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As future nurse practitioners, we must seek every opportunity to provide quality, safe patient care. Life gets in the way, but staying current with or always revoloving technology with computers can assist our patients with much-needed help. Telecommunications are crucial in this day and age and will continue to be convenient for the providers and patients in communicating and assessing health concerns.

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0:00 / 4:53•Watch full video

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Health Informatics aids in communicating with the clinician to clinician or provider to provider on assisting practical continuity and safe care to patients. Its important use for the development of IT is for research, evidence-based practice for diagnosis, labs/medications. This article speaks about aiding patients with an effective team approach.

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You don't have to become a doctor, nurse or allied health professional to work in medicine or save lives. Many administrative and non-clinical roles in health care dramatically impact the quality of care, access to care, and even patient outcomes. Health informatics is one of them. This broad, wide-reaching health care discipline is focused primarily on the many applications of data in medicine and medical research. In other words, informaticists (or informaticians) are the data scientists, systems analysts, IT specialists, and database engineers of health care.

What they do, in brief, is act as a bridge between people and the vast quantity of data generated in patient care, medical management and research. They ensure providers and other stakeholders can access the information they need to make evidence-based decisions in both hospital rooms and boardrooms. They also transform data into actionable insights that can streamline facility operations, reduce costs associated with care, make medical devices or drugs safer, or guide the development of one-of-a-kind specialized treatment plans.

Some are drawn to this discipline by a desire to help people. Others want to join the ranks of leadership in medicine but don't relish the idea of going to medical school. Still, others look at data analysis challenges in clinical settings and see puzzles they want to solve. Students in Master of Science in Health Informatics (MSHI) programs come from many different backgrounds. What they have in common is they are ready to make their mark in health care with data.

What inspires your interest in informatics can be as important as the steps you take to prepare to enter and excel in this field because there are numerous professional pathways in health informatics. The University of Pittsburgh School of Health and Rehabilitation Sciences' online Master of Science in Health Informatics program offers four tracks plus four certificate options designed to support a wide range of informatics careers. Figuring out which path is right for you before you invest in an MSHI is a matter of learning as much as you can about health informatics as a discipline and the academic and professional options before you.

Who uses health informatics?

Answering this question requires asking another: What is health informatics? According to the U.S. National Library of Medicine, health informatics is the "interdisciplinary study of the design, development, adoption and application of IT-based innovations in health care." It encompasses the many uses of technology, information systems and data in medicine, medical management and research, and its applications extend well beyond electronic health records keeping.

People might associate informatics with EMR systems, but clinical data science, public health informatics and bioinformatics are all part of this discipline. Medical practices, hospitals, allied health networks and insurance companies rely on informatics professionals, but so do research laboratories, consumer health agencies and public health organizations.

Informaticists in different settings may do very different work, however. As clinicians, researchers and administrators have identified needs, subdomains of informatics have emerged in health care and medical science.

Who typically gets a master's in health informatics?

There is no typical MSHI student because people come to this discipline via IT, medical management, data analytics and patient care. A representative master's in health informatics cohort might include business people, IT workers, and clinicians like doctors, nurses and allied health providers.

Informaticists in analytics roles capture, analyze and communicate data to support patient care or medical and public health research. Analytics roles in health informatics include:

Health data scientist

Health informatics data analyst

Clinical informatics analyst

Clinical informatics specialist

Health informatics business analyst

Medical research analyst

Technology roles in informatics typically involve designing, configuring or managing the physical infrastructure and software systems that collect, organize and analyze health care data. Tech roles in health informatics include:

Health informatics consultant

Information systems analyst

EHR implementation specialist

Health care information technician

Health IT project manager

Product manager

Privacy officer

Health information technician

Informaticists in leadership roles oversee it all. Their job is critical given how complex supporting clinical and health care business decision-making with data can be. Informatics leaders often serve as translators, bridging the gap between the technical teams who work with information and the medical or research teams who use it. Leadership roles in health informatics include:

Director of health informatics

Director of research informatics

Medical director of informatics quality and process improvement

Clinical informatics coordinator

Clinical informatics director

Vice president of health informatics

Chief medical information officer

Is health informatics a good career choice?

Modern informatics has been a part of medicine since the 1950s. Professional groups in fields like medicine, epidemiology, and bioengineering sought to standardize and secure electronic patient records and clinical information systems. The technologies used in health care data reporting have changed significantly since then, however, and so has the focus of informatics. There are now standardized data exchange protocols that let different health care organizations share essential patient information, research and statistics securely and efficiently. New challenges in health care informatics revolve around the amount of data generated by the health care industry—thousands of exabytes each year—and finding ways to leverage that data.

Today, health informaticists have an essential role to play in making medicine better, whether they work for hospitals and medical practices, allied health networks, insurance companies or public health organizations. Big Data in medicine is used to lower the cost of care and make care more efficient and improve patient outcomes by reducing medical errors and helping health care providers customize treatments. Health informatics professionals might spend their days working on projects related to systems integration, regulatory compliance and data analysis, but what they do has a positive impact on patient health and public health.

Informatics is a smart career choice for anyone driven to do transformative work that makes a difference in people's lives. It's also a good option for those who value job security. Demand for health informatics professionals is already high and will likely grow over time because the volume of data generated by patient care, research and health finance is exploding. Earning an online MS in Health Informatics is the best way to take advantage of the explosive growth in this field. Apply now, and it won't be long before you have the knowledge and qualifications you'll need to advance in medical information leadership, technology and analysis.

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What Is Health Informatics? [MSHI Degree Explained]

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Health informaticists have an essential role, whether they work for hospitals and medical practices, allied health networks, insurance companies or public health organizations.

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This article speaks about aiding nurses in preventing the phrase Human error. One is proper documentation, using the barcodes to scan in the system, and pharmacists able to view documentation and perform audits to help in education increase patient safety and aid nurses in preventing errors. If nurses use the tools correctly, this can help in decreasing medication errors in patients.

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By Racquel Williamson, RN, BScN, MSN-INF

Abstract

Background

Safety in nursing reduces blame in the phrase, "to err is human" (Bates and Singh, 2018, p.1736). Adopting introspective analysis to expand on fixed patterns of feedback mitigates adverse events. This type of performance-based analysis in nursing reduces harm and enhanced knowledge for sustainable and effective solutions in maintaining quality nursing care. Maximizing clinical decision-supports prepares nurses to draw on analytics in interpreting nursing practice standards for patient safety. Globally, Patient Safety Organizations (PSO's) have to minimize preventable harms and explore virtual platforms by using technology strategically to effectively engage nurses (Gallagher, Liske, and Widener, 2018). One way to mitigate medication and documentation structures analytically as an advanced practice nurse is to harness confluence analysis and accessibility factors. Currently, the burdens of medication errors to the public are multiplying. The cause of medication errors arises from poorly used health products and from poorly used systems. Evaluating systems and practices of care delivery with technology strengthens data, it strengthens processes, and it saves time evidenced by cost (World Health Organization, 2018a).

Objective

To integrate an e-booklet to show nurses how to proactively analyze and synthesize pharmacy audits collaboratively translates to augmented reciprocity in patient safety initiatives that are recommended by the World Health Organization (2018a).

The Purpose

Evaluating how nursing utilize medication and documentation practices while engaging with clinical decision-supports associates technology informatics guiding education reform (TIGER) initiatives in practice.

The Scope

When e-communication dash boards are maximized to debrief nurses on medication documentation safety, the outcome facilities tangible analysis in medication and documentation practices to meet health safety strategies

The Conjecture

If clinical decision-support dash boards are used efficiently to debrief nurses, will nurses cohesively interconnect medication and documentation practice standards? This integrated evaluation supports technology informatics guiding education reform (TIGER) initiatives to maximize technology and informatics into practice to augment patient safety (Healthcare Information and Management Systems Society (HIMSS), 2018).

Method

The evaluation utilized pharmacy audits along with reviewing the strength, opportunities, weakness, and threat (S.W.O.T) analysis of nurses to drive the gap analysis. This review was performed in an organization in Ontario, Canada that facilitated the implementation of a revised Clinical-Decision Support Database. The target audience were a mixed ratio of Registered Nurses and Registered Practical Nurses. The evaluation was conducted over a twelve-week time frame.

Extensive and randomized daily observation of the clinical data was reviewed anonymously to audit order sets: transcription, and documentation processes in real time. This information was aggregated. Then, an e-booklet was uploaded in the clinical communication dash board summarizing the evaluation. The e-booklet provided a comprehensive analysis of pharmacy audits while drawing on precedence and current nursing regulations. The analysis captured the S.W.O.T questionnaire survey, and inexpensively reviewed literature to facilitate pharmacy audits through a research perspective.

Results

This research facilitates better use of communication dash boards that maximizes accessible support systems to close the loop when translating patient safety data as an actionable quality improvement strategy. The effective use of clinical dash boards with nursing input is a consequential imperative when debriefing nurses on audits. Specifically, clinical audits in medication and documentation practice standards have to use similar principles by not only delivering feedback but including nursing analysis in the delivery of feedback data. Consolidating audits that maximize nursing inclusion factors along with maximizing clinical decision-supports strengthens knowledge translation, structures process analysis, and it is substantive in understanding paradigms of inquiry within disciplines to strengthen practice standards. Utilizing products and tools that are relevant and that are easily accessible fosters actionable sharing of information (World Health Organization, 2018a). The results show that nurses in this organization were at a ninety percent compliance relative to, the seventy percent compliance rate before the project was implemented. Real-time reports showed that within twenty-four hours nurses connected the Medication Administration Record (MAR) and used co-related progress notes ninety percent of the time with descriptive detailed assessment statements, after the e-booklet was embedded and read.

Keywords: Medication safety, clinical decision-supports, nursing documentation, nursing progress notes, analyze audits, proactively debriefs, nursing practice standards, (TIGER) initiatives.

Background

Patient Safety Organizations (PSO's) have to minimize preventable harms with medication safety plans. Currently, the burdens of medication errors to the public are multiplying. An infamous 1984 Harvard Medical Practice Study (Brennan and Leape, 1991) was not the first study to investigate adverse drug events, but its findings were the impetus to standardize measures on this occurrence based on policy reform (World Health Organization, 2016).

The Canadian Medical Association and the Canadian Nurses Association's principles on improving population health stipulated that more adaptable technologies for optimal management of care integrated delivery systems transforms governance. The Royal College of Physicians and Surgeons of Canada also released policy statements on integrated interdisciplinary collaboration as a key component in health organizations. More effective adaptation of health information technologies at point-of-care as a target plan, meets the needs of Canadians (Canadian Medical Association & Canadian Nurses Association, 2011).

Medication error incidents are linked to Prescriber error with inappropriate starting dose, dispensing errors, medication preparation errors, and monitoring systems errors. The cause of medication errors arises from poorly designed health products and can be amended by utilizing timely reciprocity systems. Adverse events reporting, and learning systems were developed by the World Alliance for Patient Safety in 2005 to help expand medication safety, facilitating changes at the local levels to mediate medication safety with triggering tools. Bringing awareness of gaps with nurses to light is strategic. Collaboration improves health culture, it strengthens the importance of reporting adverse events, and it advances communication on e-drug safety globally (World Health Organization, 2016).

Before automation, medication interpretation differed among nurses, pharmacists, and physicians. Today nurses specifically require prompt clinical decision support alerts that analyze and translate electronic medication audits to enhance the quality and safety of health care audits. This sharing of information closes the loop in medication safety. The proposed action was to implement an alert in the organizations communication dashboard to guide practice translation with electronic pharmacy audits. This adjunct review integrates timely evidence-informed communication with nursing practice and facilitates technology refinement in nursing informatics strategies (Canadian Nurses Association, 2006).

The case of A.M.C v. N.C in 2018 illustrated how to avoid negligence and reduce interpretation of evidence, if systems issues related to safe nursing care are investigated and utilized fully. Guay v. Wong in 2008 supported the premise of causation through inference of risk to show how trier of fact determines responsibilities in relation to vicarious liability. Understanding documentation practices and ensuring public safe was outlined in the College of Nurses of Ontario v. Lim case of 2012 in evidence tendered on how not meeting nursing's medication standard of practice lead to the College on Nurses of Ontario revoking Lim's certificate of registration (The Canadian Legal Information Institute, 2018).

The significance of this project was to reduce causal effects of medication management outlined by the World Health Organization (2016) by introducing structured analysis of medication management practices by nurses in organizations. The research question: If computer interfaces are maximized, will nurses maintain medication and documentation standards of practice? This integrated focus supports technology informatics guiding education reform (TIGER) initiatives to maximize technology and informatics as a change agent (Healthcare Information and Management Systems Society, 2018).

The Purpose

The focus of this project was to maintain nursing medication standards of practice through extrapolations, while utilizing automation in translational care. Decreasing the severity of medication incidence, shrinking the frequency of risks that contribute to errors, and increasing knowledge translation were key objectives. Constructive patient safety outcomes that integrate and embrace the use of technology dash boards progressed how information was received and perceived (Institute for Safe Medication Practice Canada, 2013).

The field of implementation science provided useful frameworks to draw from that emphasized that quality and consistency must be adaptive. Better understanding and maintaining dialogue within frameworks of laws to nurture public safety and expand collaborative exchange through coaching was a goal. Secondly, reviewing mechanisms that reduce barriers in facilitation and decision-supports, maintain fiduciary duties, and continue to collaboratively develop competencies in leadership drivers was critical in discovering the voice of the regulatory practice. Educational alerts have to contain inclusive exchanges to expand reciprocity in risk assessment (MHS Assessments, 2017).

Dr. Leslie Curry, Professor in Public Health Policy and Management at Yale University, focused on how qualitative research can be used to understand the depth of research such as implementing a new technology for context. Appreciating the potential of inclusive analysis generates actionable research concepts that transcend into sustainable health policy and management (Yale University, 2015).

Implementing an e-booklet that captures strengths, weakness, opportunities, and threats (S.W.O.T) of nurses with rigorous literature review is one patient safety health information technology (IT) strategy to engage innovation and collaboration into practice. The information gathered from this analysis can be used to forecast organizational predictive and retrospective analytics. Advanced practice nurses have to develop needs assessments, translate evidence into practice, and develop and evaluate the learning management systems so, that meaningful translational information continues to be timely, responsible, and collaborative (Sipes, 2015).

Focus of Inquiry

Information exchange related to e-medication management audits have to be presented in conjunction with nursing analysis, so that members are better prepared to meet medication standards of practice that are outlined by the College of Nurses of Ontario (College of Nurses of Ontario, 2018). This collaborative movement will lead to positive gains to increase understanding, to share cost, to enhance quality programs, and decrease mitigated risk. Interoperability measures that are value-based advances safety in caring for populations (World Health Organization, 2010).

Evidence accumulated from Statistics Canada has shown that medication errors are aggregated by clinical electronic records sharing. Nurses are at time focused on when the research community investigates medication-related errors. Nurses clarify ambiguous orders and nurses have the knowledge to question orders. The broader context research shows that the complex clinical environments, rather than the individual professional contributes to risk and negligence. The National Survey of the Work and Health of Nurses (Wilkins & Shields, 2005) collected data responses and offered an opportunity to study medication errors based on the Donabedian's structure-processes. Outcome frameworks in this area found that analyzing actual performance with desired performances with accessible technology reduced errors. Analytical logistic regression analysis suggested that confidence intervals on medication error analysis shows that medication errors occur in phases such as prescribing, dispensing, administration, and monitoring. Integrating system development life cycle applications to understand and guide methods in this practice gap advances strategies in safe-medication processes (Institute of Safe Medication Practices Canada, 2018).

Needs Assessment / Gap Analysis

Current Situation: Currently, there are medication management gaps within the electronic health system. Nurses are aware of medication management discrepancies through pharmacy audits, but that data is not synthesized through a collaborative approach to maintain medication and documentation standards of practice.

Gap: There needs to be information designs that analyzes medication and documentation practices collaboratively to debrief nurses on nursing practice standards.

Goal: The goal was to upload an analysis of e- medication management data in the organization communication dashboard. The secondary goal was to show nurses how to synthesis pharmacy feedback proactively to build capacities in patient safety.

S.W.O.T Findings

Review of Literature

Quantitative research has been done which proposed that analyzing medication safety has to have an inter-professional approach. This enables nurses and the pharmacy team to learn from experience. This is a key inter-professional strategy to increase comprehensive documentation of drug management (Bell, Granas, Enmarker, Omol, and Steinbekk, 2017).

Further research inquiries have shown that knowledge translation strategies enhance health professional, organizational, financial, and regulatory decision-making. Evidence-informed decision-making with nurse inclusion closes the gap between knowledge and actions (Yost, Thompson, Ganann, aloweni, Newman, McKibbon, Dobbins, and Ciliska, 2014).

Medication safety prior to the adoption of electronic health records added redundancies that cost money, slowed innovation, and hindered communication mediums. The complexity of medication management must be paralleled with assessment, evaluation, policy, and technology to provide substantive recommendations in medication and documentation cohort analysis. Auto-populated electronic MARs or automated nursing alerts improve system design and processes to integrate substantiate de-briefing (Pascale, Wetterneck, Cartmill, Blosky, and Brown, 2014).

Honorable Beverley McLachlin informed us that listening, making efforts in cheering for value-based conversations, and encouraging collegiality propels advancements in the partisan of the constitution. Policy judgements with collaboration provides good results and is a sound value judgement. Interactions cannot be silent. It has to be weathered, it should be questioned, and compared to due processes as it related to sufficient detail (CBC News: The National, 2017).

Working with stakeholders to amend medication safety with sustainable analytics strategies is a global initiative to strengthen health excellence. Conducting a series of allegations with discovery, that introduces amendments is upheld by law. Forms of case analysis is historical, contextual, and constraining, but introduces optimization that empowers authorities (World Health Organization, 2016).

Practicum Methodology / Procedures

Step 1: Perform a literature review to support the mini gap analysis findings by August 27, 2018

Step 2: Conduct a gap analysis by August 27, 2018

Step 3: Conduct a SWOT analysis of peer feedback to drive the e-booklet clinical quality assurance and safety objectives in electronic health record medication ordering by August 27, 2018.

Step 4: Implement an e- booklet with content analysis by the end of October 2018.

Step 5: Upload the e-booklet to the clinical dashboard communication message tab for one month or for as long as the organization authorizes by the second last week of practicum.

Step 6: Prepare an end of project feedback survey to evaluate the hypothesis: if e-booklets are designed into an organization intranet to collaborate with pharmacy audits, health organizations will be better prepared to support safe e-medication management by October 2018.

Direct Resources

Direct resources included the health management team, the organizations' pharmacy audits, the nursing team, my practicum mentor, the University of Phoenix (UOP) practicum professor, and the writer.

Indirect Resources

Indirect resources included the pharmacy audits, literature review journals, the University of Phoenix (UOP) e-book resources, the UOP library database, the World Health Organization (2016, 2018a) recommendations, the College of Nurses of Ontario guidelines (2018), the Registered Nurses' Association of Ontario recommendations (2018), Google, Google scholar, and citations from the Canadian Legal Institute (2018).

Applicable Compliance

There are several legislated guidelines that all health professionals, including nures must follow. For instance, in the United States, nurses have to be compliant with the Health Insurance Portability and Accountability Act of 1996 (HIPAA) that protects health information, and the Health Information Technology for Economic, and Clinical Health Act of 2009 Title X111 (HI-TECH Act). In Canada, nurses must follow applicable federal and provincial guidelines, such as the need for Ontario nurses to comply with the Personal Health Information Protection Act of 2004 section A. The federal Personal Information Protection and Electronic Documentation Act of 2000 (PIPEDA) and the Digital Privacy Act of 2015 stipulate applicable laws that must be adhered to by all Canadian nurses when maintaining public safety (Anonymous Health Organization, 2018).

To handle applicable compliance the project adhered to the above laws since the writer read and understood the legalities on digital transactions and governance with UOP guided legal authorization. The writer used evidence-based best practices for reducing legal and security compliance risk by monitoring the uploaded project according to the signed UOP agreement.

Regulatory Requirements

When reviewing medication safety deductively, a global reduction of medication harm by fifty percent is recommended (The World Organization, 2016). Medication reconciliation across transitions of care through Safer Healthcare Now is performed by nurses to improves medication reconciliation (The Institute of Safe Medication Practices Canada, 2018).

Nurses meet regulatory medication standards when authority, compliance, safety, and a decision tree are adhered to for public safety (College of Nurses of Ontario, 2018). Nurses in an organization have to be 100 percent compliant with medication management policies supported by pharmacy audits (Anonymous Research Organization, 2018).

Compliance will be successful when as needed (PRN) medication order entries have at least ninety percent compliance with progress notes that are corroborated with real-time pharmacy audits. Nursing connecting documentation entries with medication errors will decline quarterly (Registered Nurses' Association of Ontario, 2013).

The writer outlined College of Nurses of Ontario medication standard requirements (2018) and identified and remediated vulnerabilities based on management suggestions. The management team were updated weekly on the progress of the project to expand utilized focused exchange processes. Making sense of data comes with collaboration. This type of exchange called on understanding the buying-in phase as the intended beneficiary related to the preponderance of evidence (Quinn, 2012).

The writer also reviewed and analyzed medication-reconciliation documents, physician order entries, the Medication Administration Record, and the progress notes processes over the practicum observation time-frame and introduced the e-booklet to facilitate structured guidance in showing nurses tangible evidenced-informed analysis of this medication safety gap.

Design Objectives

1. To have the project run on existing networks used by the organization.

2. The project will run on a SQL database

3. The implementation will be compatible with Java programming language.

4. Prioritizing requirements:

The e-booklet must be uploaded by the end of October 2018.

The project should incorporate the gap analysis, literature review, and regulatory information.

The project design may contain graphics to add to the visual appearance and the cache principles.

The project design would be more effective if management agrees to embed this process with future communication too nurses.

5. Performance testing was done prior to October 2018 to ensure the e-book application was viable without setting of system alerts. The performance testing was done by the end of September 2018. This testing assessed the response time, reliability, usage, and scalability of the project tool by showing no pop-up alerts.

Cost Benefit Analysis

Developing a sustainable cost benefit project document measures performance against constraints. This document outlines time, cost, quality, risk, and scope of the project that are aligned to reduce mitigated risk. The cost benefit analysis outlines the Plan, Do, Check, Act (PDCA) process to track project tasks and deliverables (Sipes, 2015). The actual cost of developing this project is thirteen dollars and fifty cents, based on the calculations in the cost benefit document.

Plan

To design an e-booklet to upload in the intranet communication dashboard to analyze nursing e-medication management. This design collaborates with pharmacy audits and nursing actions when pharmacy audits are reported.

Do

This plan was implemented during the practicum time frame influenced by the SWOT analysis, determined by the gap analysis, gauged by the literature review, and measured by regulatory requirements.

Check

The e-booklet implementation was measured by the end of project survey responses in November 2018. Management were updated weekly on the progress of the project. Performance testing was done in September 2018 and the e-booklet was implemented in November 2018.

Act

The implementation design collaborated with the pharmacy audits to maintain nursing medication practice standards mandated by the College of Nurses of Ontario (CNO, 2018) and supported by the World Health Organization (2016) recommendations.

S.M.A.R.T Objectives

S = The e-booklet will be implemented in the communication dashboard for nurses to review, reflect, and improve on e-medication management synthesis.

M = The measure is effective when nurses in the organization review the e-booklet, when nurses verbalize positive feedback with the end-of-survey form, and when the next pharmacy audit shows reduced e-management concerns.

A = This implementation design will be achievable within the project time allotted by the practicum agreement time frame. The estimated budget to complete the project design is ten to fifteen dollars. The resources used will be the management team, the SWOT survey, and the pharmacy audit indicators.

R = The objectives comply with the organization's mission to improve e-medication management with the EHR for patient safety.

T = The due dates for each task are assigned. Please refer to the project scope.

Risk Management Plan

The integration of quality management systems can bring organizations incalculable advantages. Risk management agendas are aimed at preventing risk or harm thus providing quality care (Costa, Meirelles-Schlindwein, and Erdmann, 2013). This risk plan minimizes the practice gap to show that when technology is utilized and when technology is easily accessible, nurses will continue to adhere to medication practice standards. This phase of this project draws on theoretical data and ethics to frame nurses accounts of computer systems that are linked to strengths, weakness, opportunities, and threats with medication management. The aim in this risk management plan is to identify potential sources of risk. The risk plan chart to proceed will help decide what, if any issues may impact the project, and outline solutions to decrease the degree of probability. (click to view risk plan chart)

Methods

This paper provides an extensive literature review on a specific strategy to augment TIGER initiatives in nursing practice. The evaluation utilized pharmacy audits along with reviewing the strength, opportunities, weakness, and threat (S.W.O.T) analysis of nurses to drive the gap analysis. This review was performed in an organization in Ontario, Canada that facilitated the implementation of a revised Clinical-Decision Support Database. The target audience were a mixed ratio of Registered Nurses and Registered Practical Nurses. The evaluation was conducted over a twelve-week time frame.

Extensive and randomized daily observation of the clinical data was reviewed anonymously to audit order sets: transcription, and documentation processes in real time. This information was aggregated. Then, an e-booklet was uploaded in the clinical communication dash board summarizing the evaluation. The e-booklet provided a comprehensive analysis of pharmacy audits while drawing on precedence and current nursing regulations. The analysis captured the S.W.O.T questionnaire survey, and inexpensively reviewed literature to facilitate pharmacy audits through a research perspective. Some nurses were observed to demonstrate a dampened behavioral response when pharmacy audits were mentioned. The needs assessment found that this gap in nursing practice hampered nursing excellence in practice. The e-booklet described the process used to guide nursing standard expectations similar to a second order process that predicts performances.

Preparing a comprehensive analysis in the database communication dash board, rather than setting up a timed teaming or imputing real-time audit statistics with descriptive words, provided nurses with a substantive break-down that shifted nursing medication and documentation practices. After the e-booklet implementation the organization yielded a ninety percent compliance rate to medication and documentation nursing standards. The transfer of accountability was evident in twenty-four hours, once the e-booklet prompt was read by nurses. Observational and compliance evidence revealed that the nursing compliance ratio with medication quality indicators were maintained at a steady curve and continued beyond the evaluation time. The evaluation was set to delete after three months. Measuring the implementation deletion timeframe was aligned with quarterly quality improvement sustainability tools philosophy that focused on measuring outcomes and process quarterly when scaling algorithms (Ideas Ontario, 2014).

Metrics for Success

The metrics for success were demonstrated by: maintaining the cost schedule, showing that the project implementation meets security compliance; demonstrating that the project uploaded without error alerts; reviewing if pharmacy audit reports improved in the next quarter analysis, the uploading of the e-booklet into the organizational data-base, and lastly, were demonstrated with improved MAR and progress notes by nurses in the organization. This engaged collated feedback that reinforces and moderated the inquiry. When re-evaluating the research, applying theoretical integrity that considers sub-structs responses to conclusions is important. Setting up indicators with reflection and response options related to the needs analysis to meet the project goals. Future research must broaden staff expectations through a shared medium to improve performance triggers (Rapley, Girling, Mair, Murray, Treweek, McColl, Steen, May, & Finch, 2018)

Program Adaptation

Initially, the project adapted to the clinical area when management agreed to its implementation. As well, the project adapted by minimizing risks by sticking to the schedule and budget. and detecting problems early by reviewing real time audits five out or seven days a week, and keeping all people involved utilizing full disclosure based on the University of Phoenix signed Agreement Contract between myself and the organization.

Results

There was a ninety percent amendment in practice that linked medication administration practices to detailed progress notes evidenced by pharmacy audits and daily real time observational methodology within the practicum time frame. Once the e-booklet debriefing tool was uploaded nursing connected medication and documentation standards in the clinical system rose from 70 to 90 percent compliance. Management revealed that there were not only more detailed reports to review that met Nursing Standard of Practice, but specifically, that nursing practice behaviour shifted in asking detained questions when interacting with pharmacy, and that verbal, non-verbal, and written communication to pharmacy were timely, transparent, and accountable (Anonymous Health Organization, 2018).

When information systems are maximized the facilitation of information exchange competencies augments patient safety to meet TIGER initiatives and ultimately meets Ontario nursing practice standards. On a macro level, global initiatives that expand and prioritize collaborative health technologies enriches health systems. This learning domain interfaces professional leadership with research, it advances roles and responsibilities in containing cost, and it allocates reflection in health expenditures to the health environment as it relates to big-data streaming analytics (World Health Organization, 2010).

Appropriately documented health systems review with aggregation of trends relates to cohesive innovations in the delivery of care quality so, that time, skills, knowledge translation, and processes in the workforce identifies and mitigates practice-gaps collaboratively. This form of data adaptation is essential in facilitating pragmatic, sharing of information in health domains globally. Collecting statistics, designing aligned systems, and reporting of information to Health Canada on the management and prevention of medication safety is essential when analyzing health policy indicators (Canadian Center for Policy Alternative, 2011).

Evaluation of Program Success

The project was deemed successful once it was uploaded into the organizations' intranet. It was also deemed successful when the design objectives were met, management and the nursing team verbalized and demonstrated changes in practice to maintain CNO practice standards, and the University of Phoenix MSN-INF core objectives were met by the writer receiving an MSN-INF designation.

Implications

The reduction of causal effects in medication safety as outlined by the World Health Organization by introducing structured analytics collaboratively in clinical decision-supports prioritized safety efforts and closes the loop in medication safety to meet TIGER initiatives that advance Informatics practices in health safety (World Health Organization, 2016).

Conclusion

"The ultimate purpose of metrics and dashboards is not to provide more information but to provide the right information to the right persons at the right time...Project management is an attempt to improve efficiency and effectiveness in the use of resources" (Kerzner, 2017, p. ix). Implementation plans are fluid methodologies capable of interfacing with the business process related to the Triple Aim to improve patient experience, to improve health population, and to reduce per capita cost (Institute for Health Improvement, 2018). This project demonstrates how the e-booklet methodology is a realistic intervention and is easily accessible, which helps nurses continue to adhere to College of Nurses of Ontario (2018) practice standards through the power of inclusive data set analytics.

References

Anonymous Health Organization. . (2018). Authorized for Research Evaluation. Ontario: Canada.

Bates., D.W., and Singh, H. (2018). Two decades since To Err is Human: an assessment of progress and priorities in patient safety. Agency of Healthcare Research and Quality. 37,1736-1743. Retrieved from http://www.psnet.ahrq.gov.

Bell, H. T., Granas, A. G., Enmarker, I., Omil, R., and Steinsbekk, A. (2017). Nurses' and pharmacists' learning experiences from participating in interprofessional medication review for elderly in primary health care - a quantitative study. BMC Family Practice. 18, 2-9.

Brennan, T. A., & Leape, L. L. (1991). Adverse events, negligence in hospitalized patients: results from the Harvard Medical Practice Study. Perspectives In Healthcare Risk Management, 11(2), 2-8.

Canadian Center for Policy Alternatives. (2011). Health Care.Retrieved from http://www.policyalternatives.ca.

Canadian Medical Association & Canadian Nurses Association. (2011). Health Care Transformation in Canada. Retrieved from https://cna-aiic.ca/~/media/cna/files/en/guiding_principles_hc_e.pdf

Canadian Nurses Association. (2006). E-nursing strategy for Canada. Retrieved from https://www.cna-aiic.ca/~/media/cna/page-content/pdf-en/e-nursing-strategy-for- canada.pdf?la=en

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Maximizing Clinical - Systems Aligns Nursing Practice with TIGER Initiatives

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By Racquel Williamson, RN, BScN, MSN-INF Safety in nursing reduces blame in the phrase, "to err is human" (Bates and Singh, 2018, p.1736). Adopting introspective analysis to expand on fixed patterns of feedback mitigates adverse events. This type of performance-based analysis in nursing reduces harm and enhanced knowledge for sustainable and effective solutions in maintaining quality nursing care.

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Interoperability is essential in healthcare in sharing information to all teams to help the patient.  All electronic health records aren’t the same, but with interoperability, it can transfer the data from different operating data so that we all can understand without a gap of vital information to aid in the patient, collaborative healthcare management.

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Sharing patient clinical information is not new. Before health information networks, health care providers and health plans exchanged information directly by hand, fax, telephone or e-mail. These methods are more expensive, time consuming and not secure. In other words, communication was at the center, not the patient.

Most health care providers are now using electronic health records (EHR). However, when a patient goes from one provider to another (i.e. Primary Care Physician referral to a Specialist), these EHRs are not the same. Health information networks allows these providers to share information, essentially allowing their EHRs to "talk to one another." Communication is much more efficient this way so your physician can put the patient the center.
Health care providers can submit a single request through a health information network, such as the Tiger Institute Health Alliance, to obtain electronic records from other members. The provider must have sufficient personal information about a patient to prove they have a treatment relationship before the network will allow access to patient information

This brief video provides an overview of the interoperability challenges facing the healthcare industry.

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Interoperability - Tiger Institute for Health Innovation

# data[].attributes.attachment_link.description
Sharing patient clinical information is not new. Before health information networks, health care providers and health plans exchanged information directly by hand, fax, telephone or e-mail. These methods are more expensive, time consuming and not secure. In other words, communication was at the center, not the patient.

# data[].attributes.body
I have to be honest and say I'm still quite confused about what big data is. There are a plethora of definitions that I have read, including in this article. I gather that Big Data influences healthcare and the need for patient-centered care. It recognizes a possible need, and I find it quite clever. If I, for instance, google autoimmune diseases because it is in my google engine, I will find a significant amount of information that I previously looked up as interest, for example, Education, medications, and even research surveys and clinics.

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Abstract

The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.

Introduction

This paper is the first study to consolidate and characterize the use of Big Data from different perspectives. The first part consists of a brief literature review of studies on Big Data (BD) and Big Data Analytics (BDA), while the second part presents results of direct research aimed at diagnosing the use of big data analyses in medical facilities in Poland.

The next challenges that healthcare will have to face is the growing number of elderly people and a decline in fertility. Fertility rates in the country are found below the reproductive minimum necessary to keep the population stable [ 10]. The reflection of both effects, namely the increase in age and lower fertility rates, are demographic load indicators, which is constantly growing. Forecasts show that providing healthcare in the form it is provided today will become impossible in the next 20 years [ 70]. It is especially visible now during the Covid-19 pandemic when healthcare faced quite a challenge related to the analysis of huge data amounts and the need to identify trends and predict the spread of the coronavirus. The pandemic showed it even more that patients should have access to information about their health condition, the possibility of digital analysis of this data and access to reliable medical support online. Health monitoring and cooperation with doctors in order to prevent diseases can actually revolutionize the healthcare system. One of the most important aspects of the change necessary in healthcare is putting the patient in the center of the system.

Technology is not enough to achieve these goals. Therefore, changes should be made not only at the technological level but also in the management and design of complete healthcare processes and what is more, they should affect the business models of service providers. The use of Big Data Analytics is becoming more and more common in enterprises [ 17, 54]. However, medical enterprises still cannot keep up with the information needs of patients, clinicians, administrators and the creator's policy. The adoption of a Big Data approach would allow the implementation of personalized and precise medicine based on personalized information, delivered in real time and tailored to individual patients.

To achieve this goal, it is necessary to implement systems that will be able to learn quickly about the data generated by people within clinical care and everyday life. This will enable data-driven decision making, receiving better personalized predictions about prognosis and responses to treatments; a deeper understanding of the complex factors and their interactions that influence health at the patient level, the health system and society, enhanced approaches to detecting safety problems with drugs and devices, as well as more effective methods of comparing prevention, diagnostic, and treatment options [ 40].

In the literature, there is a lot of research showing what opportunities can be offered to companies by big data analysis and what data can be analyzed. However, there are few studies showing how data analysis in the area of healthcare is performed, what data is used by medical facilities and what analyses and in which areas they carry out. This paper aims to fill this gap by presenting the results of research carried out in medical facilities in Poland. The goal is to analyze the possibilities of using Big Data Analytics in healthcare, especially in Polish conditions. In particular, the paper is aimed at determining what data is processed by medical facilities in Poland, what analyses they perform and in what areas, and how they assess their analytical maturity. In order to achieve this goal, a critical analysis of the literature was performed, and the direct research was based on a research questionnaire conducted on a sample of 217 medical facilities in Poland. It was hypothesized that medical facilities in Poland are working on both structured and unstructured data and moving towards data-based healthcare and its benefits. Examining the maturity of healthcare facilities in the use of Big Data and Big Data Analytics is crucial in determining the potential future benefits that the healthcare sector can gain from Big Data Analytics. There is also a pressing need to predicate whether, in the coming years, healthcare will be able to cope with the threats and challenges it faces.

This paper is divided into eight parts. The first is the introduction which provides background and the general problem statement of this research. In the second part, this paper discusses considerations on use of Big Data and Big Data Analytics in Healthcare, and then, in the third part, it moves on to challenges and potential benefits of using Big Data Analytics in healthcare. The next part involves the explanation of the proposed method. The result of direct research and discussion are presented in the fifth part, while the following part of the paper is the conclusion. The seventh part of the paper presents practical implications. The final section of the paper provides limitations and directions for future research.

Considerations on use Big Data and Big Data Analytics in the healthcare

As already mentioned, in recent years, healthcare management worldwide has been changed from a disease-centered model to a patient-centered model, even in value-based healthcare delivery model [ 68]. In order to meet the requirements of this model and provide effective patient-centered care, it is necessary to manage and analyze healthcare Big Data.

The issue often raised when it comes to the use of data in healthcare is the appropriate use of Big Data. Healthcare has always generated huge amounts of data and nowadays, the introduction of electronic medical records, as well as the huge amount of data sent by various types of sensors or generated by patients in social media causes data streams to constantly grow. Also, the medical industry generates significant amounts of data, including clinical records, medical images, genomic data and health behaviors. Proper use of the data will allow healthcare organizations to support clinical decision-making, disease surveillance, and public health management. The challenge posed by clinical data processing involves not only the quantity of data but also the difficulty in processing it.

In the literature one can find many different definitions of Big Data. This concept has evolved in recent years, however, it is still not clearly understood. Nevertheless, despite the range and differences in definitions, Big Data can be treated as a: large amount of digital data, large data sets, tool, technology or phenomenon (cultural or technological.

In turn, Knapp perceived Big Data as tools, processes and procedures that allow an organization to create, manipulate and manage very large data sets and storage facilities [ 38]. From this point of view, Big Data is identified as a tool to gather information from different databases and processes, allowing users to manage large amounts of data.

Similar perception of the term 'Big Data' is shown by Carter. According to him, Big Data technologies refer to a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data by enabling high velocity capture, discovery and/or analysis [ 13].

Jordan combines these two approaches by identifying Big Data as a complex system, as it needs data bases for data to be stored in, programs and tools to be managed, as well as expertise and personnel able to retrieve useful information and visualization to be understood [ 37].

Following the definition of Laney for Big Data, it can be state that: it is large amount of data generated in very fast motion and it contains a lot of content [ 43]. Such data comes from unstructured sources, such as stream of clicks on the web, social networks (Twitter, blogs, Facebook), video recordings from the shops, recording of calls in a call center, real time information from various kinds of sensors, RFID, GPS devices, mobile phones and other devices that identify and monitor something [ 8]. Big Data is a powerful digital data silo, raw, collected with all sorts of sources, unstructured and difficult, or even impossible, to analyze using conventional techniques used so far to relational databases.

Volume (refers to the amount of data and is one of the biggest challenges in Big Data Analytics),

Velocity (speed with which new data is generated, the challenge is to be able to manage data effectively and in real time),

Variety (heterogeneity of data, many different types of healthcare data, the challenge is to derive insights by looking at all available heterogenous data in a holistic manner),

Variability (inconsistency of data, the challenge is to correct the interpretation of data that can vary significantly depending on the context),

Veracity (how trustworthy the data is, quality of the data),

Visualization (ability to interpret data and resulting insights, challenging for Big Data due to its other features as described above).

Value (the goal of Big Data Analytics is to discover the hidden knowledge from huge amounts of data).

Big Data is defined as an information asset with high volume, velocity, and variety, which requires specific technology and method for its transformation into value [ 21, 77]. Big Data is also a collection of information about high-volume, high volatility or high diversity, requiring new forms of processing in order to support decision-making, discovering new phenomena and process optimization [ 5, 7]. Big Data is too large for traditional data-processing systems and software tools to capture, store, manage and analyze, therefore it requires new technologies [ 28, 50, 61] to manage (capture, aggregate, process) its volume, velocity and variety [ 9].

The Big Data concept is constantly evolving and currently it does not focus on huge amounts of data, but rather on the process of creating value from this data [ 52]. Big Data is collected from various sources that have different data properties and are processed by different organizational units, resulting in creation of a Big Data chain [ 36]. The aim of the organizations is to manage, process and analyze Big Data. In the healthcare sector, Big Data streams consist of various types of data, namely [ 8, 51]:

clinical data, i.e. data obtained from electronic medical records, data from hospital information systems, image centers, laboratories, pharmacies and other organizations providing health services, patient generated health data, physician's free-text notes, genomic data, physiological monitoring data [ 4],

biometric data provided from various types of devices that monitor weight, pressure, glucose level, etc.,

financial data, constituting a full record of economic operations reflecting the conducted activity,

data from scientific research activities, i.e. results of research, including drug research, design of medical devices and new methods of treatment,

data provided by patients, including description of preferences, level of satisfaction, information from systems for self-monitoring of their activity: exercises, sleep, meals consumed, etc.

data from social media.

These data are provided not only by patients but also by organizations and institutions, as well as by various types of monitoring devices, sensors or instruments [ 16]. Data that has been generated so far in the healthcare sector is stored in both paper and digital form. Thus, the essence and the specificity of the process of Big Data analyses means that organizations need to face new technological and organizational challenges [ 67]. The healthcare sector has always generated huge amounts of data and this is connected, among others, with the need to store medical records of patients. However, the problem with Big Data in healthcare is not limited to an overwhelming volume but also an unprecedented diversity in terms of types, data formats and speed with which it should be analyzed in order to provide the necessary information on an ongoing basis [ 3]. It is also difficult to apply traditional tools and methods for management of unstructured data [ 67]. Due to the diversity and quantity of data sources that are growing all the time, advanced analytical tools and technologies, as well as Big Data analysis methods which can meet and exceed the possibilities of managing healthcare data, are needed [ 3, 68].

Therefore, the potential is seen in Big Data analyses, especially in the aspect of improving the quality of medical care, saving lives or reducing costs [ 30]. Extracting from this tangle of given association rules, patterns and trends will allow health service providers and other stakeholders in the healthcare sector to offer more accurate and more insightful diagnoses of patients, personalized treatment, monitoring of the patients, preventive medicine, support of medical research and health population, as well as better quality of medical services and patient care while, at the same time, the ability to reduce costs (Fig. 1).

(Source: Own elaboration)

Healthcare Big Data Analytics applications

(Source: Own elaboration)

Process of Big Data Analytics

Big Data Analytics in medicine and healthcare refers to the integration and analysis of a large amount of complex heterogeneous data, such as various omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenetics, deasomics), biomedical data, talemedicine data (sensors, medical equipment data) and electronic health records data [ 46, 65].

When analyzing the phenomenon of Big Data in the healthcare sector, it should be noted that it can be considered from the point of view of three areas: epidemiological, clinical and business.

From a clinical point of view, the Big Data analysis aims to improve the health and condition of patients, enable long-term predictions about their health status and implementation of appropriate therapeutic procedures. Ultimately, the use of data analysis in medicine is to allow the adaptation of therapy to a specific patient, that is personalized medicine (precision, personalized medicine).

From an epidemiological point of view, it is desirable to obtain an accurate prognosis of morbidity in order to implement preventive programs in advance.

In the business context, Big Data analysis may enable offering personalized packages of commercial services or determining the probability of individual disease and infection occurrence. It is worth noting that Big Data means not only the collection and processing of data but, most of all, the inference and visualization of data necessary to obtain specific business benefits.

In order to introduce new management methods and new solutions in terms of effectiveness and transparency, it becomes necessary to make data more accessible, digital, searchable, as well as analyzed and visualized.

Erickson and Rothberg state that the information and data do not reveal their full value until insights are drawn from them. Data becomes useful when it enhances decision making and decision making is enhanced only when analytical techniques are used and an element of human interaction is applied [ 22].

Thus, healthcare has experienced much progress in usage and analysis of data. A large-scale digitalization and transparency in this sector is a key statement of almost all countries governments policies. For centuries, the treatment of patients was based on the judgment of doctors who made treatment decisions. In recent years, however, Evidence-Based Medicine has become more and more important as a result of it being related to the systematic analysis of clinical data and decision-making treatment based on the best available information [ 42]. In the healthcare sector, Big Data Analytics is expected to improve the quality of life and reduce operational costs [ 72, 82]. Big Data Analytics enables organizations to improve and increase their understanding of the information contained in data. It also helps identify data that provides insightful insights for current as well as future decisions [ 28].

Big Data Analytics refers to technologies that are grounded mostly in data mining: text mining, web mining, process mining, audio and video analytics, statistical analysis, network analytics, social media analytics and web analytics [ 16, 25, 31]. Different data mining techniques can be applied on heterogeneous healthcare data sets, such as: anomaly detection, clustering, classification, association rules as well as summarization and visualization of those Big Data sets [ 65]. Modern data analytics techniques explore and leverage unique data characteristics even from high-speed data streams and sensor data [ 15, 16, 31, 55]. Big Data can be used, for example, for better diagnosis in the context of comprehensive patient data, disease prevention and telemedicine (in particular when using real-time alerts for immediate care), monitoring patients at home, preventing unnecessary hospital visits, integrating medical imaging for a wider diagnosis, creating predictive analytics, reducing fraud and improving data security, better strategic planning and increasing patients' involvement in their own health.

descriptive analytics in healthcare is used to understand past and current healthcare decisions, converting data into useful information for understanding and analyzing healthcare decisions, outcomes and quality, as well as making informed decisions [ 33]. It can be used to create reports (i.e. about patients' hospitalizations, physicians' performance, utilization management), visualization, customized reports, drill down tables, or running queries on the basis of historical data.

predictive analytics operates on past performance in an effort to predict the future by examining historical or summarized health data, detecting patterns of relationships in these data, and then extrapolating these relationships to forecast. It can be used to i.e. predict the response of different patient groups to different drugs (dosages) or reactions (clinical trials), anticipate risk and find relationships in health data and detect hidden patterns [ 62]. In this way, it is possible to predict the epidemic spread, anticipate service contracts and plan healthcare resources. Predictive analytics is used in proper diagnosis and for appropriate treatments to be given to patients suffering from certain diseases [ 39].

prescriptive analytics-occurs when health problems involve too many choices or alternatives. It uses health and medical knowledge in addition to data or information. Prescriptive analytics is used in many areas of healthcare, including drug prescriptions and treatment alternatives. Personalized medicine and evidence-based medicine are both supported by prescriptive analytics.

discovery analytics-utilizes knowledge about knowledge to discover new "inventions" like drugs (drug discovery), previously unknown diseases and medical conditions, alternative treatments, etc.

Although the models and tools used in descriptive, predictive, prescriptive, and discovery analytics are different, many applications involve all four of them [ 62]. Big Data Analytics in healthcare can help enable personalized medicine by identifying optimal patient-specific treatments. This can influence the improvement of life standards, reduce waste of healthcare resources and save costs of healthcare [ 56, 63, 71]. The introduction of large data analysis gives new analytical possibilities in terms of scope, flexibility and visualization. Techniques such as data mining (computational pattern discovery process in large data sets) facilitate inductive reasoning and analysis of exploratory data, enabling scientists to identify data patterns that are independent of specific hypotheses. As a result, predictive analysis and real-time analysis becomes possible, making it easier for medical staff to start early treatments and reduce potential morbidity and mortality. In addition, document analysis, statistical modeling, discovering patterns and topics in document collections and data in the EHR, as well as an inductive approach can help identify and discover relationships between health phenomena.

Advanced analytical techniques can be used for a large amount of existing (but not yet analytical) data on patient health and related medical data to achieve a better understanding of the information and results obtained, as well as to design optimal clinical pathways [ 62]. Big Data Analytics in healthcare integrates analysis of several scientific areas such as bioinformatics, medical imaging, sensor informatics, medical informatics and health informatics [ 65]. Big Data Analytics in healthcare allows to analyze large datasets from thousands of patients, identifying clusters and correlation between datasets, as well as developing predictive models using data mining techniques [ 65]. Discussing all the techniques used for Big Data Analytics goes beyond the scope of a single article [ 25].

The success of Big Data analysis and its accuracy depend heavily on the tools and techniques used to analyze the ability to provide reliable, up-to-date and meaningful information to various stakeholders [ 12]. It is believed that the implementation of big data analytics by healthcare organizations could bring many benefits in the upcoming years, including lowering health care costs, better diagnosis and prediction of diseases and their spread, improving patient care and developing protocols to prevent re-hospitalization, optimizing staff, optimizing equipment, forecasting the need for hospital beds, operating rooms, treatments, and improving the drug supply chain [ 71].

Challenges and potential benefits of using Big Data Analytics in healthcare

Modern analytics gives possibilities not only to have insight in historical data, but also to have information necessary to generate insight into what may happen in the future. Even when it comes to prediction of evidence-based actions. The emphasis on reform has prompted payers and suppliers to pursue data analysis to reduce risk, detect fraud, improve efficiency and save lives. Everyone-payers, providers, even patients-are focusing on doing more with fewer resources. Thus, some areas in which enhanced data and analytics can yield the greatest results include various healthcare stakeholders (Table 1).

Healthcare organizations see the opportunity to grow through investments in Big Data Analytics. In recent years, by collecting medical data of patients, converting them into Big Data and applying appropriate algorithms, reliable information has been generated that helps patients, physicians and stakeholders in the health sector to identify values and opportunities [ 31]. It is worth noting that there are many changes and challenges in the structure of the healthcare sector. Digitization and effective use of Big Data in healthcare can bring benefits to every stakeholder in this sector. A single doctor would benefit the same as the entire healthcare system. Potential opportunities to achieve benefits and effects from Big Data in healthcare can be divided into four groups [ 8]:

1. 1.
Improving the quality of healthcare services:

assessment of diagnoses made by doctors and the manner of treatment of diseases indicated by them based on the decision support system working on Big Data collections,

detection of more effective, from a medical point of view, and more cost-effective ways to diagnose and treat patients,

analysis of large volumes of data to reach practical information useful for identifying needs, introducing new health services, preventing and overcoming crises,

prediction of the incidence of diseases,

detecting trends that lead to an improvement in health and lifestyle of the society,

analysis of the human genome for the introduction of personalized treatment.

2. 2.
Supporting the work of medical personnel

doctors' comparison of current medical cases to cases from the past for better diagnosis and treatment adjustment,

detection of diseases at earlier stages when they can be more easily and quickly cured,

detecting epidemiological risks and improving control of pathogenic spots and reaction rates,

identification of patients who are predicted to have the highest risk of specific, life-threatening diseases by collating data on the history of the most common diseases, in healing people with reports entering insurance companies,

health management of each patient individually (personalized medicine) and health management of the whole society,

capturing and analyzing large amounts of data from hospitals and homes in real time, life monitoring devices to monitor safety and predict adverse events,

analysis of patient profiles to identify people for whom prevention should be applied, lifestyle change or preventive care approach,

the ability to predict the occurrence of specific diseases or worsening of patients' results,

predicting disease progression and its determinants, estimating the risk of complications,

detecting drug interactions and their side effects.

3. 3.
Supporting scientific and research activity

supporting work on new drugs and clinical trials thanks to the possibility of analyzing "all data" instead of selecting a test sample,

the ability to identify patients with specific, biological features that will take part in specialized clinical trials,

selecting a group of patients for which the tested drug is likely to have the desired effect and no side effects,

using modeling and predictive analysis to design better drugs and devices.

4. 4.
Business and management

reduction of costs and counteracting abuse and counseling practices,

faster and more effective identification of incorrect or unauthorized financial operations in order to prevent abuse and eliminate errors,

increase in profitability by detecting patients generating high costs or identifying doctors whose work, procedures and treatment methods cost the most and offering them solutions that reduce the amount of money spent,

identification of unnecessary medical activities and procedures, e.g. duplicate tests.

According to research conducted by Wang, Kung and Byrd, Big Data Analytics benefits can be classified into five categories: IT infrastructure benefits (reducing system redundancy, avoiding unnecessary IT costs, transferring data quickly among healthcare IT systems, better use of healthcare systems, processing standardization among various healthcare IT systems, reducing IT maintenance costs regarding data storage), operational benefits (improving the quality and accuracy of clinical decisions, processing a large number of health records in seconds, reducing the time of patient travel, immediate access to clinical data to analyze, shortening the time of diagnostic test, reductions in surgery-related hospitalizations, exploring inconceivable new research avenues), organizational benefits (detecting interoperability problems much more quickly than traditional manual methods, improving cross-functional communication and collaboration among administrative staffs, researchers, clinicians and IT staffs, enabling data sharing with other institutions and adding new services, content sources and research partners), managerial benefits (gaining quick insights about changing healthcare trends in the market, providing members of the board and heads of department with sound decision-support information on the daily clinical setting, optimizing business growth-related decisions) and strategic benefits (providing a big picture view of treatment delivery for meeting future need, creating high competitive healthcare services) [ 73].

The above specification does not constitute a full list of potential areas of use of Big Data Analysis in healthcare because the possibilities of using analysis are practically unlimited. In addition, advanced analytical tools allow to analyze data from all possible sources and conduct cross-analyses to provide better data insights [ 26]. For example, a cross-analysis can refer to a combination of patient characteristics, as well as costs and care results that can help identify the best, in medical terms, and the most cost-effective treatment or treatments and this may allow a better adjustment of the service provider's offer [ 62].

In turn, the analysis of patient profiles (e.g. segmentation and predictive modeling) allows identification of people who should be subject to prophylaxis, prevention or should change their lifestyle [ 8]. Shortened list of benefits for Big Data Analytics in healthcare is presented in paper [ 3] and consists of: better performance, day-to-day guides, detection of diseases in early stages, making predictive analytics, cost effectiveness, Evidence Based Medicine and effectiveness in patient treatment.

Methods

The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities in Poland.

Presented research results are part of a larger questionnaire form on Big Data Analytics. The direct research was based on an interview questionnaire which contained 100 questions with 5-point Likert scale (1-strongly disagree, 2-I rather disagree, 3-I do not agree, nor disagree, 4-I rather agree, 5-I definitely agree) and 4 metrics questions. The study was conducted in December 2018 on a sample of 217 medical facilities (110 private, 107 public). The research was conducted by a specialized market research agency: Center for Research and Expertise of the University of Economics in Katowice.

When it comes to direct research, the selected entities included entities financed from public sources-the National Health Fund (23.5%), and entities operating commercially (11.5%). In the surveyed group of entities, more than a half (64.9%) are hybrid financed, both from public and commercial sources. The diversity of the research sample also applies to the size of the entities, defined by the number of employees. Taking into account proportions of the surveyed entities, it should be noted that in the sector structure, medium-sized (10-50 employees-34% of the sample) and large (51-250 employees-27%) entities dominate. The research was of all-Poland nature, and the entities included in the research sample come from all of the voivodships. The largest group were entities from Łódzkie (32%), Śląskie (18%) and Mazowieckie (18%) voivodships, as these voivodships have the largest number of medical institutions. Other regions of the country were represented by single units. The selection of the research sample was random-layered. As part of medical facilities database, groups of private and public medical facilities have been identified and the ones to which the questionnaire was targeted were drawn from each of these groups. The analyses were performed using the GNU PSPP 0.10.2 software.

The aim of the study was to determine whether medical facilities in Poland use Big Data Analytics and if so, in which areas. Characteristics of the research sample is presented in Table 2.

The research is non-exhaustive due to the incomplete and uneven regional distribution of the samples, overrepresented in three voivodeships (Łódzkie, Mazowieckie and Śląskie). The size of the research sample (217 entities) allows the authors of the paper to formulate specific conclusions on the use of Big Data in the process of its management.

For the purpose of this paper, the following research hypotheses were formulated: (1) medical facilities in Poland are working on both structured and unstructured data (2) medical facilities in Poland are moving towards data-based healthcare and its benefits.

The paper poses the following research questions and statements that coincide with the selected questions from the research questionnaire:

From what sources do medical facilities obtain data? What types of data are used by the particular organization, whether structured or unstructured, and to what extent?

From what sources do medical facilities obtain data?

In which area organizations are using data and analytical systems (clinical or business)?

Is data analytics performed based on historical data or are predictive analyses also performed?

Determining whether administrative and medical staff receive complete, accurate and reliable data in a timely manner?

Determining whether real-time analyses are performed to support the particular organization's activities.

Results and discussion

On the basis of the literature analysis and research study, a set of questions and statements related to the researched area was formulated. The results from the surveys show that medical facilities use a variety of data sources in their operations. These sources are both structured and unstructured data (Table 3).

According to the data provided by the respondents, considering the first statement made in the questionnaire, almost half of the medical institutions (47.58%) agreed that they rather collect and use structured data (e.g. databases and data warehouses, reports to external entities) and 10.57% entirely agree with this statement. As much as 23.35% of representatives of medical institutions stated "I agree or disagree". Other medical facilities do not collect and use structured data (7.93%) and 6.17% strongly disagree with the first statement. Also, the median calculated based on the obtained results (median: 4), proves that medical facilities in Poland collect and use structured data (Table 4).

In turn, 28.19% of the medical institutions agreed that they rather collect and use unstructured data and as much as 9.25% entirely agree with this statement. The number of representatives of medical institutions that stated "I agree or disagree" was 27.31%. Other medical facilities do not collect and use structured data (17.18%) and 13.66% strongly disagree with the first statement. In the case of unstructured data the median is 3, which means that the collection and use of this type of data by medical facilities in Poland is lower.

In the further part of the analysis, it was checked whether the size of the medical facility and form of ownership have an impact on whether it analyzes unstructured data (Tables 4 and 5). In order to find this out, correlation coefficients were calculated.

Based on the calculations, it can be concluded that there is a small statistically monotonic correlation between the size of the medical facility and its collection and use of structured data (p < 0.001; τ = 0.16). This means that the use of structured data is slightly increasing in larger medical facilities. The size of the medical facility is more important according to use of unstructured data (p < 0.001; τ = 0.23) (Table 4.).

To determine whether the form of medical facility ownership affects data collection, the Mann-Whitney U test was used. The calculations show that the form of ownership does not affect what data the organization collects and uses (Table 5).

Detailed information on the sources of from which medical facilities collect and use data is presented in the Table 6.

The questionnaire results show that medical facilities are especially using information published in databases, reports to external units and transaction data, but they also use unstructured data from e-mails, medical devices, sensors, phone calls, audio and video data (Table 6). Data from social media, RFID and geolocation data are used to a small extent. Similar findings are concluded in the literature studies.

From the analysis of the answers given by the respondents, more than half of the medical facilities have integrated hospital system (HIS) implemented. As much as 43.61% use integrated hospital system and 16.30% use it extensively (Table 7). 19.38% of exanimated medical facilities do not use it at all. Moreover, most of the examined medical facilities (34.80% use it, 32.16% use extensively) conduct medical documentation in an electronic form, which gives an opportunity to use data analytics. Only 4.85% of medical facilities don't use it at all.

Other problems that needed to be investigated were: whether medical facilities in Poland use data analytics? If so, in what form and in what areas? (Table 8). The analysis of answers given by the respondents about the potential of data analytics in medical facilities shows that a similar number of medical facilities use data analytics in administration and business (31.72% agreed with the statement no. 5 and 12.33% strongly agreed) as in the clinical area (33.04% agreed with the statement no. 6 and 12.33% strongly agreed). When considering decision-making issues, 35.24% agree with the statement "the organization uses data and analytical systems to support business decisions" and 8.37% of respondents strongly agree. Almost 40.09% agree with the statement that "the organization uses data and analytical systems to support clinical decisions (in the field of diagnostics and therapy)" and 15.42% of respondents strongly agree. Exanimated medical facilities use in their activity analytics based both on historical data (33.48% agree with statement 7 and 12.78% strongly agree) and predictive analytics (33.04% agrees with the statement number 8 and 15.86% strongly agree). Detailed results are presented in Table 8.

Medical facilities focus on development in the field of data processing, as they confirm that they conduct analytical planning processes systematically and analyze new opportunities for strategic use of analytics in business and clinical activities (38.33% rather agree and 10.57% strongly agree with this statement). The situation is different with real-time data analysis, here, the situation is not so optimistic. Only 28.19% rather agree and 14.10% strongly agree with the statement that real-time analyses are performed to support an organization's activities.

When considering whether a facility's performance in the clinical area depends on the form of ownership, it can be concluded that taking the average and the Mann-Whitney U test depends. A higher degree of use of analyses in the clinical area can be observed in public institutions.

Whether a medical facility performs a descriptive or predictive analysis do not depend on the form of ownership (p > 0.05). It can be concluded that when analyzing the mean and median, they are higher in public facilities, than in private ones. What is more, the Mann-Whitney U test shows that these variables are dependent from each other (p < 0.05) (Table 9).

When considering whether a facility's performance in the clinical area depends on its size, it can be concluded that taking the Kendall's Tau (τ) it depends (p < 0.001; τ = 0.22), and the correlation is weak but statistically important. This means that the use of data and analytical systems to support clinical decisions (in the field of diagnostics and therapy) increases with the increase of size of the medical facility. A similar relationship, but even less powerful, can be found in the use of descriptive and predictive analyses (Table 10).

Considering the results of research in the area of analytical maturity of medical facilities, 8.81% of medical facilities stated that they are at the first level of maturity, i.e. an organization has developed analytical skills and does not perform analyses. As much as 13.66% of medical facilities confirmed that they have poor analytical skills, while 38.33% of the medical facility has located itself at level 3, meaning that "there is a lot to do in analytics". On the other hand, 28.19% believe that analytical capabilities are well developed and 6.61% stated that analytics are at the highest level and the analytical capabilities are very well developed. Detailed data is presented in Table 11. Average amounts to 3.11 and Median to 3.

The results of the research have enabled the formulation of following conclusions. Medical facilities in Poland are working on both structured and unstructured data. This data comes from databases, transactions, unstructured content of emails and documents, devices and sensors. However, the use of data from social media is smaller. In their activity, they reach for analytics in the administrative and business, as well as in the clinical area. Also, the decisions made are largely data-driven.

In summary, analysis of the literature that the benefits that medical facilities can get using Big Data Analytics in their activities relate primarily to patients, physicians and medical facilities. It can be confirmed that: patients will be better informed, will receive treatments that will work for them, will have prescribed medications that work for them and not be given unnecessary medications [ 78]. Physician roles will likely change to more of a consultant than decision maker. They will advise, warn, and help individual patients and have more time to form positive and lasting relationships with their patients in order to help people. Medical facilities will see changes as well, for example in fewer unnecessary hospitalizations, resulting initially in less revenue, but after the market adjusts, also the accomplishment [ 78]. The use of Big Data Analytics can literally revolutionize the way healthcare is practiced for better health and disease reduction.

The analysis of the latest data reveals that data analytics increase the accuracy of diagnoses. Physicians can use predictive algorithms to help them make more accurate diagnoses [ 45]. Moreover, it could be helpful in preventive medicine and public health because with early intervention, many diseases can be prevented or ameliorated [ 29]. Predictive analytics also allows to identify risk factors for a given patient, and with this knowledge patients will be able to change their lives what, in turn, may contribute to the fact that population disease patterns may dramatically change, resulting in savings in medical costs. Moreover, personalized medicine is the best solution for an individual patient seeking treatment. It can help doctors decide the exact treatments for those individuals. Better diagnoses and more targeted treatments will naturally lead to increases in good outcomes and fewer resources used, including doctors' time.

Conclusion

The quantitative analysis of the research carried out and presented in this article made it possible to determine whether medical facilities in Poland use Big Data Analytics and if so, in which areas. Thanks to the results obtained it was possible to formulate the following conclusions. Medical facilities are working on both structured and unstructured data, which comes from databases, transactions, unstructured content of emails and documents, devices and sensors. According to analytics, they reach for analytics in the administrative and business, as well as in the clinical area. It clearly showed that the decisions made are largely data-driven. The results of the study confirm what has been analyzed in the literature. Medical facilities are moving towards data-based healthcare and its benefits.

In conclusion, Big Data Analytics has the potential for positive impact and global implications in healthcare. Future research on the use of Big Data in medical facilities will concern the definition of strategies adopted by medical facilities to promote and implement such solutions, as well as the benefits they gain from the use of Big Data analysis and how the perspectives in this area are seen.

Limitations and future directions

The research described in this article does not fully exhaust the questions related to the use of Big Data Analytics in Polish healthcare facilities. Only some of the dimensions characterizing the use of data by medical facilities in Poland have been examined. In order to get the full picture, it would be necessary to examine the results of using structured and unstructured data analytics in healthcare. Future research may examine the benefits that medical institutions achieve as a result of the analysis of structured and unstructured data in the clinical and management areas and what limitations they encounter in these areas. For this purpose, it is planned to conduct in-depth interviews with chosen medical facilities in Poland. These facilities could give additional data for empirical analyses based more on their suggestions. Further research should also include medical institutions from beyond the borders of Poland, enabling international comparative analyses.

Future research in the healthcare field has virtually endless possibilities. These regard the use of Big Data Analytics to diagnose specific conditions [ 47, 66, 69, 76], propose an approach that can be used in other healthcare applications and create mechanisms to identify "patients like me" [ 75, 80]. Big Data Analytics could also be used for studies related to the spread of pandemics, the efficacy of covid treatment [ 18, 79], or psychology and psychiatry studies, e.g. emotion recognition [ 35].

Availability of data and materials

The datasets for this study are available on request to the corresponding author.

Acknowledgements

We would like to thank those who have touched our science paths.

Funding

This research was fully funded as statutory activity-subsidy of Ministry of Science and Higher Education granted for Technical University of Czestochowa on maintaining research potential in 2018. Research Number: BS/PB-622/3020/2014/P. Publication fee for the paper was financed by the University of Economics in Katowice.

Author information

Authors and Affiliations

1. Department of Business Informatics, University of Economics in Katowice, Katowice, Poland

Kornelia Batko

2. Department of Biomedical Processes and Systems, Institute of Health and Nutrition Sciences, Częstochowa University of Technology, Częstochowa, Poland

Andrzej Ślęzak

Contributions

KB proposed the concept of research and its design. The manuscript was prepared by KB with the consultation of AŚ. AŚ reviewed the manuscript for getting its fine shape. KB prepared the manuscript in the contexts such as definition of intellectual content, literature search, data acquisition, data analysis, and so on. AŚ obtained research funding. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Kornelia Batko.

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Not applicable.

Competing interests

The author declares no conflict of interest.

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# data[].attributes.attachment_link.title
The use of Big Data Analytics in healthcare - Journal of Big Data

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The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare.

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What's interesting about this video is how they are using data to treat patients specifically. Researchers and physicians can treat the patient's individually and specifically to their needs. Knowing what worked for one patient using electronics for data and information can help doctors predict what won't work for another patient. Using a phone or even a wireless finger probe for sleep studies is impressive.

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How Big Data Could Transform The Health Care Industry

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This article talks about how the DIKW hierarchy works in helping nurse leaders manage healthcare and how it is used in patient care. Data helps predict the staff needed for the nurse-patient ratio and predictors on future admissions and bed flow; it can aid in how many beds will be needed. Some of the Istat machines are so small that we can take a little blood sample from the patient's bedside and see some critical labs transferred into the patient's records.

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I thought this video was cute; it shows how important it is to have a vendor or educator be there physically to help implement a new EHR in a clinic or hospital. Knowing that you have a knowledgeable member to aid in difficulties and uncertainty always helps a transition go smoothly.

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The Virginia department of health and their requirement was an excellent read. I had questions about how it all worked with public health. However, it shows that participating physicians and hospitals that work with public health have their objectives with the Virginia department of health and the requirement they have to fulfill, such as immunizations, cancer registry, electronic laboratory results, and Syndromic surveillance that are explicitly used for urgent care facilities.

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The U.S. Centers for Medicare and Medicaid Services (CMS) has renamed the Electronic Health Record (EHR) Incentive Programs (formerly Meaningful Use) to the Promoting Interoperability (PI) Programs.
Virginia Department of Health (VDH) is currently able to receive electronic submissions for six public health-related measures (Antimicrobial Use and Resistance (AUR), Cancer Reporting, Electronic Case Reporting (eCR), Electronic Laboratory Reporting (ELR), Immunizations, and Syndromic Surveillance) and is actively working with eligible hospitals (EH), critical access hospitals (CAH), and eligible professionals (EP) to assist them in meeting these measures. This webpage serves as the VDH Declaration of Readiness for Promoting Interoperability/Meaningful Use requirements beginning January 1st, 2018.

The Health Information Technology for Economic and Clinical Health Act (HITECH) provides financial incentives to encourage the use of electronic health records. CMS implemented incentive programs (i.e. Promoting Interoperability (PI), Meaningful Use (MU), Merit-based Incentive Payments System (MIPS), Hospital Outpatient Prospective Payment System (OPPS)) for eligible health care professionals, eligible hospitals and critical access hospitals to adopt, implement or demonstrate meaningful use of certified EHR technology.

Note: The use of the term "Meaningful Use" on VDH websites refers to the exchange of public health messaging with VDH for all CMS EHR Incentive Programs such as Promoting Interoperability, Meaningful Use, MIPS, or OPPS.
Please print and retain a copy of this webpage to document the status of services provided by VDH to support Meaningful Use.
Register with VDH
All eligible hospitals and eligible professionals intending to work on any of the public health measures (with the exception of AUR) must register with VDH each attestation year in order to receive acknowledgments for Meaningful Use, including those already submitting Meaningful Use data to VDH. For information about AUR registration, click here.
Summary of Public Health Measures accepted by VDH

The table below summarizes the Public Health Reporting Measures accepted by the Virginia Department of Health for each provider type.
Promoting Interoperability (2018 to present) Meaningful Use Modified Stage 2 (2015-2017)
✓= public health measure supported
X = public health measure not supported

ConnectVirginia

VDH is also the lead agency in development of ConnectVirginia, the statewide Health Information Exchange (HIE). ConnectVirginia provides services that allow healthcare providers to send and receive health information electronically and offers a convenient way to transmit Meaningful Use messages to VDH.

CMS Electronic Health Record Incentive Program

The U.S. Centers for Medicare and Medicaid Services (CMS) provides detailed information on the Electronic Health Record Incentive Program requirements, as well as helpful answers to frequently asked questions, on their web page at http://www.cms.gov/EHRIncentivePrograms/.

Both Medicare and Medicaid offer financial incentives to providers and hospitals that implement or enhance EHR systems and meet criteria to demonstrate their meaningful use. Specific requirements and timeframes are provided in the regulations. Implementation and effective use of Certified EHR technology is a step toward reforming the health care system and improving health care quality, efficiency, and patient safety.

Virginia Medicaid Provider Incentive Program

The Medicaid Provider Incentive Program in Virginia is administered by the Department of Medical Assistance Services. Information is available on the Incentive Program website: http://dmasva.dmas.virginia.gov/Content_pgs/pr-arra.aspx.

Page last modified: 6/24/2021 2:00PM

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Electronic Health Record Incentive Programs ("Meaningful Use") - Meaningful Use

# data[].attributes.attachment_link.description
The U.S. Centers for Medicare and Medicaid Services (CMS) has renamed the Electronic Health Record (EHR) Incentive Programs (formerly Meaningful Use) to the Promoting Interoperability (PI) Programs. Virginia Department of Health (VDH) is currently able to receive electronic submissions for six public health-related measures (Antimicrobial Use and Resistance (AUR), Cancer Reporting, Electronic Case Reporting (eCR), [...]

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Tyler, the patient in the video sees his primary care physician. The physician notices heart irregularities puts an urgent consult in the cardiology. Consult is received via EHR patient get seen by cardiology goes to Operating room for an AICD implant. AICD is entered in the computers for function and serial numbers. Patient is sent home can look at discharge instructions via online and can communicate with his doctor. This is a short synopsis of how meaningful use works in healthcare.

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A Patient's Journey: Meaningful Use Stage 3 and MIPS

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I honestly didn’t know there was a difference between Electronic Medical records (EMR) and Electronic Health Records (EHR) until today. In my understanding, EHR is a database that can be seen and shared across a spectrum of facilities and patients. This is where patients can view their labs through an app or portal and communicate with their providers through email or instant messaging. EMR is just facility-related, and records are kept in their database. The database is not shared. I found that cost is a factor in why some providers may choose one over the other. In all, EHR would be most beneficial for patient care.

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EHR vs. EMR: what’s the difference?

Although some clinicians use the terms EHR and EMR interchangeably, the benefits they offer vary greatly. An EMR (electronic medical record) is a digital version of a chart with patient information stored in a computer and an EHR (electronic health record) is a digital record of health information.

Practice Fusion is a cloud-based EHR system that securely stores data on external servers and can be accessed with any device that has an internet connection, whereas server-based EHR systems store data on either a personal server or in a data center.

1. Differences between EHR and EMR

2. EMR (electronic medical record) Definition

3. EHR (electronic health record) Definition

4. EHR vs. EMR; usage trends

5. EHR vs. EMR; the advantages

6. EHR vs. EMR; the disadvantages

7. EMR vs. EHR; the benefits

8. CEHRT or CEMRT

Differences between EHR and EMR

EHR (electronic health records) | EMR (electronic medical records)
- A digital record of patient health information | - A digital version of a chart
- Streamlined sharing with other providers and labs, etc. of updated, real-time information | - Not designed to be shared outside the individual practice
- Allows a patient’s medical information to move with them | - Patient record does not easily travel outside the practice
- Access to tools that providers can use for decision making | - Mainly used by providers for diagnosis and treatment

What is the definition of an EMR (electronic medical record)?

The EMR, or electronic medical record, refers to everything you’d find in a paper chart, such as medical history, diagnoses, medications, immunization dates, and allergies. While EMRs work well within a practice, they’re limited because they don’t easily travel outside the practice. In fact, the patient’s medical record might even have to be printed out and mailed for another provider to see it.

What is the definition of an EHR (electronic health record)?

An EHR or electronic health record is a digital record of health information. It contains all the information you’d find in a paper chart — and a lot more. An EHR may include past medical history, vital signs, progress notes, diagnoses, medications, immunization dates, allergies, lab data and imaging reports. It can also contain other relevant information, such as insurance information, demographic data, and even data imported from personal wellness devices.

Practice Fusion is a cloud-based EHR system that allows enables interoperability and the secure sharing of health information. It offers the opportunity to extend the capabilities available to health organizations in order to implement better ways of working and to offer new services to patients.

The power of an EHR lies not only in the data it contains, but how it’s shared – health information becomes instantly accessible to authorized providers across practices and health organizations, helping to coordinate care efficiently. An EHR can be shared with clinicians and organizations involved in a patient’s care, such as labs, specialists, imaging facilities, pharmacies, emergency facilities, and school and workplace clinics.

EHRs MEET MEANINGFUL USE REQUIREMENTS FOR MEDICARE AND MEDICAID

An EHR is necessary to meet Meaningful Use requirements. Meaningful Use is a Medicare and Medicaid program that supports the use of an EHR to improve patient care. To achieve Meaningful Use and avoid penalties on Medicare and Medicaid reimbursements, eligible providers must follow a set of criteria that serve as a roadmap for effectively using an EHR. CMS has renamed the Medicare Meaningful Use program into MIPS and the Medicaid program into Promoting Interoperability.

Using Practice Fusion enables you to meet the criteria for Meaningful Use so you can receive reimbursement.

What are the EHR vs. EMR usage trends?

EHRs are the future of healthcare because they provide critical data that can help coordinate care between all providers in the healthcare ecosystem.

While both EHR and EMR are commonly used terms, the term “EHR” (electronic health records) is now referenced more frequently. This is likely due to the Centers for Medicare & Medicaid Services (CMS), as well as the Office of the National Coordinator for Health Information (ONC) preference for the term “EHR.”

When speaking of health care reform, CMS always uses the terminology, “meaningful use of an EHR.” The ONC exclusively uses the terms “EHR” and “electronic health records,” explaining that the word ‘health’ is more encompassing than the word ‘medical’. The term “Medical Records” implies clinician records for diagnosis and treatment, while the term “Health Records” more broadly denotes anything related to the general condition of the body. A Personal Health Record known as PHR is just that: personal. It is those parts of the EMR/EHR that an individual person “owns” and controls.

According to the Centers for Disease Control and Prevention (CDC), 85.9% of office-based physicians use any EMR/EHR system, while 79.7% use a certified EMR/EHR system.¹

A fully functional EHR system goes beyond basic functionalities such as clinical notes and documentation. It incorporates more of your practice workflows. With a fully functional EHR, your practice is seamlessly integrated with other members of the healthcare community, helping to:

Improve coordination of care

Increase patient participation in care

Improve the quality of patient care

Increase efficiencies and cost savings for your practice

What are the advantages of an EHR versus an EMR?

Compared to paper records, a digital patient-record (EHR) system can add information management tools to help providers provide better care by more efficiently organizing, interpreting, and reacting to data.

EHR software can provide clinical reminder alerts, connect experts for health care decision support, and analyze aggregate data for both care management and research

The more interactive an EHR system is, the more it will prompt the user for additional information. This not only helps collect more data but also enhances their completeness.

EHRs are the future of healthcare because they provide critical data that informs clinical decisions, and they help coordinate care between all providers in the healthcare ecosystem.

EHR systems focus on the total health of the patient. EHR software is designed to reach out beyond the health organization that originally collects and compiles the information. They are built to share information with other health care providers, such as laboratories and specialists, so they contain information from all the clinicians involved in the patient’s care.

The information moves with the patient—to the specialist, the hospital, the nursing home, the next state or even across the country. EHR systems are designed to be accessed by all people involved in the patient’s care—including the patient.

WHAT ARE THE ADVANTAGES OF PRACTICE FUSION?

Practice Fusion is a cloud-based EHR system and offers an affordable, turnkey solution that is considered one of the most user-friendly EHRs available. For only $149 per provider, per month, Practice Fusion can help your practice meet regulations, electronically prescribe controlled substances, and integrate patient records with laboratories and imaging centers.

Practice Fusion can help you and your staff save time and improve clinical efficiency. You’ll also get all the support you need for no extra or hidden charges. Practice Fusion’s expert support staff stands ready to assist with training and issues if they arise.

WHAT ARE THE DISADVANTAGES OF EHR and EMR SOFTWARE SYSTEMS?

While there are many advantages to having an EMR or EHR system, there are some disadvantages as well.

They are typically much more expensive to implement initially, as providers must invest in the proper hardware, training and support on top of the software.

Unless properly built, there’s also the chance the system will malfunction, destroy all data.

What are the benefits of an EHR versus an EMR?

The EHR is the future of healthcare because they provide critical data that can help coordinate care between everyone in the healthcare ecosystem. An EHR has the following benefits over an EMR:

Health information and data. The system holds what‘s normally in a paper chart – problem lists, ICD-10 codes, medication lists, test results.

Results management. An EHR lets you receive lab results, radiology reports, and even X-ray images electronically while ensuring tests are not duplicated.

Order entry. No more prescription pads. All your orders are automated using secure e-prescribing technology.

Decision support. Offer access to evidence-based tools to support clinical decisions. An EHR is smart enough to warn you about drug interactions, help you make a diagnosis, and point you to evidence-based guidelines when you’re evaluating treatment options.

Electronic communications and connectivity. You can talk in cyberspace with patients, your medical assistant, referring doctors, hospitals, and insurers—securely. Interoperability is the key word as you streamline your workflow by interfacing with other providers, labs, imaging centers, and payers.

Patient support. Engage your patients by allowing to them to receive educational material via the EHR and enter data themselves through online questionnaires and home monitoring devices.

Administrative processes. The system lends a hand with practice management and helps avoid delays in treatments. Patients can schedule their own appointments and staffers can check on insurance eligibility.

Reporting and population health management. How many patients did you treat for tuberculosis in 2014 How many of your diabetics have their HbA1c under 7? An EHR can provide the answers, thanks to a searchable database.

CEHRT or CEMRT?

In order to capture and share patient data efficiently, providers need an EHR that stores data in a structured format. Structured data allows patient information to be easily retrieved and transferred and allows the provider to use the EHR in ways that can aid patient care.

Regardless if providers and vendors use the terms, “Electronic Medical Record” or “EMR” when talking about Electronic Health Record (EHR) technology, for the purposes of the Medicare and Medicaid Incentive Programs, eligible professionals must use certified EHR technology or CEHRT.

Practice Fusion is 100% certified EHR technology (CEHRT).

HOW TO CHOOSE THE BEST EHR FOR YOUR PRACTICE

Choosing a fully integrated EHR goes beyond just the features — you’ll need to evaluate the costs, required hardware, the complexity of implementation, and the available training and support. Learn more about how to choose an EHR.

HOW THE PRACTICE FUSION EHR BENEFITS PATIENTS

Improving clinician workflows and practice efficiencies passes the benefits of the EHR on to patients. Quality of care and patient satisfaction improves as clinicians become more efficient in their daily tasks. Spending less time charting allows for more time to do what matters most: care for your patients.

Practice Fusion can provide an affordable EHR solution for small and independent practices. Its cloud-based EHR platform enables clinicians to comply with electronic prescribing of controlled substances (EPCS) regulations, qualify for meaningful use reimbursement, and integrate patient records with labs and imaging centers.

An easy-to-use EHR like Practice Fusion, can help reduce clinician burnout due to time spent maintaining paper charts or working with cumbersome, hard-to-use software. Most importantly, Practice Fusion can help improve interoperability and contribute toward a better quality of care and patient experience. Your practice is our purpose.

References:

1. CDC.gov. 2021. FastStats. Available at: https://www.cdc.gov/nchs/fastats/electronic-medical-records.htm Accessed 29 April 2021.

Practice Fusion is a streamlined and efficient EHR system designed for independent practices like yours. Start your free 14-day trial today!

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EHR vs. EMR Definition, Benefits & EHR Usage Trends | Practice Fusion

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EHR or electronic health record are digital records of health information. Meet the EHR that helps independent practices thrive. Easy to master & phenom support.

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I find this enlightening being surrounded by all of this daily. I never knew the term for it. So to have an ah-ha moment is astonishing. So my current facility where I work uses the Be Well program. The incentive for answering questions and participating in several programs is that you get money at the beginning of the year to use for your health. The annual lab and vitals the requirements are weight, waist measurement, cholesterol, and glucose. If you are found in critical status, there are certain programs that you can choose from. What I find interesting is that there is no continuity of care. Let's say your glucose is elevated. No provider calls you. It is left up to me to contact my primary physician and alert them of my findings; that isn't continuity of care.

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This is my state's health information exchange. Before this class, I didn't know this existed, but I found some helpful information and will explore more on my current hospital participation use and status. I did learn how meaningful comes to play with ConnectVirginia and health information exchange in general.

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Doctors, hospitals, and health care providers share your medical information via phone, fax, and mail. We provide a better, safer, and faster way for your medical providers to share pertinent information through a secure HIE (Health Information Exchange). When your providers join our network, they can quickly access information such as allergies or medications, giving them the ability to coordinate and improve the quality of your care.

Click here for a fact sheet on Health IT.

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Doctors, hospitals, and health care providers share your medical information via phone, fax, and mail. We provide a better, safer, and faster way for your medical providers to share pertinent information through a secure HIE (Health Information Exchange).

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. 10 Best Healthcare Data Sets (Examples)

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This blog I found interesting on several organizations and databases. I found a database that I  was interested in, the Human Mortality Database, and the research and data set for all countries across the nation. World Health Organization (WHO) is a popular database that I have used in research for class assignments and projects. Using these and not knowing that these particular subjects that one may be interested in are data sets is an eye-awakening moment.

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Quick Navigation

Wikipedia defines a data set as a collection of data. What does that mean exactly? A data set is a collection of related sets of information composed of separate items, which can be processed as a unit by a computer. Generally, a single database table or a single statistical data matrix can be a data set. The set of items can consist of just a few items or millions of them. Either way, the fact that the items are stacked together makes them a set. This is particularly useful for data mining, a method of data analysis that searches for trends and patterns in data, providing the competitive advantage to any custom software solution.

What are healthcare datasets and why are they important?

Healthcare analytics is based on data and data sets in particular and provides all benefits of dashboards in healthcare systems. Due to the diversity of healthcare data sources, data standardization is a key pillar for efficient and meaningful use of the information and collaboration of healthcare professionals, care providers, insurers, and government agencies.

Data interchange in the US healthcare industry is strictly regulated both on national and federal levels. The Health Insurance Portability and Accountability Act (HIPAA), published in 1996, is the core set of healthcare IT data standards.

The HIPAA Rules regulate the use and disclosure of personal health information (PHI) and establish national standards to protect individuals' electronic PHI from data theft. The Health Information Technology for Economic and Clinical Health Act (HITECH Act), adopted in 2009, is aimed to "improve health care quality, safety, and efficiency through the promotion of health IT, including electronic health records (EHR) and private and secure electronic health information exchange".

Both laws insist on the importance of data interchange standards, including common encoding specifications, medical templates for structuring information, document architectures, and information models. And, of course, there are definite standards concerning data sets in healthcare. We should keep in mind that the purpose of health care data sets is to identify the data elements to be collected for each patient and to provide uniform definitions for common terms.

Let's take a closer look at some of these data sets.

Examples of datasets in healthcare

The Uniform Hospital Discharge Data Set (UHDDS) was an initiative of the Department of Health, Education, and Welfare, the predecessor of today's Department of Health and Human Services (HHS). It was first implemented in 1974 and has since undergone several revisions. Though this set is specific to hospitals that provide medical services for those covered by Medicare and Medicaid, all insurance companies tend to gather information and medical records in similar styles to the UHDDS, as the value of having compatible data is clear. The UHDDS lists and defines a set of common, uniform data elements for every hospital inpatient and includes principal and previous diagnosis, the principal procedure, and other significant procedures.

The Uniform Ambulatory Care Data Set (UACDS) regulates the area of ambulatory care. The set includes reasons for the encounter, living arrangements, and marital status. The UACDS is a recommended set, not a mandatory one.

The Minimum Data Set for long term care (MDS) was published by the Department of Health & Human Services in 2013 and modified in 2016. It is a standardized, primary screening and assessment tool of health status that forms the foundation of the comprehensive assessment for all residents in a Medicare and or Medicaid-certified, long-term care facility. The set is used to collect demographic and clinical data on nursing home residents that must be completed for every resident at the time of admission and during reassessment periods. It is used to develop care plans and document placement at the appropriate care level.

Data elements for emergency department systems (DEEDS) is an initiative of the Centers for Disease Control and Prevention's (CDC) and the National Center for Injury Prevention and Control (NCIPC). DEEDS is a data set used to support the uniform collection of data at hospital-based emergency departments and to reduce incompatibilities in emergency care data. It is designed to provide uniform specifications for data elements chosen to be retained, revised, or added to their ED record systems to build reusable clinical data definitions.

The Outcomes and Assessment Information Set (OASIS) is a standardized data set designed to facilitate the rigorous and systematic measurement of patient home health care outcomes to assess the quality of home health services. It is also used as the basis of reimbursement. The set was designed to gather data about Medicare beneficiaries who are receiving services from a home health agency. It includes a set of core data items that are collected on all adult home health patients.

The Health Plan Employer Data and Information Set (HEDIS) is a set of standard performance measures designed to provide health care purchasers and consumers with the information they need to compare the performance of managed health care plans. It is used by the National Committee for Quality Assurance as a part of the accrediting process for managed care organizations. This tool is used by more than 90% of America's health plans to measure performance on important dimensions of care and service. The set includes administrative data, claims, and health record review data.

This list of datasets is, of course, not exhaustive but demonstrates the importance of a comprehensive approach to data collection and meaningful use for future data-driven healthcare. To catch up with other industries, healthcare organizations should adopt more long-term approaches to data collection and analysis. Moreover, with the rise of digital health systems, we have become more concerned about data security in healthcare.

Using big data for research and EMR analytics can benefit the whole healthcare system, as the ability to derive value from healthcare information and make well-grounded decisions have the potential to improve data quality in healthcare and patient care as well as reduce costs. It is a good idea to adopt the experience of other industries in coping with big data. It also makes sense to involve data scientists and IT companies to handle the collected data and get used out of a big data environment.

10 best healthcare datasets for data mining

There are a lot of data sources besides hospital data that can be useful for healthcare systems analytics. We have compiled a shortlist of the best healthcare data sets that can be used for statistical analysis. The list includes both free healthcare data sets and business data sets for healthcare providers.

1. The Healthdata.gov site incorporates 125 years of US healthcare data. The data include claim-level Medicare data, epidemiology, and population statistics. Here you can find not only the data sets provided by agencies across the Federal Government but also the tools and applications for data handling and processing data.

2. World Health Organization provides data and analyses on global health priorities, including health and disease statistics. Each page is dedicated to a specific topic and provides information on global situations and trend highlights. The information includes core indicators, database views, major publications and links to relevant web pages on the topic.

3. data.gov includes over 197,747 data sets which, among others, include health, public safety, and science & research data sets that come from across the Federal Government. The source provides "data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations" for the purposes of improving the health and lives of all Americans.

4. The Human Mortality Database (HMD) provides detailed mortality and population data for about 40 countries. The database is accessible for researchers, students, journalists, policy analysts, and others interested in the history of human longevity.

5. Data and Tools of the National Center for Health Statistics include public-use data files and documentation, and restricted data. The site also provides data tools and data analysis aids, as well as data visualization for the general public, survey participants, researchers, and students.

6. openFDA, launched by the U.S. Food and Drug Administration allows developers to access public FDA data through open APIs, provides raw data downloads, and offers documentation and examples. The available dataset includes reports on the adverse events of drugs, such as side effects, product use errors, product quality problems, and therapeutic failures. You can also find data on drug product labeling.

7. The Big Cities Health Inventory Data Platform by the Big Cities Health Coalition is an open data platform that allows users to access and analyze health data from 26 cities, for 34 health indicators, and across six demographic indicators. The latest version of the platform features over 17,000 data points across 28 large cities, allowing users to examine a number of pressing health issues impacting urban communities across the country.

8. Medicare.gov offers you a wealth of databases available to download. The available databases include 2018 Drug and Health Plan Data, 2018 Medigap Data, Hospital Compare and much more.

9. US Census Bureau is another leading source of quality data about people, business and the economy of the USA. It provides demographic data at the state, city, and even zip code level.

10. National Cancer Institute provides data sets on cancer incidence segmented by age, race, gender, year, and other factors. Additional datasets include Standard Population Data, U.S. Mortality Data, and U.S. Population Data. The platform also provides statistical software.

Data mining is very promising for the healthcare industry as it can identify the most useful data sources and give insights into how to use them most efficiently not forgetting about patient safety. Your facility can use data mining and analytics to answer the questions you already have and to identify inefficiencies and best practices that can improve care and reduce costs for your healthcare system.

Take advantage of your data now to get the most value out of it. By involving IT professionals experienced in managing and leveraging healthcare data, you can deliver the solutions necessary to make informed decisions in the state-of-the-art, healthcare business environment.

Contact our team at studios-info@cprime.com for more information.

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Healthcare data sets include a vast amount of medical data, various measurements, financial data, statistical data, demographics of specific populations, and insurance data, to name just a few, gathered from various healthcare data sources. Let's look into how data sets are used in the healthcare industry.

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I found this information exciting, and maternal health is vital to me. Telehealth is becoming more popular, and we are bridging the gaps to increase quality care for our patients who may not have access to hospitals or reliable transportation. Telehealth can be used as a monitoring system for patients at risk of complications. Patients can visit with specialties and subspecialties providers to diagnose and treat patients from afar. I hope that when I get into practice, I can make a difference in reducing maternal mortality and death by integrating telehealth into routine visits.

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Official websites use .gov
A .gov website belongs to an official government organization in the United States.

‹ For providers › Best practice guides

Print this page

Maternal health care is critical for the long-term health and success of parents and children. Health equity in maternal care has long been a struggle, especially for those in rural and underserved communities. Telehealth is one way to bridge those gaps.

This guide will highlight the needs and procedures for a successful maternal telehealth program.

The United States offers some of the most advanced medical technologies in the world. But our maternal morbidity rate still lags far behind other high-income countries.

The current maternal mortality rate is 17.4 deaths per 100,000 live births, according to the Centers for Disease Control and Prevention. And Black mothers are 2.5 times more likely to die from pregnancy or childbirth causes than white women. Overall, more than 65 percent of pregnancy-related deaths are preventable.

Additionally, pregnant patients are at higher risk of maternal mortality from COVID-19 infection during pregnancy. Complications associated with a COVID-19 infection during pregnancy include: more severe forms of COVID-19, pre-term birth, and transmission of COVID-19 to newborns.

Types of Maternal telehealth

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Introduction to telehealth for maternal health services | Telehealth.HHS.gov

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Official websites use .gov A .gov website belongs to an official government organization in the United States. ‹ For providers › Best practice guides Print this page Maternal health care is critical for the long-term health and success of parents and children.

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The need for tlehealth has increased and many specialities and subspecialties use it for clinical diagnostic purposes. This article talks about how the global market of telehealth is growing and how the market is increasing huffily. It shows the need and the growth in the year to come.

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PUNE, India, Aug. 1, 2019 /PRNewswire/ -- The global Telehealth Market is likely to expand considerably with impetus from the ability of telehealth to serve the rural population. According to a report published by Fortune Business Insights, titled "Telehealth Market: Global Market Analysis, Insights, and Forecast, 2019-2026," the market was valued at US$ 49.8 Bn in 2018. Fortune Business Insights has predicted that the market will reach US$ 266.8 Bn by 2026, thereby exhibiting a CAGR of 23.4%.

Telehealth Market Analysis, Insights and Forecast, 2015-2026

The ability of telehealth to access and manage healthcare services has led to their rising uptake across the world. Telehealth is used to enhance healthcare services as well as increase feasibility within the healthcare services. Recent technological advancements in telehealth services have created huge potential for growth of the global Telehealth Market for the forthcoming years. The ability of telehealth services and products to reach out to rural areas and people who cannot access healthcare facilities has created a huge demand across the world. Resulting from such exceptional benefits for telehealth products, investors have identified this market as a gem and have identified several growth opportunities for the Telehealth Market companies around the world. Additionally, applications of telehealth in improving coordination and communication among the healthcare team will contribute to the increasing demand for telehealth products across the world.
Browse Complete Report Details with Table of Content and Figures: Technological Advancements to Fuel Demand for Telehealth Services https://www.fortunebusinessinsights.com/industry-reports/telehealth-market-101065 Company Collaborations Are Proving Chief Growth Drivers
The technological advancements in telehealth products and services have fuelled the demand for telehealth services across the world. The advent of user-friendly systems has contributed to a high demand, which in turn has led to an increase in the global Telehealth Market value in recent years. In 2019, InTouch announced the launch of a fully integrated end-to-end virtual platform aimed at providing enhanced patient care solutions. 'Solo' was a flexible platform integrated with services such as enhanced emergency care solutions, direct-to-patient, and direct-to-customer, which in turn will contribute to the growth of the global Telehealth Market in the coming years.
Sample PDF https://www.fortunebusinessinsights.com/enquiry/request-sample-pdf/telehealth-market-101065 Telehealth Market Key Players

American Well

GlobalMed

Teladoc Health, Inc.

Dictum Health, Inc., LLC

InTouch Technologies, Inc.

Doctor On Demand, Inc.

MDLIVE Inc.

Encounter Telehealth

HelloMD

SnapMD, Inc.

Other players

Speak to Analyst Table of Content:

Market Dynamics

Market Drivers

Market Restraints

Market Opportunities

Key Insights

Overview of Telehealth Services

Regulatory Scenario - For Key Countries

Reimbursement Scenario - For Key Countries

New Product Launch

Overview of Countries with Telehealth Policies

Technological Advancements in Telehealth Market

Startups with their Funding Overview

Key industry Developments - Mergers, Acquisitions and Partnerships

Global Telehealth Market Analysis, Insights and Forecast, 2015-2026

Key Findings / Summary

Market Analysis, Insights and Forecast - By Type

Market Analysis, Insights and Forecast - By Application

Telemedicine

Patient Monitoring

Continuous Medical Education

Others

Market Analysis, Insights and Forecast - By Modality

Store-and-forward (Asynchronous)

Real-time (Synchronous)

Remote Patient Monitoring

Market Analysis, Insights and Forecast - By End User

Healthcare Facilities

Homecare

Others

Market Analysis, Insights and Forecast - By Region

Competitive Analysis

Strategic Recommendations

Request for Customization: Browse Related Report:

Telemedicine Market Size, Share and Global Trend By Type (Products, Services), By Application (Teleradiology,m Telepathology, Teledermatology, Telepsychiatry, Telecardiology, Others), By Modality (Store-and-forward (Asynchronous) , Real-time (Synchronous), Others) and Geography Forecast till 2026

Mass Spectrometert Market Size, Share and Global Trend By Product (Hybrid, Single), By End-user (Biopharmaceutical and Pharmaceutical Companies, Research Institutes & Government Organization, Hospitals & Diagnostic Centers, Others (Environmental testing, food, and chemical testing)) and Geography Forecast till 2026

Rehabilitation Robots Market Size, Share and Global Trend By Type (Therapeutic Robots, Exoskeleton Robots, Assistive Robots, Others), By End User (Rehabilitation Centers, Hospitals, Others) and Geography Forecast till 2026

Pain Management Devices Market Size, Share and Global Trend By Product Type (Electrical Stimulation Devices, RF Ablation Devices, Neuromodulation Devices, Infusion Pumps), By Application (Musculoskeletal, Brain, Cancer, Neuropathy, Others), By End User (Physiotherapy Centers, Hospitals and Clinics, Others) and Geography Forecast till 2026

About us: https://www.fortunebusinessinsights.com/enquiry/customization/telehealth-market-101065 https://www.fortunebusinessinsights.com/enquiry/speak-to-analyst/telehealth-market-101065
Due to the increasing demand, Telehealth Market companies are adopting collaborative strategies with the aim of expanding business on a global scale. Furthermore, leading companies in the telehealth industry are focusing on mergers and acquisitions to gain competitive strength. Companies are trying to incorporate modern industrial concepts with a view to gain more end users. Fortune Business Insights expects market collaborations will help companies generate substantial Telehealth Market revenue. In April 2018, American Well acquired Avizia Limited with the aim of strengthening its position among the companies in the telehealth industry. American Well's acquisition of Avizia will help the company take a big leap in the Telehealth Market, owing to the exceptional portfolio of telehealth services bolstered by Avizia. The report includes company mergers, similar to American Well's latest acquisition and signifies the impact of such M&As on the global Telehealth Market.

Fortune Business Insights highlights some of the prominent Telehealth Market companies. This report analysis includes market dynamics and competitive landscape. Various key insights provided in the report are the regulatory scenario for key countries, health reimbursement overview, telehealth services overview, new product launches, an overview of countries with telehealth policies, technological advancements, recent industry developments such as mergers & acquisitions, and others.

Fortune Business Insights offers expert corporate analysis and accurate data, helping organizations of all sizes make timely decisions. We tailor innovative solutions for our clients, assisting them address challenges distinct to their businesses. Our goal is to empower our clients with holistic market intelligence, giving a granular overview of the market they are operating in.

Our reports contain a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our team of experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies, interspersed with relevant data.

At Fortune Business Insights we aim at highlighting the most lucrative growth opportunities for our clients. We therefore offer recommendations, making it easier for them to navigate through technological and market-related changes. Our consulting services are designed to help organizations identify hidden opportunities and understand prevailing competitive challenges.

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SOURCE Fortune Business Insights

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Telehealth Market to Cross US$ 266.8 Bn by 2026; Recent Technological Advancements in Healthcare Equipment to Provide Impetus, Says Fortune Business Insights

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PUNE, India, Aug. 1, 2019 /PRNewswire/ -- The global Telehealth Market is likely to expand considerably with impetus from the ability of telehealth to serve the rural population. According to a report published by Fortune Business Insights, titled "Telehealth Market : Global Market Analysis, Insights, and Forecast, 2019-2026," the market was valued at US$ 49.8 Bn in 2018.

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I found this video to be exciting as using gamification to change behaviors. The many apps we have and use for fitness or weight loss never thought of it that way. Now in my workplace, we have the BE WELL application and tool, and we link watches through apps to track physical activity and require CBTs to earn money to spend at the beginning of the year.

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I found this an interesting aspect of why some games fail in healthcare. They speak about badges and points that aren't what is necessary. The critical piece is the challenge needed to inspire and spark interest in players. Make the game unique and challenging, so when designing an application, think about that.

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What do people get wrong about gamification in healthcare?

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This talks about how technology is supposed to help with alert fatigue but that, as humans, we tend to ignore it, or it's an overwhelming response of various alarms that go off healthcare providers and clinicians who are ignoring safety features out of habit.

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Background

The rapidly increasing computerization of health care has produced benefits for clinicians and patients. Yet the integration of technology into medicine has been anything but smooth, and as newer and more sophisticated devices have been added to the clinical environment, clinicians' workflows have been affected in unanticipated ways. These fundamental shifts have resulted in new threats to patient safety-a cruel irony given that technological solutions have been promoted for many years as the most promising solution to medical errors.

Most health care technologies-be they computerized provider order entry systems (CPOE), smart intravenous infusion pumps, or cardiac monitoring devices-provide auditory or visual warnings to clinicians to prevent or act on unsafe situations. These warnings are well intended and in isolation may be helpful. However, in the current highly computerized clinical environment, an individual clinician interacts with many different alert-generating devices-meaning that every day, clinicians are on the receiving end of a staggering number of alerts. A 2014 study found that the physiologic monitors in an academic hospital's 66 adult intensive care unit beds generated more than 2 million alerts in one month, translating to 187 warnings per patient per day. In a study in the Veterans Affairs primary care, clinicians received more than 100 alerts per day.

The term alert fatigue describes how busy workers (in the case of health care, clinicians) become desensitized to safety alerts, and as a result ignore or fail to respond appropriately to such warnings. This phenomenon occurs because of the sheer number of alerts, and it is compounded by the fact that the vast majority of alerts generated by CPOE systems (and other health care technologies) are clinically inconsequential-meaning that in most cases, clinicians should ignore them. The problem is that clinicians then ignore both the bothersome, clinically meaningless alarms and the critical alerts that warn of impending serious patient harm. In essence, a proliferation of alerts that are intended to improve safety actually results in a paradoxical increase in the chance patients will be harmed. Although little discussed prior to the widespread use of electronic medical records, alert fatigue is now recognized as a major unintended consequence of the computerization of health care and a significant patient safety hazard.

Effect of alert fatigue on patient safety

Much of the literature on alert fatigue derives from studies of CPOE and clinical decision support systems, in which alerts are provided to warn of potentially harmful drug-drug interactions or incorrect medication doses. These studies consistently show three main findings:

Alerts are only modestly effective at best. A systematic review of computerized reminders found only minor improvements in targeted processes of care, and, while CPOE systems have been shown to markedly decrease prescribing errors, this can largely be ascribed to their ability to standardize drug doses, provide decision support, and eliminate errors from poor handwriting or incorrect transcriptions.

Alert fatigue is common. Clinicians generally override the vast majority of CPOE warnings, even "critical" alerts that warn of potentially severe harm. There is less literature on other types of warnings, but it is likely that rates of overriding or ignoring warnings in other settings are also high.

Alert fatigue increases with growing exposure to alerts and heavier use of CPOE systems. This finding is intuitive, but also raises the important implication that without system redesign, the safety consequences of alert fatigue will likely become more serious over time.

Although there are few studies that quantify adverse events related to alert fatigue, this phenomenon has been implicated as a significant cause in several high-profile errors. A 2011 Boston Globe investigation identified more than 200 deaths over a 5-year period attributable to failure to appropriately heed alarms from physiologic monitoring systems. A recent book by a prominent patient safety leader details how a hospitalized teenager received a 38-fold overdose of an antibiotic, in large part because the ordering physician had been advised by colleagues to "just ignore the alerts."

Current context

Although only recently recognized, alert fatigue (and the unintended consequences of the computerization of health care) has become a high profile patient safety issue. The Joint Commission released a sentinel event alert in April 2015 calling for health care organizations to pay close attention to information technology as a safety issue. In order to mitigate these consequences-including alert fatigue-The Joint Commission recommended improving the culture of safety by creating a shared sense of responsibility between users and developers, paying careful attention to safe IT implementation, and engaging leadership to provide oversight of health IT planning, implementation, and evaluation.

There is intense interest in developing specific methods to combat alert fatigue, but as yet, there is no consensus on the optimal approaches. Solving alert fatigue will require use of the principles of human factors engineering as well as those of informatics, as the problem fundamentally arises from both the technology itself and how busy human beings interact with the technology. A WebM&M provided several suggestions on how to minimize alert fatigue in CPOE systems:

Increase alert specificity by reducing or eliminating clinically inconsequential alerts.

Tailor alerts to patient characteristics and critical integrated clusters of physiologic indicators. For example, incorporate renal function test results into the alert system so that alerts for nephrotoxic medications are triggered only for patients at high risk.

Tier alerts according to severity. Warnings could be presented in different ways, in order to key clinicians to alerts that are more clinically consequential.

Make only high-level (severe) alerts interruptive.

Apply human factors principles when designing alerts (e.g., format, content, legibility, and color of alerts).

A quality improvement program in the Veterans Affairs system that incorporated the above principles and provided primary care physicians with education on managing alerts achieved a small but meaningful reduction in alerts.

One limitation to addressing alert fatigue pertains to the legal consequences of removing alerts. A recent commentary pointed out that system developers have thus far been reluctant to remove alerts for fear of being held liable if patients were harmed in the absence of a warning. There has been progress toward developing guidelines for high-severity alerts (which warn of significant risk of harm and should be retained) and low-severity alerts (less clinically consequential warnings, which could be made non-interruptive or removed entirely).

In solving the problem of alert fatigue, health care will need to look to examples from other industries. The aviation industry offers a sharp contrast to health care, because cockpit technology is rigorously designed to provide only highly consequential alerts to pilots, minimizing minor alerts in order to allow pilots to maintain situational awareness. This use of human factors engineering and deep attention to the experience of the end-user has thus far been lacking in health care technology design.

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Alert fatigue (alarm fatigue) is when healthcare workers become desensitized to electronic safety alerts. Alert fatigue can lead to medical errors and adverse events.

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One the nurse was very rude and short, never explained what she was doing with the child to the parents. She put some leads on the child and it was alarming, she ignored it and did not check on the baby due to her negligence. This is very scary but shows a troubling example of alarm fatigue.

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The concerns of privacy and security are valid. There have certainly been breaches throughout computer software that were concerning and scary to access consumers' social security numbers and policy numbers. Having one's medical information and identification to sell or use is a concern, and while using technology, one can't 100% say that a record or software is safe from hacks and breaches. This article discusses the concerns and ways we can secure privacy and security in electronic health records using a team effort.

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Abstract

Electronic Medical Records (EMRs) can provide many benefits to physicians, patients and healthcare services if they are adopted by healthcare organizations. But concerns about privacy and security that relate to patient information can cause there to be relatively low EMR adoption by a number of health institutions. Safeguarding a huge quantity of health data that is sensitive at separate locations in different forms is one of the big challenges of EMR. A review is presented in this paper to identify the health organizations' privacy and security concerns and to examine solutions that could address the various concerns that have been identified. It shows the IT security incidents that have taken place in healthcare settings. The review will enable researchers to understand these security and privacy concerns and solutions that are available.

Keywords

Electronic health records

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Security and privacy of electronic health records: Concerns and challenges

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Electronic Medical Records (EMRs) can provide many benefits to physicians, patients and healthcare services if they are adopted by healthcare organizations. But concerns about privacy and security that relate to patient information can cause there to be relatively low EMR adoption by a number of health institutions.

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Patient safety issues with VA Cerner EHR caused harm to veterans, federal watchdog says

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The article discusses how A Veterans hospital in Washington state launched a new electronic health record. Where did it go? When the physicians were ordering labs and diagnosgtic studies the recipients never received them. The OIG review found that the new EHR sent thousands of orders for medical care to an undetectable location, or unknown queue, instead of the intended care or service location, effectively causing the orders to disappear without letting clinicians know they weren't delivered. This situation is scary; not only was the patients' safety in the delay of care but where did the information go?

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This article discusses the importance of data analytics. Data Analytics examines raw datasets to find trends, draw conclusions and identify the potential for improvement. The pandemic data analytics played a huge role in predicting COVID and hospital stay outbreaks. It helped hospitals predict and prepare to take on patients needing hospital care, and the numbers within the commnnity showed covid outbreaks. There are three types of analytics descriptive, predictive, and prescriptive.

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Data Analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level.

The use of health data analytics allows for improvements to patient care, faster and more accurate diagnoses, preventive measures, more personalized treatment and more informed decision-making. At the business level, it can lower costs, simplify internal operations and more.

What is health care data?

In order to discuss health data analytics and the role it plays in the health care sector, we must first understand the data that is being collected and analyzed. There is data being collected on the processes and procedures of the business side of health care, but there is also an enormous amount of health data being gathered, stored and analyzed.

Health data is any data relating to the health of an individual patient or collective population. This information is gathered from a series of health information systems (HIS) and other technological tools utilized by health care professionals, insurance companies and government organizations.

We are able to see a holistic view of each individual patient as well as trends tied to location, socioeconomic status, race and predisposition. The information being collected can be broken down into specific datasets that can then be analyzed.

There are a variety of tools and systems used to collect, store, share and analyze health data gathered through various means. These tools include:

Electronic Health Records (EHRs)

Personal Health Records (PHRs)

Electronic Prescription Services (E-prescribing)

Patient Portals

Master Patient Indexes (MPI)

Health-Related Smart Phone Apps and more

With digital data collection, there is more and more health care data to be analyzed every second. With the increase of electronic record keeping, applications and other electronic means of data collection and storage, there is a significant amount of data being collected in real time.

These data sets are so complex that traditional processing software and storage options cannot be used. Cloud storage is a necessity when dealing with "Big Data." Cloud storage is built to be secure, an absolute must when dealing with sensitive patient information. It is also very cost-efficient and has been helpful in lowering the increasing cost of health care.

Big Data Analytics and COVID-19

The impact COVID-19 has had on the health care industry is evident to anyone and everyone. You don't need to be plugged into the world of medicine to see what has been happening worldwide during this pandemic.

What most people don't see, though, is the impact COVID-19 has had on health care data analytics. "Big data tools have played an increasingly significant role in health care decision-making" says HealthITAnalytics. It is not just providers, but lawmakers and researchers who are turning to big data analytics and predictive models to help allocate resources, predict surges, improve patient care and outcomes and employ preventive measures.

Big data and health data analytics have played an integral role in the fight against COVID-19. The data is coming in at a near constant rate. Analyzing that health data has allowed for a better understanding of how to respond and treat patients.

This pandemic has resulted in an enormous surge of health data being recorded and manipulated allowing for bigger and better analytics. Unfortunately, we are also seeing that COVID-19 is " shining a harsh spotlight on health care's biggest issues. " There are a lot of obstacles when it comes to sharing health data across organizations and a distinct lack of standardization in the way that data is collected and analyzed.

This widespread problem was evident in the early days of the pandemic as conflicting and ever-changing information was being presented to the public. We saw a turn towards disbelief when it came to COVID-related information with many still believing misinformation and previously held beliefs on how this virus should be handled.

The spotlight that COVID-19 shined on these problems, however, will allow for them to be rectified. The providers, researchers and policymakers can learn from these mistakes and work towards a better, more standardized solution for big data in health care.

The Importance of Health Care Data Analytics

We can collect all the data we want, but it doesn't do any good if we don't know what to do with that information. We need a centralized, systematic way of collecting, storing and analyzing data so we can use it to our advantage.

The collection of data in health care settings has become more streamlined in recent years. Not only does the data help improve day-to-day operations and better patient care, it can now be better used in predictive modeling. Instead of just looking at historical information or current information, we can use both datasets to track trends and make predictions. We are now able to take preventive measures and track the outcomes.

The fee-for-service style of health care is becoming a thing of the past. There is a growing demand for patient-centric, or value-based, medical care which has led to a considerable shift towards predictive and preventive measures in regards to public health in recent years. Data makes this possible. Instead of simply treating the symptoms as they present, practitioners are able to identify patients at high risk of developing chronic illnesses and help to treat an issue before it surfaces. This helps to lower costs for the practitioner, insurance company and patient as the preventive treatment may help to stave off long-term issues and expensive hospitalizations.

If hospitalization is necessary, data analytics can help practitioners predict risks of infection, deterioration and readmission. This too can help lower costs and improve patient care outcomes.

Consider the impact this has had on the COVID-19 pandemic. The data being collected is analyzed in real time to understand the effects of the virus better and predict future trends so we may slow the spread and prevent future outbreaks.

Types of Health Care Analytics

Not every question can be answered by using the same analysis of the data. Through the use of different types of big data analytics, we can answer many of the questions being asked in health care settings.

Descriptive Analytics

Descriptive analytics uses historical data to draw comparisons or discover patterns. This type of analysis is best for answering questions about what has already occurred. We can gain insight into the past with descriptive analytics.

Predictive Analytics

Predictive analytics uses current and historical data to make predictions about the future. The models created with this type of analytics are best for answering questions about what could happen next. We can gain insight into the future with predictive analytics.

Prescriptive Analytics

Prescriptive analytics will also make predictions about future outcomes. Machine learning is a big factor with this type of analytics. The information provided can help determine the best course of action. We can gain insight on what course of action should be taken to reach the most ideal outcome with prescriptive analytics.

How Data Analytics Leads to Health Care Solutions

Health care data management has the potential to lead to better care if used properly. With centralized datasets, there is immediate access to necessary information whenever and wherever it is needed. The addition of big data analytics improves efficiency on all fronts. Better data leads to better care.

Predictive Modeling

Predictive modeling is the process of analyzing current and historical data to predict future outcomes. Models use data mining, machine learning and statistics to identify patterns and predict outcomes. Predictive models built off of the health data being collected provide solutions on the macro and micro level.

The use of predictive analytics can alert health care professionals to potential risks. By analyzing behavioral data, we can predict treatment outcomes, potential risks for chronic illness and even predict risk of self-harm. The health data collected can be used for risk scoring, readmission prediction and prevention, predicting infection and deterioration and so much more at the individual patient level.

Predictive modeling can also be used on a much larger scale. Population health management is impossible without the use of these models. Outbreaks can be predicted, outcomes can be predicted, and in knowing what is to come, preventive measures can be taken.

Predictive modeling can even be used in administrative applications to increase efficiency and lower costs for all.

Reductions in Health Care Costs

Health care is expensive. And, those costs only continue to increase across the board. We are, however, seeing a shift from fee-for-service payment models to value-based care.

Through the use of predictive and prescriptive analytics, health care organizations and practitioners can get detailed models for lowering costs and patient risk. In addition to the patient-centric benefits mentioned above, health data analytics can reduce appointment no-shows, manage supply chain costs, prevent equipment breakdowns and decrease fraud.

The Growing Role of the Health Data Analyst

Health Data Analysts take the health care data being collected and use skills such as data acquisition, data management, data analysis and data interpretation to provide actionable insights. The role of big data in health care and the increasing need for improvement in the health care sector has led to a higher demand for qualified health data analysts.

The role of a health data analyst varies based on their position and industry of choice. Regardless of industry, a health data analyst will need to be able to work with, develop and evaluate health information technology (health IT) and other health information systems (HIS).

They may also be expected to:

Collect or mine data

Examine current and historical data

Evaluate raw data

Build predictive models

Automate reports

Required Skills for Health Data Analysts

Health data analysts solve problems for the organizations that hire them. In order to do so, they should be well versed in several essential skills:

Structured Query Language (SQL)

Excel

Statistical Programming

Data Visualization

You should also have certain soft skills as a health data analyst such as:

Analytical thinking

Creative thinking

Attention to detail

Strong communication skills

Schooling for Health Data Analysts

While it is not a requirement to become a health data analyst, many jobs will require a bachelor's degree in health information management (HIM) or a related field (data science, mathematics, health informatics, statistics, etc.).

A master's degree is, again, not a requirement to work as a health care data analyst, but it will qualify you for higher, more competitive positions. You can earn your master's in data analytics, health informatics, or another related field.

Where Will A Health Data Analyst Be Employed?

Numerous industries and organizations utilize health data and require the help of health data analysts. These include:

Hospitals, private or public

Government health care departments

Diagnostic centers

Health insurance companies

Health care consulting companies

Large medical practices

Health IT vendors

Other health organizations

Depending on the industry or role they choose to take, health care data analysts may work as part of larger teams or alone.

How Much Do Health Data Analysts Make?

Your salary, much like your role responsibilities, will vary based on the industry or organization for which you choose to work. Payscale lists the median salary for a health data analyst as $63,000 per year. The U.S. Bureau of Labor Statistics doesn't have an entry specific to Health Data Analysts, however, the Medical and Health Services Manager page does list "health information manager." The median pay listed here is closer to $100,000.

Take the Next Step in Your Career

The University of Pittsburgh School of Health and Rehabilitation Sciences offers a Master of Science in Health Informatics degree with a Data Science track. If you want to enter the field of health informatics or health data analytics or open up your career options, this master's degree can make you a competitive candidate.

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The Role of Data Analytics in Health Care

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Data Analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level.

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The information formally educates us on how we use health analytics in healthcare; we use data analytics through preventative care to identify risk factors that may correlate to a disease or condition. It can also help with predictions of possible conditions or diseases. Health analytics is vital in healthcare today. It aids in staffing issues. The predictions could aid in how busy a hospital could potentially be ranging from 7 to 14 days. This is huge in creating a schedule and getting ahead on staffing needs. This also helps with patient engagement improvements. It also helps with population health management , EHR use with medical providers and patients.

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Table of Contents

The following topics are thoroughly covered in this article:

1. What is healthcare analytics?

2. Examples of analytics in healthcare

3. How analytics benefits providers, patients and payers

4. How data analytics can support your hospital or healthcare systems' cost savings and financial improvement goals

What is Healthcare Analytics?

Analytics is becoming increasingly prevalent across nearly every industry, which is a trend that's notably apparent within the healthcare industry. The provision of modern treatment methods and techniques relies on data analytics. When implemented correctly, analytics can predict outbreaks, bolster preventive care, enhance the overall quality of care that patients receive, and lessen the costs associated with treatments.
When you hear the word "analytics", this means that an ample amount of data is being digitized and further consolidated, which helps with analysis of the data. In disparate pieces, the data may not mean much. However, when put together, the information can tell a story that allows healthcare professionals to treat illnesses early or identify the initial warning signs for a disease.
By improving the lives of patients, data analytics also helps to reduce expenses for hospitals. Healthcare analytics involves numerous intricacies that differ from other industries. For instance, real-time vital signs and electronic health records are taken into account when performing analytics. This data can be added to social determinants of health data to proactively manage patients when they leave a hospital. Because of the complex processes involved with healthcare analytics, an agile analytics solution is necessary that is supported by subject matter healthcare experts.
By improving the lives of patients, data analytics also helps to reduce expenses for hospitals. Click To Tweet

Examples of Analytics in Healthcare

To better understand healthcare analytics and all that it entails, it's important to take a look at various examples of analytics in healthcare. The comprehensive data that can be collected through healthcare analytics can be converted into numerous insights. The following are some top examples of how analytics can be used within the healthcare industry.

Preventive Care

The key to preventive care is to identify risk factors in someone who may be susceptible to a specific condition or disease. For instance, anyone who smokes cigarettes on a regular basis will have a higher risk of developing lung diseases like COPD, asthma, and lung cancer. Healthcare analytics can be used to effectively identify risk factors that might not have previously been tied to the disease or condition in question.
Back in 2017, Blue Cross Blue Shield analyzed many years of pharmacy and insurance data pertaining to opioid abuse and overdose. Through this analysis, they were able to identify nearly 750 risk factors that accurately predict if someone is at risk of abusing opioids. Gathering all of this data was only possible with the help of analytics experts and the right software solutions.

In the same vein as preventive care, data analytics is also being used for predictive purposes. Companies like Optum Labs have collected electronic health records of millions of patients, which have then been stored in a database. From this database, it's possible to use the data to strengthen patient care and make it more efficient. When analytics are used in a healthcare setting, doctors can make immediate decisions that are driven largely by data that's available through analytics. In certain situations, this data can be used to predict the initial onset or eventual progression of various conditions.

Staffing

One manner in which hospital data analytics can be highly beneficial is with staffing. Without the use of analytics, staffing issues can result from under-staffing or over-staffing. When the hospital is under-staffed, the patients aren't provided with the level of care that they require. On the other hand, over-staffing issues result in higher expenses and a loss of money for the hospital. Using data analytics allows your hospital to successfully avoid such problems by analyzing months or years of hospital admissions records.
When healthcare data analytics is performed on these records, it's possible to identify patterns and accurately predict what admissions will be like at the hospital around 7-14 days in advance. This data can then be used to schedule a higher amount of staff during times when greater admissions are expected. This should lead to less wait times for the patient as well as better overall care.

Patient Engagement Improvements

Using data analytics, healthcare has been shown to improve patient engagement as well. A large number of prospective patients wear smart devices that are able to track numerous metrics like sleep patterns, heart rates, and number of steps taken. Through these metrics, patients are monitoring their overall health. It's possible for the data that's collected from these devices to be uploaded to the cloud, which provides doctors and physicians with the ability to look over the data and draw conclusions from it.
If a patient suffers from hypertension or asthma, the data that's sent to the physician from the wearable device can be used to identify the proper treatment plan and to make sure that the patient is taking the steps necessary to correct their symptoms and lessen their need for medical care. When used correctly, patients should be able to reduce the number of visits that they need to take to their primary doctor. The doctor could also identify a potential risk factor or problem with the data that requires further treatment on an in-person basis, which could allow for preventive care to be administered.

Electronic Health Records

Hospital data analytics is mainly used for electronic health records, which are currently kept by nearly 95 percent of all hospitals across the U.S. A digital health record is an extensive record that includes even the smallest of details pertaining to the patient's health. Along with standard demographic information and medical histories of family members, electronic health records also document allergies, medications, test results, and past diagnoses.
Each record is comprised of a single file, which makes it easy to avoid duplicate records and excess paperwork. These records can be kept for many years without needing to worry about a loss of data or similar problems that could result in incomplete records. Electronic health records are only possible with data analytics healthcare, which provides hospitals with a simple tool for the collection and analysis of copious amounts of data.
Keep in mind that a core benefit of electronic health records is that they can be instantly accessed by different healthcare providers and hospitals, which eliminates the need for sending patient information from one medical facility to another. With the kind of data that's collected in an EHR, lab tests, and office visits, a patient's healthcare can be optimized.

How HealthCare Data Analytics is Used by Medical Providers

In the past, most healthcare providers relied on a fee-for-service model that was straightforward and efficient in practice, but had its drawbacks. With this particular model, doctors received very little incentive to provide value-based care that was more holistic in nature.
Without using hospital data analytics, the value-based care model can be more inefficient than its fee-based counterpart. Hospitals are required to improve efficiency and day-to-day operations with the value-based model, which is where analytics is used. When an analytics solution is implemented, medical providers should be able to:

Improve performance with quality care that's backed up by data

Enhance quality of care and patient satisfaction by automating processes like processing insurance and making appointments

Perform predictive analytics for at-risk patients, which lessens readmission rates

When looking specifically at patient care, data analytics healthcare can use patient histories and patterns to determine which treatments are necessary. Predictive analytics can hone in on patients who currently suffer from chronic conditions and may be more at risk of being involved in an emergency medical situation. The decisions that are made from this data can increase corrective care while decreasing the number of emergency visits.
Keep in mind that human error is another significant problem that hospitals face when administering medical care. The majority of insurance claims that are successfully filed against a hospital are brought about by human error, which could involve anything from an incorrect diagnosis to the wrong dosage with a medication.
When these errors occur, hospitals and other healthcare facilities will be tasked with absorbing some of the claims and insurance costs that arise. Hospital data analytics can look over patient data and any prescribed medication to alert doctors and patients of incorrect dosages or wrong prescriptions, which lessens human error and the cost to your hospital.

How Healthcare Data Analytics Helps with Population Health Management

With population health management, a greater focus is being placed on predictive and preventive care as opposed to reactive care that centers around a response and treatment. When predictive analytics are implemented, a chronic illness could be identified while still in the early stages. Catching illnesses early on reduces the costs of care and lessens the possibility of repeat hospitalization. Some of the data that's collected and analyzed with data analytics healthcare extends to claims data, health data, social factors, and the results from lab testing.
When large amounts of data are collected and analyzed, healthcare data analytics can:

Measure potential patient outcomes to determine which treatments and programs have the highest likelihood of success

Track patient satisfaction and provider performance to better help with resource allocation

Provide the correct patient/provider ratios depending on the situation and number of admissions

The use of analytics in healthcare assists with keeping financial mismanagement at bay. By reducing over-staffing and under-staffing issues, patients should be provided with the appropriate level of care without long wait times. More efficient care equates to higher patient satisfaction and lower bed shortages.

How data analytics can support your hospital or healthcare systems cost savings and financial improvement goals

These are four ways a hospital can utilize data analytics to achieve cost savings:
1. Run 12 to 18 month PO detail report and benchmark each line item

1. This should be performed on a quarterly basis because pricing does change frequently on supply and implant expenses and because new products are utilized; this is a great way to identify off contract spend before it increases your costs unnecessarily.

2. The key to getting best pricing is to have a constant quarterly monitoring review and even from 2 different benchmarking resources; different companies will have access to different data and you want to see all of the opportunities available to your organization.

2. Identify utilization improvement opportunities through comparing physician variations in testing, supplies and specifically implant costs in the O.R.

1. Physician engagement is a leading (and underutilized) strategy for hospitals to deploy.

2. Given the right data and understanding of the benchmarks, they will be one of the best resources your hospital can utilize for cost savings.

3. Analyze your professional hourly rates to identify ways to reduce these outsourced hourly fees and standardize throughout your hospital and health system.

1. Organize hourly rates for professional services by specific category and have a one hourly rate allowable for specific services. Project management is one hourly rate, technical analyst is another rate, etc.

2. Engage an outside expert resource to provide you with accurate and competitive hourly rates. Most organizations have the ability to save over 20% in this area of spend with a focused initiative on hourly professional services rates and having the right benchmarking partner to achieve better pricing.

3. Take an area of spend in your purchased services, like regulated medical waste or medical records or specialty reference lab, and analyze the last 12 months of invoice line item spend for utilization improvement, invoice accuracy to the agreement, and pricing opportunities that can be achieved through an experienced benchmarking resource.

1. Since services spend is detailed only on an invoice, the best approach is to work with a company, like VIE Healthcare, who has patented technology to extract and analyze your purchased services invoice line item details.

2. Review ways to reduce utilization or consolidate if you have multiple service vendors providing the same service.

At VIE Healthcare Consulting, we focus on delivering non-labor cost savings. We are the preferred advisory solution for hospitals and healthcare systems to increase operating margins, reduce costs, and improve efficiencies.
We are a team of margin improvement experts, cost consultants, healthcare business analysts, data scientists, technology specialists, and clinicians. We deliver real cost savings and financial results - since 1999 we have saved our clients over $1 Billion in non-salaried expenses.
If you are interested in learning more, the quickest way to get your questions answered is to contact one of our margin improvement experts or email Lisa Miller at lmiller@viehealthcare.com.

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How Data Analytics is Used in Healthcare

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The following topics are thoroughly covered in this article: What is healthcare analytics? Examples of analytics in healthcare How analytics benefits providers, patients and payers How data analytics can support your hospital or healthcare systems' cost savings and financial improvement goals Analytics is becoming increasingly prevalent across nearly every industry, which is a trend that's notably apparent within the healthcare industry.

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This blog shows data visualization's importance by looking at xrays, ekg strips, and heart rate. To the untrained eye, it would be unrecognizable if you looked at it in a data set. So we use data visualization for this in diagnosing and evaluating patients. We also use charts and graphs as well.

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The fact that data and data visualization are everywhere in today's world, makes me wonder how data visualization impacts medical applications and what's their role within patients' diagnoses? Undoubtedly, patients trust their wellness on healthcare professionals' criteria and decisions but what do doctors, nurses, practitioners, and other healthcare professionals base their judgment and decisions on?

The experience factor indeed drives medical decisions but when talking about complex medical diagnoses, healthcare professionals need to base their decisions and judgments on accurate information coming from different sources such as laboratory analyses made by experts (e.g., medical technologists) who are constantly involved in working with medical observations, samples, data, test results, researches, etc.

For professionals working in the health sector and treating hundreds of patients a year, it is key to trust reliable and accurate data, but how does data visualization impact the medical sector?

Key Takeaways

1. The Healthcare industry is one of the industries that generates the most data every year. In 2020, it was estimated that this sector created around 2,314 exabytes of data.

2. High-performance medical applications benefit from data visualization to visualize large datasets and support more accurate decision-making processes including diseases treatments and diagnoses.

3. Visualizations featured in MedTech and Healthcare applications demand the highest performance charting components.

Table of Contents

Why is Data Visualization Important in MedTech and Healthcare?

We've all seen at least one popular TV show or movie that showcases an example of a medical application that monitors a patient's e.g., heart rate, that looks like this:

Example of an Electrocardiogram (ECG)

So, even for non-medical readers, it becomes more intuitive to get the idea of a heartbeat visualization. But what would happen if you'd have to visualize the (sample) dataset instead? Let's take a look at a sample dataset of a heart rate contained in a spreadsheet:

The image above is a sample dataset containing hundreds of data points that at the bare eye, is tedious to understand even for medical professionals. In that case, how is data visualization used in healthcare? Data visualization helps in the process of transforming and visually displaying medical data points that simplify and synthesize the analysis of complex and large datasets into visualizations that are easier to process, visualize, and understand.

With that being said, data visualization and healthcare imaging offer another way how to understand data even for non-technical readers. Nowadays, medical applications that help users to track basic medical information are more common and can be found on people's smartphones and smartwatches.

So, it wouldn't be useful for users to visualize spreadsheets on their smart devices but rather translate complex data into simpler visualizations. After this concept, it is easier to understand why medical visualization plays a key role in the medical sector.

According to Statista, the amount of healthcare data generated in 2013 was 153 exabytes (where 1 exabyte equivalents to 1 billion gigabytes) and by the end of 2020, it had already increased to the colossal amount of 2,314 exabytes.

With the upward trend of the development of more medical monitoring applications for organizations and individuals, the amount of data that will need to be visualized, analyzed, and explored will indeed continue to grow rapidly in the upcoming years.

The continuous growth of medical data is a key driver for health organizations that are trying to provide a better and more personal patient-centric health service. As a consequence of this, data analytics, big data, and data visualization are becoming important subjects in healthcare that are helping to prevent and predict diseases.

The analysis of data represents a tremendous opportunity for health specialists to make data-driven decisions based on historical information that is later presented in state-of-the-art applications featuring the most advanced data visualization technology.

Preventive & Predictive Medical Visualization

While the future cannot be predicted, certain branches of data analytics in medical technology are present in the healthcare sector in order to convey different analyses that help professionals to work with past and future diagnoses. That is the case of descriptive analytics that takes a look into the performance of historical data and its behavior and relevance in the matter, e.g, in diagnoses.

The diagnostic analysis aims to understand why certain diseases occur and to explain their root causes whereas predictive analysis aims to forecast and anticipate future diagnoses and diseases which, therefore, acts as a method of prevention. Finally, the prescriptive analysis aims to test multiple scenarios to anticipate the outcomes before a final decision is made by a health professional.

The importance of data-related subjects such as data visualization is not only for health monitoring but also helps medical professionals to step ahead into possible future diseases and explain the triggers of past illnesses and harmful conditions.

Here's an example of a 3D volume rendering visualization of a skull that involves thousands of data points and is commonly used for studies in anatomy, traumatology, implants design, craniofacial reconstruction, or surgery analysis and planning, among other applications.
Example of a 3D Volume Rendering Skull for Medical Imaging & Visualization
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What is Medical Data Visualization?

As earlier mentioned, the volumes of data are steadily increasing in the healthcare sector and more medical professionals continue to experience difficulties when trying to make sense of patients' health information.

Within the different areas of data, data visualization specializes in transforming complex healthcare data and analyses into visual formats that help medical decision-making professionals to formulate diagnoses and treatments for patients in order to deliver effective medical care.

High-quality visualizations have been determined to be key in improving how health professionals perform decision-making processes and identify patients' health trends to understand future conditions ( predictive analysis) and diseases.

Technically, medical data visualization refers to increasing the efficiency and accuracy when reviewing and observing data for instance when visualizing organs, bone elements, or diseases processes. So, the process of medical or advanced data visualization helps to merge information from multiple sources that would otherwise stand alone and could not be related.

An excellent example of this was made by researchers Jonathan Wanderer, Jesse Ehrenfeld, and Soojin Park who stated:

"A visualization made for evaluating a kidney injury could merge multiple data from sources such as urine, urine electrolytes, serum creatinine, and blood pressure allowing doctors to quickly review and predict a change in the normal functions of the kidney."

Such visualization can be later used to understand and explore the disease, discuss the condition with other medical experts, and present the diagnoses to the patient's family.
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Data Analytics & Data Visualization Applications in Healthcare

Almost all the areas of healthcare and medicine can apply data analytics including data visualization. Today, modern medicine continuously uses data to provide a patient-centered approach and a more customized healthcare service. Moreover, data visualization in health IT is driving professionals to better decision-making processes.

Several areas of medicine have been improved and benefited from 3D models, e.g., x-rays, MRIs, or 3D volume renderings. High-performance visualizations are used within, if not all, the most demanding applications in healthcare, for instance, telesurgery, monitoring, surgical planning, or genomic analyses.

Molecular Analysis

In molecular analysis, Omics data refers to data sources from molecular fields where data is used with the aiming of understanding how diseases develop and how to improve the diagnoses of medical treatments.

Common visualizations used in this field are so-called N-body simulations that simulate the behavior of particles within environments, e.g., the human body system.
Example of N-body simulation
Genomics

Genomics refers to the study of genes and their functionalities which is key for researchers and medical experts when studying the prevention and cure of certain diseases. Visualizing data is key for observing and analyzing the correlation and trends that exist within large data sets that would otherwise be more complicated to analyze.

Medical Device Development

Data visualizations are commonly and widely adopted in medical devices for, e.g., cardiac rhythm monitoring, cardiovascular devices, diabetes monitoring, and prevention, etc.
Back to contents High-performance charts for medical devices
Charts & Graphs Used in Healthcare Visualization

Charts and graphs plot the data with respect to different axes creating specific forms or visualizations. As mentioned before, the amount of healthcare data is enormous and continuously growing thus demanding visualizations to be able to render a great number of data points at high-performance characteristics.

The requirements of the visualizations will be driven by the clinical needs but some of the characteristics of the highest performing visualizations would include:

Powerful data visualization libraries. There are several open-source and commercial libraries written in different languages including JavaScript, Python, or .NET. Depending on their nature, these libraries are designed for basic or advanced data visualization purposes.

Rendering performance. Not all libraries are meant to handle advanced medical and healthcare data sources. Open-source libraries experience this problem as they are limited in resources and performance. Commercial libraries are there to solve those issues and can render billions of data points in real-time without performance issues.

Resources optimization. Is the healthcare sector prepared to visualize all its data? Certainly, it is preparing for and GPU-accelerated libraries are using the PCs' power to deliver higher-quality visualizations at faster rendering and response times.

Here are 3 examples of advanced visualizations that can be later integrated into medical applications:
Interact
1. Medical Dashboard

Developed in JavaScript, this medical dashboard features real-time visualizations of four different channels. They process the data as it is received. The channels included are:

Electrocardiogram (ECG/EKG)

Non-invasive blood pressure measurement (NIBP)

Blood flow volume (BFV)

Blood oxygen saturation (Sp02)

2. 3D Volume Rendering of a Skeleton

This is a .NET medical visualization which samples a WPF 3D volume rendering of a human skeleton.

3. Electrocardiogram

Interact

This example of a JavaScript electrocardiogram featuring a line series in an XY chart. There are 960 signal points per second sent to the chart and its common application is in cardio and electrical activity monitoring.

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5 Companies Innovating in Healthcare & Medical Visualization

Medtronic is a world-leading diagnostic and health care company with products and services around the world. With operations in 150 countries, Medtronic products treat 70 health conditions and include cardiac devices, cranial and spine robotics, insulin pumps, surgical tools, patient monitoring systems, and more.

Within Medtronic's vast variety of healthcare solutions, they have developed the CardioInsight Workstation that is a dedicated workstation for ECG signals monitoring capable of rendering 3D cardiac maps.

The workstation works together with their product CardioInsight Mapping Vest which transmits the data to the CardioInsight Workstation and translates it into real-time high-performance data visualizations using LightningChart data visualization library.

Here's a practical case of the CardioInsight Workstation together with the CardioInsight Vest being used to analyze and treat a patient's arrhythmia. The case took place in one of the Northwell Health network hospitals.

2. MGC Diagnostics

Headquartered in Minnesota, US, MCG Diagnostics was founded in 1977 and today is one of the world leaders in the development of cardiorespiratory health solutions. They have a great scope of devices for the healthcare sector including devices for detection, classification, and management of cardiorespiratory diseases.

MCG Diagnostics is implementing high-performance data visualization solutions and features LightningChart data visualization charts and graphs in their Ultima™ CardiO2® gas exchange analysis system. This novelty device is an easy-to-use metabolic stress testing system with high-performance features including oxygen and carbon dioxide sensors to collect data from patients breathing, continuous data processing and analysis, and data visualization in real-time.

Source

3. BiosenseWebster

Biosense Webster specializes in the development of world-class medical devices for 3D navigation. They are innovators in the field of electrophysiology and have developed their novel CARTO™ 3D mapping technology used for signal monitoring and diagnoses that are improving the lives of patients with cardiac arrhythmias condition.

Biosense Webster relies on implementing powerful data visualization libraries capable to process signal data in real-time that helps healthcare experts to analyze a patient's conditions and provide a better diagnosis. Some of the visualizations used in the CARTO™ 3D mapping implement LightningChart .NET 3D volumetric rendering.
Source

Here's a detailed demonstration of a real-time ablation medical procedure made with the Carto system where abnormal tissues are removed.

4. Digicare Biomedical Technology

Is a family-owned and innovative company that provides devices solutions for medical monitoring and measurement. Digicare Biomedical Technology has implemented LightningChart Data Visualization Solutions in their novel telemetry monitoring system CentralVue CV10 that features different line series and XY charts that support millions of data points processing in real-time.
Source
The CentralVue CV10 is a medical multi-channel monitoring system that supports up to 32 different monitors with features including patient management and remote controlling.

5. Cerebra Sleep Scoring

As world-class innovators in the field of sleep research, Cerebra has studied, researched, and developed different solutions to improve the way how patients develop a better quality of life by enhancing their sleeping conditions.

Their Cerebra Sleep System directly analyzes the Electroencephalogram signal to observe the brain activity of patients and develop effective diagnoses that can improve the physical, mental, and emotional health of patients.

Within their system, Cerebra implements LightningChart XY and line series data visualizations for different monitoring of patients' sleeping signals coming from brain activity.

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Why Does Performance Matter in MedTech and Healthcare Data Visualization Applications?

When vendors in the MedTech and healthcare industry are evaluating charting products for processing their data, analysis, and visualizations, their top priority is high-performing charting components or data visualization solutions .

In the medicine field, data visualization is part of a bigger process where data needs to be processed at top performance with high accuracy avoiding downgrading techniques like downsampling.

End-users are health professionals who are experts in analyzing the information and translating it to their patients. Health professionals need to rely on fast and accurate tools provided by the best-performing medical devices that feature high-performance components.

The importance of high-performance in MedTech and Healthcare fits within the ability to transform millions and even billions of data points into high-performing visualizations that support the healthcare experts' decision-making processes.

High-performance data visualization is therefore crucial for MedTech applications to present data either on desktop, web, or mobile devices. Charts, graphs, and dashboards need to be able to handle millions of data points in real-time, to enable doctors and research professionals to have complete visibility and control over their instruments and MedTech applications to provide better healthcare services.

LightningChart ® Data Visualization Components for MedTech & Healthcare Application Development

Some of LightningChart's key points that support high-performance and large datasets visualization are:

MedTech cross-platform application compatibility. LightningChart data visualization library supports app development for desktop, mobile, and web applications.

Line series are commonly used in MedTech applications. LightningChart line series support visualizing up to 500 million data points.

Volumetric rendering, XY charts, and 200+ charts available. LightningChart provides the most extensive library of high-performance, interactive, and modern-looking visualizations for both .NET and JavaScript application development.

Guarantees the best performance for MedTech apps. Integrating LightningChart library to your MedTech application guarantees only the fastest response times, best hardware acceleration, and GPU-accelerated visualizations. E.g., LightningChart line charts are up to 516,600 times more efficient when rendering healthcare data.

When it comes to visualizing healthcare data, LightningChart charting components support the world's fastest and largest rendering technologies that leverage the way how healthcare experts and end-users visualize and understand the data allowing more accurate diagnoses and decision-making processes.

LightningChart® Data Visualization Solutions are advanced data visualization libraries for .NET and JavaScript that process hundreds of charts and dashboards built to handle billions of data points in real-time that support MedTech and Healthcare applications manufacturers to provide the best performing applications for end-users.

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Data Visualization in Medical & Healthcare Applications

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The fact that data and data visualization are everywhere in today's world, makes me wonder how data visualization impacts medical applications and what's their role within patients' diagnoses? Undoubtedly, patients trust their wellness on healthcare professionals' criteria and decisions but what do doctors, nurses, practitioners, and other healthcare professionals base their judgment and decisions on?

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Data visualization is essential and needed. It is very exciting and interesting to see how we use data visualization in healthcare. This blog shows the various data visualization techniques used in healthcare, with advanced technology charts and various applications and software.

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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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Ethical issues in health informatics are an issue. As our technology advances, the concerns for safeguarding electronic medical records as well s maintaining privacy laws and ethical principles. Some concerns are patient privacy, information security, healthcare data transparency, and prevention of bias in the information collected.

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Health informatics (HI), an emerging healthcare discipline that combines big data and medical practice, utilizes health information technology (IT) systems to analyze patient data and devise improved solutions. Health informatics professionals, also known as informaticians, use data in beneficial ways to improve healthcare delivery, reduce costs related to healthcare, curtail errors, and identify public health trends.

Alongside the benefits it facilitates, big data also pses genuine risks. Health information aggregation simplifies access to patient data by storing it in a centralized infrastructure, raising obvious concerns about privacy and related issues.

As the old adage cautions, with great power comes great responsibility. It's one reason informatics professionals must adhere to a strict ethical code to ensure patient privacy and confidentiality. In this article, we'll summarize ethical issues in health informatics, including:

Patient privacy

Information security

Transparency in the use of healthcare data

Preventing bias in the collection and use of healthcare data

We'll also discuss information related to:

American Medical Informatics Association professional code of ethics

How to become a health informatics professional

Earning a Master of Science in Health Informatics

Ethical issues in health informatics

Applied ethics are standard in most organizations. However, the healthcare industry has a specific branch called bioethics to address the especially critical ethical challenges within health professions. As technology advances and more healthcare systems utilize electronic medical records, the imperative to establish safeguards that ensure compliance with privacy laws and ethical principles grows evermore urgent.

Many ethical considerations arise within health informatics, including patient privacy, information security, transparency in using healthcare data, and preventing bias in the collection and use of information.

Patient privacy

Health informatics deals with large amounts of confidential patient information from electronic health records (EHRs), raising many legal issues and ethical concerns. HI must walk the fine line between necessary access to information and protecting patient privacy. We'll touch on two laws, one being the more important and the other being one of the better known.

Privacy Act of 1974

The Privacy Act is one of the most important laws that help balance information access and protection. With the advancement of health IT systems, interoperability improves the process of accessing, sharing, and evaluating patient records by physicians, clinicians, pharmacists, and relevant stakeholders on an as-needed and proper basis. Have you ever been curious about how your primary care provider could also view your information from an appointment with a specialist and vice versa? That's interoperability at work.

Although providers have access to this information, the balance comes from the federal Privacy Act, which protects patients from privacy invasions. ThePrivacy Act includes three key provisions:

The right to request access to their records

The right to make amendments to ensure accuracy

The right to protection from unwarranted invasions of privacy

Health Insurance Portability and Accountability Act of 1996

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) strengthened privacy protections to prevent the sharing of patient information without knowledge or consent. The U.S. Department of Health and Human Services (HHS) later implemented the HIPAA Privacy Rule, which ensures the protection of an individual's health information alongside health officials' right to access information if necessary to protect the public's health.

Information security

As technology advances, so do the information systems that store healthcare data. Big data and machine intelligence are essential tools to help analyze information affecting patient outcomes. Ethical principles for health informatics professionals who access confidential electronic records are equally crucial.

Earlier this year, a team of scholars from the Baylor College of Medicine suggested a new method for patients to control who accesses their information by repurposing nonfungible tokens (NFTs) for the healthcare industry. They proposed that NFT digital contracts could empower patients to specify who accesses and who shares their personal health information. Although this new approach is not in practice just yet due to privacy issues and data security concerns, it proves that technology is ever-changing as it relates to big data and its impact on various industries.

Transparency in the use of healthcare data

Although patient health data remains confidential, there are instances where individual patient information could influence public health and identify trends. For example, if a physician learns an effective treatment from one patient that could help another patient with similar symptoms, would sharing that information violate that patient's confidentiality or an imperative act of public health protection? This instance is where transparency becomes critical.

Dr. Kenneth W. Goodman, founder and director of the University of Miami Miller School of Medicine's Institute for Bioethics and Health Policy, lists three trust-enhancing ways to use the data from EHRs to prevent conflicts of interest and ensure privacy protection:

Prohibit unauthorized parties from accessing data and severely penalize those who do

Insist that oversight of informaticians, physicians, healthcare providers, and other stakeholders upholds ethical standards

Provide digital health education so that data sharing protects privacy interests while also benefiting from science at the public health level

Another approach to achieving the balance between health information technology and how stakeholders use the data is the Learning Health System (LHS), which originated in the U.S. in 2007. This system has nine core values:

Accessibility

Adaptability

Cooperative and participatory leadership

Governance

Inclusiveness

Person-focused

Privacy

Scientific integrity

Transparency

In short, LHSs require the use of secondary health data to help inform decisions that affect public health and shape future patient outcomes while remaining compliant with privacy protections.

Preventing bias in the collection and use of healthcare data

Goodman states that "the very nature of machine learning algorithms makes plain that one might unintentionally develop a biased system or accept biased results." With the rise of artificial intelligence (AI) and algorithms (with machine intelligence doing so much work behind the scenes), it's still critical to screen output to filter implicit bias to reduce the corruption of health policy.

The Healthcare Information and Management Systems Society (HIMSS) gives the following examples to explain data bias in healthcare even further:

Reading chest X-rays with data primarily from male patients will not be as accurate when applied to data from female patients

Skin cancer detection with data primarily from light-skinned individuals won't detect skin cancer as effectively from individuals with a darker hue

Although AI tools and algorithms populate the data, it is up to the data scientist or informatician to collect, analyze, and use it properly.

American Medical Informatics Association professional code of ethics

The importance of ethics in health informatics is so significant that professional associations like the International Medical Informatics Association (IMIA) expect their members to uphold a standard of ethical conduct. These members include nurses, computer scientists, physicians, and many other professionals who deal with patient confidentiality daily. The ethical guidelines consist of five categories:

Guidelines regarding patients: inform patients of their right to know about the use of electronic health records containing their personal information

Guidelines regarding colleagues: work collaboratively with and respectfully support team members; ensure colleagues are following best practices and advise when needed

Guidelines regarding employers or institutions: understand duties to employers and stay compliant with ethical and legal obligations

Guidelines regarding research and society: be aware of the public health impact and balance the greater good for society and individual patient privacy

General professional guidelines: stay up-to-date with timely resources to enhance professional development and seek consultation when needed

How to become a health informatics professional

The job outlook for health informatics professionals is growing much faster than the overall job market. In addition, as more organizations move toward technology-based platforms to collect, store, maintain, and analyze confidential patient data, the demand for informaticians and additional roles within health informatics to maintain ethical standards should increase.

For example, healthcare organizations must designate a privacy officer to ensure they adhere to federal and state guidelines, investigate privacy violations and concerns, and provide staff training. A privacy officer is just one of many roles to consider in health informatics. Most career paths fall into three categories: analytics, technology, or leadership, with some roles encompassing two or all three skill sets.

A bachelor's degree in information technology, computer science, or health administration is a gateway into the industry. However, some roles require an advanced degree in specialized areas to move up the ladder and earn a higher salary.

Earning a Master of Science in Health Informatics

A master's degree in health informatics opens the door to an impactful healthcare career that doesn't require years of medical school. Whether you are looking to transition into the field of health informatics or want more knowledge under your belt to excel in a new position, a Master of Science in Health Informatics (MSHI) is perfect for:

Healthcare providers

Health information managers

IT professionals

Nurses

Pharmacists

Physicians

Public health officials

If you're already working full-time, programs like the University of Washington offer a part-time hybrid format. Alternatively, more flexibility is available through a self-paced program like the online MSHI at the University of San Francisco or the online MSHI at the University of Pittsburgh, which has virtual or on-campus learning options. A health informatics master's degree program can take one to three years to complete; the average time frame is two years of full-time study.

Opportunities to take additional courses within a specialized concentration vary by the program selected and the college or university. Examples include:

Biomedical informatics

Clinical informatics

Cybersecurity

Data science

Ethics

Healthcare services

Healthcare supervision and management

Health data analytics

Hospital management informatics

Information technology

Nursing informatics

Organization development and leadership

Population health informatics

Registered health information administrator

There are many options available to build your career in health informatics. In addition, having an advanced degree opens more opportunities to connect big data and medicine without violating ethical principles.

*Questions or feedback? Email editor@noodle.com

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Health informatics (HI), an emerging healthcare discipline that combines big data and medical practice, utilizes health information technology (IT) systems to analyze patient data and devise improved solutions. Health informatics professionals, also known as informaticians, use data in beneficial ways to improve healthcare delivery, reduce costs related to healthcare, curtail errors, and identify public health trends.

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During a presentation, one of the major themes of the HIMSS22 conference has been the challenge of achieving health equity and eliminating implicit bias. While AI is promising and beneficial in healthcare and aiding physicians in diagnosis, some potential legal and ethical challenges must be tackled. An example used in this article's presentation was an online tool that estimates the risk of breast cancer and calculates a lower risk for Black or Latinx women than White even when every other risk factor is identical. It shows the bias and whom we can predict and prevent if we are not accurately working on helping all races and ethnicities.

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No policy is a policy. And when it comes to advancements in artificial intelligence (AI) and machine learning (ML), not having a policy could have serious implications for the patients, physicians, and every sector of the health care industry.

That was the point made by Brian Scarpelli and Sebastian Holst during their presentation titled "A modest proposal for AI regulation in healthcare," held during the HIMSS22 Global Health Conference in Orlando. Scarpelli is the senior global policy counsel for the Connected Health Initiative and Holst is principal with Qi-fense, a consulting group that works in AI and ML.

"ML properties do more than challenge domain-specific applications of technology," Scarpelli and Holst write. "Many of these properties will force an evaluation and retooling of core manufacturing, quality, and risk frameworks that have effectively served as the foundation of today's industry-specific regulations and policies."

Here's some key points from their presentation on the growth of AI/ML and the need for regulation.
1. AI is extremely promising.
AI can potentially revolutionize health care in all facets. It can reduce administrative burdens for providers and payer and allow for resources to be deployed within a health system to serve vulnerable patient populations. It can manage public health emergencies such as the COVID-19 pandemic and help improve both preventive care and diagnostic efficiency.
2. Machine learning is a rapidly growing industry.
According to Scarpelli and Holst, the growth in machine learning products has surged since 2015, starting first with processing applications, including products for processing radiological images, and has since progressed into diagnosis applications, particularly also in the radiological space to assist with triage and prioritization.

The number of patents coded to machine learning and health informatics has exploded, from 165 in 2017 to more than 1,100 in 2021.
3. Why AI needs regulating.
While AI is promising, there are potential legal and ethical challenges that must be addressed. For example, one of the major themes of the HIMSS22 conference has been the challenge of achieving health equity and eliminating implicit bias. That's one of the major challenges of AI as well since AI solutions can be biased. Many sessions focused on how diverse teams are needed when creating AI solutions to ensure that the programs don't carry the same biases as society, which could exacerbate current social problems, according to Tania M. Martin-Mercado, MS, MPH, a clinical researcher who presented on "How implicit bias affects AI in healthcare."

During her presentation, she pointed to an example of "an online tool that estimates risk of breast cancer calculates a lower risk for Black or Latinx women than White even when every other risk factor is identical."
4. Regulators need guidance.
A diverse group of health agencies, including the FDA, HHS, CMS, FTC, and the World Health Organization, are developing regulations and asking from guidance from various stakeholders, including AI developers, physicians and other providers, patients, medical societies, and academic institutions.
5. What AI should strive for.
Scarpelli says that the vision for successful AI follows four principals. It should:

Enhance access to health care.

Empower patients to manage their own health.

Strengthen the relationship patients have with their health care teams.

Reduce administrative and cognitive burdens for both physicians and patients.

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Why artificial intelligence in health care needs regulation

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No policy is a policy. And when it comes to advancements in artificial intelligence (AI) and machine learning (ML), not having a policy could have serious implications for the patients, physicians, and every sector of the health care industry.