24 big data examples in healthcare that already exist and that medical-based institutions can benefit from.

But first, let's examine the core concept and role of big data in healthcare.

Big data in healthcare is a term used to describe massive volumes of information created by the adoption of digital technologies that collect patients' records and help in managing hospital performance; otherwise too large and complex for traditional technologies.

The application of big data analytics in healthcare has a lot of positive and also life-saving outcomes. In essence, big-style data refers to the vast quantities of information created by the digitization of everything that gets consolidated and analyzed by specific technologies. Applied to healthcare, it will use specific health data of a population (or of a particular individual) and potentially help to prevent epidemics, cure diseases, cut down costs, etc.

Now that we live longer, treatment models have changed, and many of these changes are namely driven by data. Doctors want to understand as much as they can about a person and, as early in their life as possible, to pick up warning signs of serious illness as they arise - treating any disease at an early stage is far more simple and less expensive. By utilizing key performance indicators in healthcare and healthcare data analytics, prevention is better than cure, and managing to draw a comprehensive picture of someone will let insurance provide a tailored package. This is the industry's attempt to tackle the siloes problems a patient's data has: everywhere are collected bits and bites of it and archived in hospitals, clinics, surgeries, etc., with the impossibility of communicating properly.

That said, the amount of sources from which health professionals can gain insights from their patients keeps growing. This data is normally coming in different formats and sizes, which presents a challenge to the user. However, the current focus is no longer on how "big" the data is but on how smartly it is managed. With the help of the right technology, data can be extracted from the following sources of the healthcare industry in a smart and fast way:

Patients portals

Research studies

EHRs

Wearable devices

Search engines

Generic databases

Government agencies

Payer records

Staffing schedules

Patient waiting room

Indeed, for years gathering huge amounts of data for medical use has been costly and time-consuming. With today's always-improving technologies, it becomes easier not only to collect such data but also to create comprehensive healthcare reports and convert them into relevant critical insights that can then be used to provide better care. This is the purpose of healthcare data analysis: using data-driven findings to predict and solve a problem before it is too late, but also assess methods and treatments faster, keep better track of inventory, involve patients more in their own health, and empower them with the tools to do so.

Now that you understand the importance of big data in the healthcare industry let's explore 24 real-world applications that demonstrate how an analytical approach can improve processes, enhance patient care, and, ultimately, save lives.

1) Patients Predictions For Improved Staffing

For our first example of big data in healthcare, we will look at one classic problem that any shift manager faces: how many people do I put on staff at any given period? If you put on too many workers, you run the risk of having unnecessary labor costs add up. With too few workers, you can have poor customer service outcomes - which can be fatal for patients in that industry.

Big data is helping to solve this problem, at least at a few hospitals in Paris. A white paper by Intel details how four hospitals that are part of the Assistance Publique-Hôpitaux de Paris have been using data from a variety of sources to come up with daily and hourly predictions of how many patients are expected to be at each facility.

One of the key data sets is 10 years' worth of hospital admissions records, which data scientists crunched using "time series analysis" techniques. These analyses allowed the researchers to see relevant patterns in admission rates. Then, they could use machine learning to find the most accurate algorithms that predicted future admissions trends.

Summing up the product of all this work, the data science team developed a web-based user interface that forecasts patient loads and helps in planning resource allocation by utilizing online data visualization that reaches the goal of improving the overall patients' care.

2) Electronic Health Records (EHRs)

It's the most widespread application of big data in medicine. Every person has their own digital record, which includes demographics, medical history, allergies, laboratory test results, etc. Records are shared via secure information systems and are available for providers from both the public and private sectors. Every record is comprised of one modifiable file, which means that doctors can implement changes over time with no paperwork and no danger of data replication.

EHRs can also trigger warnings and reminders when a patient should get a new lab test or track prescriptions to see if he or she has been following doctors' orders.

Although EHR is a great idea, many countries still struggle to implement them fully. U.S. has made a major leap, with 94% of hospitals adopting EHRs according to this HITECH research, but the EU still lags behind. However, an ambitious directive drafted by the European Commission is supposed to change it.

Kaiser Permanente is leading the way in the U.S. and could provide a model for the EU to follow. They've fully implemented a system called HealthConnect that shares data across all of their facilities and makes it easier to use EHRs. A McKinsey report on big data healthcare analytics states that "The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests."

3) Real-Time Alerting

Other examples of data analytics in healthcare share one crucial functionality - real-time alerting. In hospitals, Clinical Decision Support (CDS) software analyzes medical data on the spot, providing health practitioners with advice as they make prescriptive decisions.

However, doctors want patients to stay away from hospitals to avoid costly in-house treatments. This is already trending as one of the business intelligence buzzwords in 2021 and has the potential to become part of a new strategy. Wearables will collect patients' health data continuously and send this data to the cloud.

Additionally, this information will be accessed to the database on the state of health of the general public, which will allow doctors to compare this data in a socio-economic context and modify the delivery strategies accordingly. Institutions and care managers will use sophisticated tools to monitor this massive data stream and react every time the results will be disturbing.

For example, if a patient's blood pressure increases alarmingly, the system will send a live alert to the doctor, who will then take action to reach the patient and administer measures to lower the pressure.

Another example is that of Asthmapolis, which has started to use inhalers with GPS-enabled trackers in order to identify asthma trends both on an individual level and looking at larger populations. This data is being used in conjunction with data from the CDC in order to develop better treatment plans for asthmatics.

4) Enhancing Patient Engagement

Many consumers - and hence, potential patients - already have an interest in smart devices that record every step they take, their heart rates, sleeping habits, etc., on a permanent basis. All this vital information can be coupled with other trackable data to identify potential health risks lurking. Chronic insomnia and an elevated heart rate can signal a risk for future heart disease, for instance. Patients are directly involved in the monitoring of their own health, and incentives from health insurance can push them to lead a healthy lifestyle (e.g., giving money back to people using smartwatches).

Another way to do so comes with new wearables under development, tracking specific health trends and relaying them to the cloud where physicians can monitor them. Patients suffering from asthma or blood pressure could benefit from it, become a bit more independent and reduce unnecessary visits to the doctor.

5) Prevent Opioid Abuse In The US

Our fifth example of big data healthcare is tackling a serious problem in the US. Here's a sobering fact: as of this year, overdoses from misused opioids have caused more accidental deaths in the U.S. than road accidents, which were previously the most common cause of accidental death.

Analysis expert Bernard Marr writes about the problem in a Forbes article. The situation has gotten so dire that Canada has declared opioid abuse to be a "national health crisis," and President Obama earmarked $1.1 billion dollars for developing solutions to the issue while he was in office.

Once again, an application of big data analytics in healthcare might be the answer everyone is looking for: data scientists at Blue Cross Blue Shield have started working with analytical experts at Fuzzy Logix to tackle the problem. Using years of insurance and pharmacy data, Fuzzy Logix analysts have been able to identify 742 risk factors that predict with a high degree of accuracy whether someone is at risk for abusing opioids.

To be fair, reaching out to people identified as "high risk" and preventing them from developing a drug issue is a delicate undertaking. However, this project still offers a lot of hope for mitigating an issue that is destroying the lives of many people and costing the system a lot of money.

6) Using Health Data For Informed Strategic Planning

The use of big data in healthcare allows for strategic planning thanks to better insights into people's motivations. Care managers can analyze check-up results among people in different demographic groups and identify what factors discourage people from taking up treatment.

The University of Florida made use of Google Maps and free public health data to prepare heat maps targeted at multiple issues, such as population growth and chronic diseases. Subsequently, academics compared this data with the availability of medical services in most heated areas. The insights gleaned from this allowed them to review their delivery strategy and add more care units to the most problematic areas.

7) Big Data Might Just Cure Cancer

Another interesting example of the use of big data in healthcare is the Cancer Moonshot program. Before the end of his second term, President Obama came up with this program that had the goal of accomplishing 10 years' worth of progress toward curing cancer in half that time.

Medical researchers can use large amounts of data on treatment plans and recovery rates of cancer patients in order to find trends and treatments that have the highest rates of success in the real world. For example, researchers can examine tumor samples in biobanks that are linked up with patient treatment records. Using this data, researchers can see things like how certain mutations and cancer proteins interact with different treatments and find trends that will lead to better outcomes.

This data can also lead to unexpected benefits, such as finding that Desipramine, which is an antidepressant, has the ability to help cure certain types of lung cancer.

However, in order to make these kinds of insights more available, patient databases from different institutions, such as hospitals, universities, and nonprofits, need to be linked up. Then, for example, researchers could access patient biopsy reports from other institutions. One of the potential big data use cases in healthcare would be genetically sequencing cancer tissue samples from clinical trial patients and making these data available to the wider cancer database.

But, there are a lot of obstacles in the way, including:

Incompatible data systems. This is perhaps the biggest technical challenge, as making these data sets able to interface with each other is quite a feat.

Patient confidentiality issues. There are differing laws state by state which govern what patient information can be released with or without consent, and all of these would have to be navigated.

Simply put, institutions that have put a lot of resources and money into developing their own cancer dataset may not be eager to share it with others, even though it could lead to a cure much more quickly.

However, as an article by Fast Company states, there are precedents to navigating these types of problems and roadblocks while accelerating progress toward curing cancer using the strength of data analysis.

8) Predictive Analytics In Healthcare

We have already recognized predictive analysis as one of the biggest business intelligence trends for two years in a row, but the potential applications reach far beyond business and much further into the future. Optum Labs, a US research collaborative, has collected EHRs of over 30 million patients to create a database for predictive analysis tools that will improve the delivery of care.

The goal of healthcare online business intelligence is to help doctors make data-driven decisions within seconds and improve patients' treatment. This is particularly useful in the case of patients with complex medical histories suffering from multiple conditions. New BI solutions and tools would also be able to predict, for example, who is at risk of diabetes and thereby be advised to make use of additional screenings or weight management.

9) Reduce Fraud And Enhance Security

Some studies have shown that 93% of healthcare organizations have experienced a data breach. The reason is simple: personal data is extremely valuable and profitable on the black market. And any breach would have dramatic consequences. With that in mind, many organizations started to use analytics to help prevent security threats by identifying changes in network traffic or any other behavior that reflects a cyber-attack. Of course, big data has inherent security issues, and many think that using it will make organizations more vulnerable than they already are. But advances in security, such as encryption technology, firewalls, anti-virus software, etc., answer the need for more security, and the benefits brought largely overtake the risks.

Likewise, it can help prevent fraud and inaccurate claims in a systemic, repeatable way. Analytical tools help to streamline the processing of insurance claims, enabling patients to get better returns on their claims and caregivers to be paid faster. For instance, the Centers for Medicare and Medicaid Services said they saved over $210.7 million in fraud in just a year.

10) Telemedicine

Telemedicine has been present on the market for over 40 years, but only today, with the arrival of online video conferences, smartphones, wireless devices, and wearables, has it been able to come into full bloom. The term refers to the delivery of remote clinical services using technology.

It is used for primary consultations and initial diagnosis, remote patient monitoring, and medical education for health professionals. Some more specific uses include telesurgery - doctors can perform operations with the use of robots and high-speed real-time data delivery without physically being in the same location as a patient.

Clinicians use telemedicine to provide personalized treatment plans and prevent hospitalization or re-admission. Such use of healthcare data analytics can be linked to the use of predictive analytics, as seen previously. It allows clinicians to predict acute medical events in advance and prevent the deterioration of patients' conditions.

By keeping patients away from hospitals, telemedicine helps to reduce costs and improve the quality of service. Patients can avoid waiting in lines, and doctors don't waste time on unnecessary consultations and paperwork. Telemedicine also improves the availability of care as patients' states can be monitored and consulted anywhere and anytime.

11) Integrating Big-Style Data With Medical Imaging

Medical imaging is vital, and each year in the US, about 600 million imaging procedures are performed. Analyzing and storing these images manually is expensive both in terms of time and money, as radiologists need to examine each image individually, while hospitals need to store them for several years.

Medical imaging provider Carestream explains how big data analytics for healthcare could change how images are read: algorithms developed analyzing hundreds of thousands of images could identify specific patterns in the pixels and convert them into a number to help the physician with the diagnosis. They even go further, saying that it could be possible that radiologists will no longer need to look at the images but instead analyze the outcomes of the algorithms that will inevitably study and remember more images than they could in a lifetime. This would undoubtedly impact the role of radiologists, their education, and the required skillset.

12) A Way To Prevent Unnecessary ER Visits

Saving time, money, and energy using big data analytics for healthcare is necessary. What if we told you that over the course of 3 years, one woman visited the ER on more than 900 occasions? That situation is a reality in Oakland, California, where a woman who suffers from mental illness and substance abuse went to a variety of local hospitals on an almost daily basis.

This woman's issues were exacerbated by the lack of shared medical records between local emergency rooms, increasing the cost to taxpayers and hospitals and making it harder for this woman to get good care. As Tracy Schrider, who coordinates the care management program at Alta Bates Summit Medical Center in Oakland, stated in a Kaiser Health News article:
"Everybody meant well. But she was being referred to three different substance abuse clinics and two different mental health clinics, and she had two case management workers both working on housing. It was not only bad for the patient, it was also a waste of precious resources for both hospitals."
In order to prevent future situations like this from happening, Alameda County hospitals came together to create a program called PreManage ED, which shares patient records between emergency departments.

This system lets the ER staff know things like:

If the patient they are treating has already had certain tests done at other hospitals, what the results of those tests are.

If the patient in question already has a case manager at another hospital, prevent unnecessary assignments.

What advice has already been given to the patient so that a coherent message to the patient can be maintained by providers?

This is another great example where the application of healthcare data analysis is useful and needed. In the past, hospitals without PreManage ED would repeat tests over and over, and even if they could see that a test had been done at another hospital, they would have to go old school and request or send long fax just to get the information they needed.

13) Smart Staffing & Personnel Management

Without a cohesive, engaged workforce, patient care will dwindle, service rates will drop, and mistakes will happen. But with big data tools in healthcare, it's possible to streamline your staff administration activities in a wealth of key areas. By working with the right HR analytics, it's possible for time-stretched medical institutions to optimize staffing while forecasting operating room demands, streamlining patient care as a result.

Too often, there is a significant lack of fluidity in healthcare institutions, with staff distributed in the wrong areas at the wrong time. This imbalance of personnel administration could mean a particular department is either too overcrowded with staff or lacking staff when it matters most, which can develop risks of lower motivation for work and increases the absenteeism rate. An HR dashboard, in this case, may help:

**click to enlarge**
Through data-driven analytics, it's possible to predict when you might need staff in particular departments at peak times while distributing skilled personnel to other areas within the institution during quieter periods.

Moreover, medical data analysis will empower senior staff or operatives to offer the right level of support when needed, improve strategic planning, and make vital staff and personnel management processes as efficient as possible.

14) Learning & Development

Expanding on our previous point, in a hospital or medical institution, the skills, confidence, and abilities of your staff can mean the difference between life and death. Naturally, doctors and surgeons are highly skilled in their areas of expertise. But most medical institutions have a range of people working under one roof, from porters and admin clerks to cardiac specialists and brain surgeons.

In healthcare, soft skills are almost important as certifications. To keep the institution running at optimum capacity, you have to encourage continual learning and development. By keeping track of employee performance across the board while keeping a note of training data, you can use healthcare data analysis to gain insight into who needs support or training and when. If everyone is able to evolve with the changes around them, you will save more lives - and medical data analytics will help you do just that.

15) Advanced Risk & Disease Control

Big data and healthcare analytics are essential for tackling the hospitalization risk for specific patients with chronic diseases. It can also help prevent deterioration.

By drilling down into insights such as medication type, symptoms, and the frequency of medical visits, among many others, it's possible for healthcare institutions to provide accurate preventative care and, ultimately, reduce hospital admissions. Not only will this level of risk calculation result in reduced spending on in-house patient care, but it will also ensure that space and resources are available for those who need it most. This is a clearcut example of how analytics in healthcare can improve and save people's lives.

As a result, big data for healthcare can improve the quality of patient care while making the organization more economically streamlined in every key area.

16) Suicide & Self-Harm Prevention

Globally, almost 800,000 people die from suicide every year. Plus, 17% of the world's population will self-harm during their lifetime. These numbers are alarming. But while this is a very difficult area to tackle, big data uses in healthcare are helping to make a positive change concerning suicide and self-harm. As entities that see a wealth of patients every single day, healthcare institutions can use data analysis to identify individuals that might be likely to harm themselves.

In a 2018 study from KP and the Mental Health Research Network, a mix of EHR data and a standard depression questionnaire identified individuals who had an enhanced risk of a suicide attempt with great accuracy. Utilizing a predictions algorithm, the team found that suicide attempts and successes were 200 times more likely among the top 1% of patients flagged according to specific datasets. Speaking on the subject, Gregory E. Simon, MD, MPH, a senior investigator at Kaiser Permanente Washington Health Research Institute, explained:
"We demonstrated that we can use electronic health record data in combination with other tools to accurately identify people at high risk for suicide attempt or suicide death."
This essential use case for big data in the healthcare industry really is a testament to the fact that medical analytics can save lives.
"If somebody tortures the data enough (open or not), it will confess anything." -Paolo Magrassi, former vice president, research director, Gartner.
17) Improved Supply Chain Management

If a medical institution's supply chain is weakened or fragmented, everything else is likely to suffer, from patient care and treatment to long-term finances and beyond. That said, the next in our big data in healthcare examples focuses on the value of analytics to keep the supply chain fluent and efficient from end to end.

Leveraging analytical tools to track the supply chain performance metrics and make accurate, data-driven decisions concerning operations as well as spending can save hospitals up to $10 million per year.

Both descriptive and predictive models can enhance decisions for negotiating prices, reducing the variation in supplies, and optimizing the ordering process as a whole. By doing so, medical institutions can thrive in the long term while delivering vital treatment to patients without potentially disastrous delays, snags, or bottlenecks.

18) Financial facility management

As you've learned by now, data plays a crucial role in today's society. It has permeated industries across the world to assist in the development of new technologies and innovations. This is especially true in the business landscape, where data-driven initiatives have become a requirement rather than a choice. This is no different in the healthcare industry, which leads us to our next big data application.

So far, we've covered multiple applications related to patients and medical innovation. That said, all of these breakthroughs would not be possible if the finances of the different health organizations wouldn't be on check. Big data in healthcare provides the necessary tools and knowledge to ensure optimal financial health across facilities, especially for big hospital networks that manage multiple facilities simultaneously.

When we talk about the financial side of the healthcare industry, we'll encounter similar KPIs as any other organization, such as net profit, cash flow, etc. However, there are some financial indicators that are unique to this sector. We are talking about drug costs, claims denial rates, reimbursement rates, and much more. Keeping track of all those indicators is not an easy task; however, organizations that invest in software to collect, arrange and analyze their data will ensure their finances are in check and the facility remains profitable while providing the best care.

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The example below is a hospital network dashboard generated with datapine's dashboard designer. The template provides a strategic overview of a private hospital network with insights into multiple facilities. With these insights in hand, decision-makers can make informed decisions and spot improvement opportunities to boost their financial efficiency.

19) Developing New Therapies & Innovations

The next in our healthcare analytics examples centers on working for a brighter, bolder future in the medical industry. Big data analysis in healthcare has the power to assist in new therapy and innovative drug discoveries. By utilizing a mix of historical, real-time, and predictive metrics as well as a cohesive mix of data visualization techniques, healthcare experts can identify potential strengths and weaknesses in trials or processes.

Moreover, through data-driven genetic information analysis as well as reactionary predictions in patients, big data analytics in healthcare can play a pivotal role in the development of groundbreaking new drugs and forward-thinking therapies. Data analytics in healthcare can streamline, innovate, provide security, and save lives. It gives confidence and clarity, and it is the way forward.

20) Help To Manage And Track Mass Diseases

Since it started at the beginning of the year 2020, the COVID-19 pandemic has impacted millions and millions of people around the world. The widespread nature of this virus presented a challenge to the health industry, which found itself trying to learn from it and control it at the same time. In that context, big data played a fundamental role in the response given to this growing disease that made the world stop for years.

Supported by advanced data management technologies, health experts were able to track in real-time how COVID was spreading, how fast it mutated under different conditions, as well as the effect it was having on the different world economies. This is done by analyzing massive data sets coming from diverse sources, such as medical records as well as individual human behaviors. For instance, looking at how many of them are staying at home, getting on the train, or going to school, as this highly influences how fast the virus spreads.

Paired to this, technologies such as AI allowed various medical imaging modalities, such as X-rays, tomography, ultrasounds, and others, to provide an earlier diagnosis to patients and prevent the spread of the disease. In fact, in 2020, the EU backed a software called InferRead to facilitate this process. Essentially, this AI-powered software "analyses images of the lungs taken by a CT scanner, identifies the signs of coronavirus, and assesses the lesions. The process would typically require a careful study by an experienced doctor, yet the machine needs only a few seconds". This level of technology allowed hospitals around Europe to prevent the spread of the virus and, consequently, flatten the curve.

21) Improved Drug Prescription Processes

As seen throughout our list of examples, the vast amount of data that care professionals have at their fingertips has helped revolutionize patient care, disease control, and many others. That said, it is not a surprise that this trend has also spread into the pharmaceutical industry, where it promises to maximize the value of medicines and improve the quality of prescribing. This leads us to our next big data application in healthcare.

One of the companies applying it is Express Scripts, a third-party organization that manages medicines coverage for clients who provide health insurance plans. According to a publication from the Pharmaceutical Journal, the US-based company collected data from 83 million patients, which included everything from patients' clinical and behavioral characteristics, such as someone picking up a prescription, to Tweets directed at the company. With this data in hand, Express Scripts can predict important scenarios, such as who is likely to become addicted to a certain medication or who is not adhering to its treatment. If the last happens, the company immediately sends personalized interventions and support to make sure people adhere to their prescribed treatment. As a result, Express Scripts stated that they cut the non-adherence rate of hepatitis C patients from 8,3% to 4,8%.

22) Prevent Human Error

Fraud in the health industry can be considered anything from erroneous billings to inefficiencies that result in wasteful tests or even adding incorrect information to a person's medical record. In fact, the National Health Care Anti-Fraud Association estimates that the financial losses due to healthcare fraud could reach $300 billion in the US alone. This means that around 10% of the total health spending is wasted due to fraud or human error. However, money is not even the biggest concern when it comes to human error and fraud, it's patients' lives that are put at risk. Prescribing someone with the wrong medicine or treatment can result in long-lasting consequences or even death.

To avoid this, companies leverage big data and forecasting to identify and prevent fraud or human error quickly. For example, by analyzing massive amounts of prescription patterns, experts are able to detect prescription errors before they occur. The same happens with dosage ranges, tests, and other procedures. In time, this not only allows doctors and caregivers to trust in technology for their decision-making but also saves facilities a big amount of money while providing the best care.

23) Alerting heart problems through personal devices

In 2017, Apple and Stanford Medicine joined forces to carry out one-of-a-kind research called "Apple Heart Study." They studied the heart rate sensor data of 400.000 Apple Watch users to see if wearable devices could help in detecting irregular heart rates to prevent fatal complications, especially atrial fibrillation (AFib).

AFib is among the leading causes of strokes across the world. Costing the life of approximately 130.000 people in the US every year, with another 750.000 hospitalizations. That's because patients that are suffering from AFib often don't experience any symptoms, so the condition can go unnoticed until it is fatal.

According to Jeff Williams, Apple's COO, the motivation to carry out this study was their own customer's testimonies. At the time, he said: "Every week, we receive incredible customer letters about how Apple Watch has affected their lives, including learning that they have AFib. These stories inspire us, and we're determined to do more to help people understand their health"

Apple and Standford Medicine created an app where any Apple Watch users in the US that were over the age of 22 could join the study. If an irregular heart rate were identified, the user would get a notification and a free doctor consultation with an electrocardiogram (ECG) patch for additional monitoring. After 8 months of testing using top-notch technology, in 2019, the results of the study were published.

Stanford Medicine found that only 0.5% of participants had irregular heart rate notifications, a positive result overall. Of the participants that did receive the notification, 84% were diagnosed with atrial fibrillation at the time of the notification. 34% of the participants that received a notification and followed up with an ECG patch were finally diagnosed with AFib. This last result was not surprising, as AFib can often go undetected.

Lastly, the study showed that comparisons between Apple Watch pulse detection and ECG patches pulse detection have a 71% positive predictive value. Showing that these devices can help people take charge of their health and, in some cases, save lives. A great testament to the power of big data in healthcare.

24) Bluetooth helps asthma patients

According to the WHO, Asthma affected an estimated 262 million people in 2019 and caused 455 000 deaths globally. People who suffer from asthma can be triggered even by invisible particles in the air, making it a complicated disease to carry.

With those concerning numbers in mind, a Wisconsin-based digital health company called Propeller Health launched a Bluetooth-powered inhaler that can be connected to a person's smartphone to collect data about the frequency of asthma attacks and the environmental conditions in which the person was located.

Once the inhaler is pressed, data about the time and location of the patient's asthma attack is sent to an app on the phone via a Bluetooth sensor. Doctors can use this data later on to analyze the frequency of the attacks as well as any other factors such as location and weather. Plus, the inhaler is also set to help patients with their medication schedules since taking medication correctly and when expected can significantly decrease the chances of ending up in the emergency room for an attack.

In fact, research carried out by Propeller Health showed that 330 patients who had attached the Bluetooth sensor to their inhaler experienced 60% fewer asthma-related emergency hospital visits. The company also found that collecting data like this for patients that live in the same town or region can prove even more valuable. Another test of their Bluetooth inhaler sensor showed that in Louisville, Kentucky, asthma attacks were mostly caused by proximity to railroads and utilities. Plus, they found multiple asthma triggers in public areas. The data collected by the sensor is highly valuable to the city as health authorities can use it to prevent asthma from becoming a major issue for their population.

All in all, we've noticed three key trends through these 24 examples of healthcare analytics: the patient experience will improve dramatically, including quality of treatment and satisfaction levels; the overall health of the population can also be enhanced on a sustainable basis, and operational costs can be reduced significantly.

Let's have a look now at concrete examples of big data in healthcare:

a) Big Data In Healthcare Applied On A Hospital Dashboard

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This healthcare dashboard below provides you with the overview needed as a hospital director or as a facility manager. Gathering in one central point all the data on every division of the hospital, the attendance, its nature, the costs incurred, etc., you have the big picture of your facility, which will be of great help to run it smoothly.

You can see here the most important metrics concerning various aspects: the number of patients that were welcomed in your facility, how long they stayed and where, how much it cost to treat them, and the average waiting time in emergency rooms. Such a holistic view helps top administrators to identify potential bottlenecks, spot trends and patterns over time, and in general, assess the situation. This is key in order to make better-informed decisions that will improve the overall operations performance, with the goal of treating patients better and having the right staffing resources.

b) Big Data Healthcare Application On Patients' Care

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Another real-world application of healthcare big data analytics, our dynamic patient KPI dashboard, is a visually-balanced tool designed to enhance service levels as well as treatment accuracy across departments.

By offering a perfect storm or patience-centric information in one central location, medical institutions can create harmony between departments while streamlining care processes in a wealth of vital areas. For instance, bed occupancy rate metrics offer a window of insight into where resources might be required while tracking canceled or missed appointments will give senior executives the data they need to reduce costly patient no-shows.

Here, you will find everything you need to enhance your level of patient care both in real-time and in the long term. This is a visual innovation that has the power to improve every type of medical institution, big or small.

By looking at our list of most insightful medical big data applications, you should have a notion of how positive the use of analytics can be for this industry. If this is not clear yet, here we will summarize the main points of importance by listing a few benefits of big data in healthcare.
Reducing costs
As mentioned, there's a huge need for big data in healthcare, especially due to rising costs in nations like the United States. As a McKinsey report states: "After more than 20 years of steady increases, healthcare expenses now represent 17.6 percent of GDP - nearly $600 billion more than the expected benchmark for a nation of the United States's size and wealth." This quote leads us to our first benefit.

As stated above, costs are much higher than they should be, and they have been rising for the past 20 years. Clearly, we are in need of some smart, data-driven thinking in this area. And current incentives are changing as well: many insurance companies are switching from fee-for-service plans (which reward using expensive and sometimes unnecessary treatments and treating large amounts of patients quickly) to plans that prioritize patient outcomes

As the authors of the popular Freakonomics books have argued, financial incentives matter - and incentives that prioritize patients' health over treating large amounts of patients are a good thing. Why does this matter?
Reducing medical errors
Well, in the previous scheme, healthcare providers had no direct incentive to share patient information with one another, which made it harder to utilize the power of analytics. Now that more of them are getting paid based on patient outcomes, they have a financial incentive to share data that can be used to improve the lives of patients while cutting costs for insurance companies.

Physician decisions are becoming more and more evidence-based, meaning that they rely on large swathes of research and clinical data as opposed to solely their schooling and professional opinion. That said, the risk of human error is always a latent threat. Even though doctors are highly trained professionals, they are still human, and the risk of selecting the wrong medication or treatment can potentially risk a person's life. With the use of big data and the tools that we mentioned throughout this post, professionals can be easily alerted when the wrong medication, test, treatment, or other has been provided and remediate it immediately. In time, this can significantly reduce the rates of medical errors and improve the facility's reputation.
Optimizing organizational and personnel management
As in many other industries, data gathering and management are getting bigger, and professionals need help in the matter. This new treatment attitude means there is a greater demand for big data analytics in healthcare facilities than ever before, and the rise of SaaS BI tools is also answering that need.

While using data to ensure you are providing the best care to patients is fundamental, there are also other operational areas in which it can assist the health industry. Part of providing quality care is ensuring the facility works optimally, and this can also be achieved with the help of big data.

By using the right BI software, professionals can gather and analyze real-time data about the performance of their organization in areas such as operations and finances, as well as personnel management. For instance, predictive analytics technologies can provide relevant information regarding admission rates. These insights can help define staffing schedules to cover demand as well as inventory for medical supplies. This way, care facilities can stay one step ahead and ensure that patients are getting the best experience possible.
Drive innovation and growth
Getting this level of insight in such an intuitive way allows managers to redirect resources where they are most needed and optimize areas that are not performing well to ensure the best return on investment possible.

Our last benefit is one that should be the clearest from the list of applications we provided earlier. The use of big data in the care industry enables professionals to test new technologies, drugs, and treatments to improve the quality of care given to patients and battle diseases that were once thought of as unbeatable.

Thanks to wearable devices that can tell your heart rate, Bluetooth asthma inhalers that gather insights to prevent attacks, and much more, doctors are able to use data to understand how common diseases work and how certain external factors might be affecting entire communities. Through that, they are able to provide personalized quality care to each and every person that goes into a hospital.

There is no denying that the power of big data analytics is saving lives. That being said, the process of managing data requires a lot of effort, and with that comes challenges, which we will discuss below.

The points mentioned above are just a few of the countless benefits of big data in healthcare. That said, with benefits also come limitations. In order to provide a full picture of the topic, we will now list a few obstacles and healthcare data challenges that organizations can face when implementing analytics into their processes.

Data integration and storage: One of the biggest hurdles standing in the way of using big data in medicine is how medical data is spread across many sources governed by different states, hospitals, and administrative departments. The integration of these data sources would require developing a new infrastructure where all data providers collaborate with each other.

Data sharing: Equally important is implementing new online reporting software and business intelligence strategy that will allow all relevant users to be connected with the data. Healthcare needs to catch up with other industries that have already moved from standard regression-based methods to more future-oriented ones like predictive analytics, machine learning, and graph analytics. This is done with the help of modern reporting tools such as a dashboard creator that allows anyone to perform advanced analytics with just a few clicks easily.

Security and privacy: Security and privacy are constant concerns are one of the biggest challenges of big data in healthcare. Daily, hospitals and care centers deal with sensitive patient data that needs to be carefully protected. Taking that into consideration with the fact that the data is coming from many different sources, security can present a challenge for these types of organizations. To avoid this, it is critical to follow law regulations, conduct regular audits to ensure everything is going well, and train employees on data protection best practices.

Data literacy: Using big data and analytics in healthcare involves many processes and tools to collect, clean, process, manage, and analyze the huge amounts of data available. This requires a level of knowledge and skills that can present a limitation for average users that are not acquainted with these processes. However, while data literacy might have been one of the big disadvantages of big data in healthcare, it is no longer the case. In the past years, various self service BI tools have been developed with the purpose of opening the analytical doors to any type of use without the need for any technical knowledge.

So, even if these analytical services are not your cup of tea, you are a potential patient, and so you should care about new healthcare analytics applications. Besides, it's good to take a look around sometimes and see how other industries cope with it. They can inspire you to adapt and adopt some good ideas.

24 Big Data Examples In Healthcare - A Summary

Predict the daily patients' income to tailor staffing accordingly

Use Electronic Health Records (EHRs)

Use real-time alerting for instant care

Help in preventing opioid abuse in the US

Enhance patient engagement in their own health

Use health data for a better-informed strategic planning

Research more extensively to cure cancer

Use predictive analytics

Reduce fraud and enhance data security

Practice telemedicine

Integrate medical imaging for a broader diagnosis

Prevent unnecessary ER visits

Smart staffing & personnel management

Learning & development

Advanced risk & disease management

Suicide & self-harm prevention

Improved supply chain management

Financial facility management

Developing new therapies & innovations

Track and control mass diseases

Improve the prescription process

Mitigate human error

Apple can alert people about heart problems

Bluetooth helps asthma patients

"Most of the world will make decisions by either guessing or using their gut. They will be either lucky or wrong." - Suhail Doshi, chief executive officer, Mixpanel.
The industry is changing, and like any other, big-style data is starting to transform it - but there is still a lot of work to be done. The sector slowly adopts the new technologies that will push it into the future, helping it to make better-informed decisions, improve operations, etc. In a nutshell, here's a short list of the examples we have gone over in this article. With healthcare data analytics, you can:

These 24 real-world examples of data analytics in healthcare prove that medical applications can save lives and should be a top priority of experts across the field. Even now, data-driven analytics facilitates early identification as well as intervention in illnesses while streamlining institutions for swifter, safer, and more accurate patient care. As technology evolves, these invaluable functions can only get stronger - the future of healthcare is here, and it lies in data.

Want to take your healthcare institution to the next level? Start building your own analysis and reports, and improve your healthcare data management with datapine's 14-day free trial!

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24 Real Life Examples of Big Data In Healthcare Analytics

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Big data in healthcare saves lives, but also money and time. See 24 real-world applications & examples of big data analytics in healthcare here!

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This article discusses the many ways data can be visualized and used to improve healthcare. Interactive apps for patients are just one helpful way data can be visualized in real time. Dashboards gather all of the data in one place so that it can be used for clinical decision making. Data visualization is what makes the loads of data useful, and this article covers the many ways that can be done.

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Healthcare Data Visualization: Examples & Key Benefits

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Healthcare data visualization is a must-have for modern hospitals. Learn how visualization tools improve care, reduce errors, and enhance patient engagement.

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This article and video covers the five W's for data information visualization. Who, What, When, Where and Why.
The video shows real time use of the patient database and how it works. It is very informative, however the voice-over needs to be improved, as it's very robotic.

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The Five W’s for Information Visualization with Application to Healthcare Informatics

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Visual Analytics and Imaging Laboratory (VAI Lab) Computer Science Department, Stony Brook University, NY

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This article covers many of the legal and ethical issues that we face as healthcare professionals. Malpractice is the biggest issue that this article points out, followed by privacy concerns and confidentiality. Other issues mentioned are our oath and ethical concerns.

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As President Barack Obama stresses health care reform, one issue that arises as of 2010 is how health care facilities will gain the revenue they need to operate. The United States, unlike most other industrialized nations, does not have national health care and therefore funds medical assistance in multiple ways.

Government Aid

The government provides some revenue for the health care industry through subsidies and grants. The government also provides the revenue for health care programs like Medicare and Medicaid. American citizens usually provide this aid indirectly through taxes or other wage deductions, but the government also borrows some funds as part of its annual budget. Both the federal and state governments generate health care revenue this way. Legislators are debating whether raising taxes is an efficient solution to getting health care to everyone, since higher taxes decrease how much Americans have to spend on other essentials.

Out of Pocket Direct Payments

Health care patients generate the majority of revenue for the industry, according to the HealthPAC Online website. Patients put money into health care when they pay out of pocket for medical services -- that is, when they cover the cost of their care without the help of a third party. The amount of revenue that a facility gets from patient payments depends on the rates the facility sets. Much of the health care reform movement has focused on trying to reduce the percentage of health care revenue that comes from patient's pockets.

Premiums

Insurance companies that provide health care insurance operate on the principle that you'll be well more than you'll be sick and that they'll earn far more in premiums than they end up paying out. The money insurance companies pay to health care providers thus comes largely from the premiums that all policy holders give the companies.

Private Donations

Sometimes medical facilities receive donations from corporations or individuals. These individuals and corporations either believe strongly in the services the facility provides or they are seeking a tax deduction for their contribution. Typically, getting this type of funding does not happen by chance. Instead, hospital administrators usually lobby for it within the community. This is a very unstable and unpredictable source of revenue, as health care providers cannot guarantee that the same amount of donations will come in from year to year. However, administrators can make some predictions about donations based on previous records.

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What Are the Primary Sources of Operating Revenue in the Health Care Industry?

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As President Barack Obama stresses health care reform, one issue that arises as of 2010 is how health care facilities will gain the revenue they need to operate. The United States, unlike most other industrialized nations, does not have national health care and therefore funds medical assistance in multiple ways.

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This article from Health Informatics Journal discusses the legal and ethical issues of crowdsourcing apps for providers. While healthcare requires that we provide privacy to our patients, healthcare is also an area where providers need to collaborate. Crowdsourcing offers that opportunity, however the ethical and privacy issues that come with it can make it difficult to know if it's the best course of action.