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Having standardized language assists in creating systems that we can integrate into practice.

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The expanding role of digital healthcare is fascinating and so rapidly evolving.

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Abstract

Telehealth is becoming a vital process for providing access to cost-effective quality care to patients at a distance. As such, it is important for nurse practitioners, often the primary providers for rural and disadvantaged populations, to develop the knowledge, skills, and attitudes needed to utilize telehealth technologies in practice. In reviewing the literature, very little information was found on programs that addressed nurse practitioner training in telehealth. This article provides an overview of both the topics and the techniques that have been utilized for training nurse practitioners and nurse practitioner students in the delivery of care utilizing telehealth. Specifically, this article focuses on topics including 1) defining telehealth, 2) telehealth etiquette, 3) interprofessional collaboration, 4) regulations, 5) reimbursement, 6) security/Health Insurance Portability and Accountability Act (HIPAA), 7) ethical practice in telehealth, and 8) satisfaction of patients and providers. A multimodal approach based on a review of the literature is presented for providing the training: 1) didactics, 2) simulations including standardized patient encounters, 3) practice immersions, and 4) telehealth projects. Studies found that training using the multimodal approach allowed the students to develop comfort, knowledge, and skills needed to embrace the utilization of telehealth in health care.

Keywords: telehealth, nurse practitioner education, telemedicine, simulation, health care

Introduction

Health care systems continue to struggle to provide access to cost-effective quality care. The dramatic changes in health care reflected by increasing patients with multiple chronic diseases, health care workforce shortages, mandates to decrease costs, and infusion of technology make it necessary for nurse practitioners to embrace telehealth. 1 The use of telehealth technology presents new opportunities for addressing these challenges by increasing patient access to care and decreasing costs, while improving desired care outcomes. 2

For nurse practitioners to address health care of the future, advanced knowledge and skills in the use and application of telehealth technologies in practice are becoming essential. Preparing nurse practitioners to utilize telehealth technology requires specific and dedicated didactic and experiential telehealth education incorporated within existing curricula. It is important that educational programs focus on the understanding of telehealth and its application as well as skills development. Based on a comprehensive review of the literature, this article provides a multimodal framework for preparing nurse practitioners to utilize and champion the use of telehealth within the complex health care environment.

Literature search and study selection

A systematic literature search was conducted in MEDLINE, EMBASE, PEDro, Scopus, Cochrane Central Register of Controlled Trials, CINAHL, and PsycINFO to identify current evidence and strategies for preparing providers in the utilization of telehealth within health care. The search was limited to peer-reviewed journals that were published in English in the US. Search keywords included telehealth, mhealth, eHealth, telemedicine, systematic reviews, education, and nurse practitioner. The review focused on educational programs in health care that focused on eHealth, telehealth, and telemedicine. Eighty articles were initially reviewed. Articles that focused on the care of patients using telehealth were removed from the review as the focus of this article was on education. Articles that either described programs developed to educate students in telehealth and eHealth or provided content that should be included in such programs were chosen.

Gaps in the literature

Literature related to preparing nurse practitioners to utilize telehealth for practice is limited. Much of the literature supports the need for educational programs in telehealth and documents content that should be provided; however, there are very few articles that describe the educational process for nurse practitioners. Many of the programs that are provided focus on the education of medical students or mental health providers. 3- 5 One review found only 11 studies that reported the results of educational programs designed to deliver telehealth training. Five of the studies described short continuing education programs 3- 7 and only six described formal university programs 8- 13 with three being with nurse practitioners. 9, 11, 12 Compared to other aspects of telehealth research (i.e., feasibility, effectiveness, economics, and satisfaction), literature related to education and training in telehealth has remained scant. 14 This may be due to many providers being unacquainted with telehealth or perhaps a lack of interest in the field, secondary to this limited experience. 15, 16 One study found that 77% of faculty surveyed identified a need for training in telehealth. 15 The cost of starting a telehealth program was also identified as a barrier to programmatic implementation. 15 Ali et al 15 found that the programs that did utilize equipment as a tool for teaching telehealth were only able to do so with funding from a Health Resources and Services Administration (HRSA) grant.

One major gap identified in the literature is the incorporation of the human factors content related to telehealth (communication, empathy, and telehealth etiquette). 17, 18 While studies do exist related to phone etiquette and phone communication between provider and patients, 19, 20 government regulations and the American Telehealth Association do not classify telephone communication with patients as telehealth. 21, 22 Providers who had been using telehealth for several years discussed the "filter" of technology and how they were uncomfortable with the human factors necessary to conduct a telehealth visit. 23

This review suggests that there is a shortage of health professional programs that actually integrate training in telehealth within their health care curriculum. Most of the journal articles on telehealth tend to focus on didactic content rather than hands-on experience or clinical placements. Due to the paucity of articles on the training of nurse practitioners, this article will provide an overview of first the topics that should be covered and then the techniques that have been used in telehealth education.

Topics

Telehealth programs have been used to provide content that focuses on 1) defining telehealth, 2) telehealth etiquette, 3) interprofessional collaboration, 4) regulations, 5) reimbursement, 6) security/Health Insurance Portability and Accountability Act (HIPAA), 7) ethical practice in telehealth, and 8) satisfaction. 14, 15, 17, 23- 25 The following sections address recommended topics that programs might consider addressing when training nurse practitioners in telehealth.

Defining telehealth

Technology has been applied to health care in so many diverse ways that it is not surprising that there is great confusion as to what terms such as telemedicine and telehealth mean and how they are used in health care. Thus, educational programs might begin with a clear understanding of what constitutes telehealth and how it differs from telemedicine. While some organizations such as the American Telemedicine Association (ATA) use telehealth and telemedicine interchangeably, 26 many definitions discern a difference between telehealth and telemedicine. There have been more than 100 definitions of telemedicine in the literature over the past 10 years, suggesting that clarification for practitioners is needed. 27 The HRSA 28 defines telehealth as the use of technology in care delivery, health information, and remote health education. Telehealth is therefore the use of electronic information, devices, and telecommunication technology to provide direct patient care, remote patient monitoring, and education at a distance. Telephone, audio-only, text messaging, fax, and email communication are not considered telehealth technologies. 21, 22 Telehealth has been called a "disruptive technology" 29 in that it may either replace or supplement the traditional face-to-face patient-provider encounter.

By contrast, telemedicine, a part of telehealth, generally denotes patient care delivery by way of the virtual clinic visit or specialty consultation. According to the World Health Services Organization, 30 telemedicine is

the delivery of health care services, where distance is a critical factor, by all health care professionals using information and communication technologies for the exchange of valid information, for diagnosis, treatment and prevention of disease and injuries, research and evaluation, and for the continuing education of health care providers, all in the interest of advancing the health of individuals and their communities.

For the purpose of this article, telehealth includes all uses of technology to communicate with patients and/or practitioners regarding health and health care delivery, monitoring, and/or education. Telemedicine is part of telehealth but involves virtual medical consultations (patient-providers or providers-providers), virtual clinic visits, remote monitoring, and teletransfer of diagnostic imaging. provides a detailed outline of telehealth and its connection to telemedicine.

Amid the complexity of technology in general, there are only two basic types of technology used in telehealth: 1) real-time videoconferencing and 2) store-and-forward technologies. The development of Integrated Services Digital Network (ISDN) technology enabled transfer of both video and voice connections at the same time over one line, making live video conferencing possible. 31 Videoconferencing allows patients and providers at different sites to see and talk with one another in real time using computers, cameras, TVs, microphones, software, and computer networks. 32 Today's videoconferencing technology in health care enables two or more people to connect for consultation and care, patient management and monitoring, and education across the state or across the world.

Telehealth etiquette

An important aspect of the telehealth encounter that must be included in health care professionals' education is telehealth etiquette. Telehealth etiquette includes the unique behaviors necessary to conduct an effective telehealth visit. 15, 17 These behaviors are similar, yet different from previously learned behaviors necessary to conduct either a traditional in-person visit or a telephone consultation. 19 Telehealth etiquette includes proper camera positioning, elimination of office or clinic noise, the removal of personal objects located within view of the camera, and even the clothing choice made by the provider. 17 Bright or busy clothing patterns can distract from a telehealth encounter. Traditional communication skills must also be adjusted for this type of visit. Students must be taught the subtleties of eye contact during a telehealth visit. Eye contact is made with the client by looking at the camera, not the client's face on the screen. Looking down or taking notes can be perceived as disinterest or distraction. 17, 23 While empathy can be conveyed during a telehealth visit, it must be carefully constructed. Students must be taught to look and lean into the camera and to nod their head to encourage their client. Rather than using touch, students must be taught to choose the words that they use with their clients carefully to display empathy. 17, 23, 38 When students are adequately prepared to communicate using proper telehealth etiquette, they are better assured that they will have a successful visit. Strategies used to assist students in learning about telehealth etiquette include didactic lectures and visual examples as found in videoed encounters of poor telehealth interactions. 39

Interprofessional collaboration

Regulations

Despite decades of research that highlights the positive impact that telehealth can have on patient outcomes and patient satisfaction, providers still face many obstacles when attempting to integrate telehealth into their practice. 43 State laws and regulations that govern provider practice along with health insurance reimbursement rules for telehealth vary widely and often hinder the use of telehealth in certain practice settings. Thus, it is imperative that providers understand the regulations that impact their specific practice. In a report from the ATA 43 comparing physician practice standards and licensure for telemedicine by state, barriers to telemedicine implementation were identified as 1) limiting definitions of physician-patient encounters, 2) requirements related to the presence of a telepresenter on-site during telemedicine encounters, 3) additional requirements of patient informed consent, 4) limited licensure portability/reciprocity across state lines, and 5) limitations related to internet prescribing. 43 Nine states (Louisiana, Maine, Minnesota, Nevada, New Mexico, Ohio, Oregon, Tennessee, and Texas) currently require a special telehealth, conditional or special purpose license for providers to practice telemedicine across state lines. 43 Washington DC, Maryland, New York, and Virginia have licensure reciprocity from bordering states, while 17 states have adopted the Federation of State Medical Boards (FSMB) Interstate Medical Licensure Compact to facilitate expedited out-of-state licensure for physicians wishing to practice telehealth across state lines. This promotes portability of licensure in inclusive states which follows the registered nurse licensure compact that has been adopted by 25 states. 44, 45 State regulations can require physicians practicing medicine across state lines to have a license in every state where their patients may be located.

Regulations governing the practice of advanced practice nurses are often addressed by both the Board of Medicine and the Board of Nursing and vary widely from state to state. 44 In some states, nurse practitioners do not practice independently but are required to have established practice agreements with supervising or collaborating physicians. Supervision of Advanced Practice Nurses by collaborating physicians can be accomplished via telehealth in some states but not all. 44 As training is developed for nurse practitioners, there must be a focus on the regulations that will impact their utilization of telehealth.

Reimbursement

Telehealth technologies have reached a tipping point due to decreased cost of technology, adoption of standards-based operations, greater movement to the desktop and handheld devices, and simplification of processes. As a result, telehealth is incorporated into health care and health care delivery at an ever increasing rate. Telehealth has great potential as a tool in health care to help decrease costs, having the greatest effect in chronic disease management. 46- 48 Thus, it is important to not only educate nurse practitioners on the use of telehealth but also understand how the cost of telehealth is regulated.

One of the most important issues regarding the use of telehealth in practice is insurance coverage and provider reimbursement. Coverage for telehealth services in the US is addressed by federal and state laws and regulations. Nearly all state Medicaid programs reimburse for some form of telehealth services with 48 states providing reimbursement for telehealth services delivered via live video, 12 states allowing reimbursement for store-and-forward telehealth services, 19 states providing reimbursement for remote patient monitoring, and 7 states providing reimbursement for live video, store-and-forward, and patient monitoring telehealth services. 45 Medicare reimburses providers for live videoconferencing consults and visits, some store-and-forward services, and some remote monitoring for chronic diseases. 29

There are three areas of requirements for reimbursement for telehealth services in the Medicare program. These include originating site, providers, and telehealth services. For coverage, the patient must be at the originating site (defined as the office of the practitioner, critical access hospitals, rural health clinics, skilled nursing facility, community mental health center, federally qualified health center, hospitals, or renal dialysis units). Unfortunately, the originating site cannot be the patient's home. In addition, originating sites must be in a designated rural health professional shortage areas or other specified area. Practitioners delivering care must also have privileges at the remote location where they are connecting for services. These barriers restrict the delivery of telehealth. 49

States have the most control over laws and regulations for telehealth reimbursement and insurance coverage as they control the ability to pass legislation to allow or deny coverage. Almost all states, including the District of Columbia, include some coverage for telehealth in their Medicaid programs. A majority of states also have telehealth laws that require private payers to cover telehealth services. On the private payer side, 21 states have some form of legislation that addresses reimbursement for telehealth services for private payers. 45

An important aspect of this coverage is whether there is parity in coverage. Parity allows for telehealth services to be covered at the same rates as those of face-to-face services. Opponents suggest that since telehealth can decrease costs of care it should be reimbursed at a lesser rate. However, proponents support equal coverage as incentives to use telehealth rather than face to face to get the higher reimbursements. Therefore, it is essential that nurses are familiar with the telehealth rules and regulation that govern their state to fully participate in nursing telehealth practice. 50

Security/HIPAA

Ethical issues related to telehealth such as security, confidentiality, and patient information are paramount; most programs clearly address this content. 16 The HIPAA of 1996 established guidelines for electronic health transactions, national identifiers for providers, and security and privacy of health data. 50 The regulatory guidelines for privacy and confidentiality covered by HIPAA govern provider practice in all settings including telehealth encounters. Beyond safeguarding the privacy and confidentiality of private-protected personal health information (PHI), telehealth providers are responsible for ensuring that the "space" or environment where the encounter takes place is secure at both the originating location and the distant site and that the transmission between the originating site and distant site is secure. This necessitates proper encryption of protected patient data and appropriate HIPAA agreements with all technical personnel. 50 Penalties for HIPAA infractions or violations can include fines up to $50,000 per violation, criminal charges, and civil penalties. 50

Ethical practice in telehealth

Telehealth practice is governed by the same Codes of Ethics for health care professionals as traditional face to face/same place encounters, yet the intricacies of providing care via this modality can trigger interesting ethical challenges for providers. Mehta 51 outlines four potential ethical "pitfalls" related to telehealth implementation: erosion of the provider/patient relationship, threats to patient privacy, forcing one-size-fits-all implementations, and the temptation to assume that the new technology must be effective. Issues related to patient privacy, confidentiality, and security are addressed earlier, but exploring other areas of concern such as the erosion of the patient/provider relationship is paramount to a balanced and comprehensive advanced practice nursing curriculum. Ensuring that advanced practice nurses possess the moral sensitivity to identify ethical issues in care regardless of the structure and format of the interaction is an important step in advanced practice nursing education.

Providers have also voiced concern about the potential for patients to "shop" for providers and abuse the system in search of practitioners who will provide expedient care and desired prescriptions. 52 Exploring how this is achieved within the ethical and legal landscapes that vary from state to state requires close review and understanding of the laws and regulations that govern provider scope of practice and the practice of telehealth. Kaplan and Litewka 53 call for providers to thoughtfully explore the ethical issues of informed consent, autonomy, and empowerment within the context of an exploding technological shift toward telehealth and ehealth modalities. Thoughtful examination of what these cornerstone ethical concepts mean in the context of telehealth is an important part of the ethical trainings of nurse practitioners.

Satisfaction with telehealth

Of high importance in education is the need to increase the student's positive attitudes toward the topics presented, and telehealth is no exception. In fact, students are often resistant to engaging in telehealth activities stating that they will never use it or that their patients will not like it. Data must be presented to students on the importance of having an open mind toward telehealth. One approach is to present data on patient and provider acceptance.

Generally, the overall satisfaction levels for services delivered via telehealth have been high for patients, providers, and health care students. One study by Gros et al 54 found that evidence-based psychotherapies, delivered via telehealth, for post-traumatic stress disorder (PTSD) in a veteran population were accepted and valued at the same level as in-person services. Additionally, another recent study providing telepsychology treatment for depression in an older veteran population noted that quality of life and satisfaction with care were not adversely affected by a decision to use telehealth. 55 Another study reviewed patient satisfaction with telerehabilitation after total knee arthroplasty (TKA). 56 Two groups were studied, one group received face-to-face home visits and the other participated in home telerehabilitation. Patient satisfaction was measured using the Health Care Satisfaction Questionnaire (HCSQ) and both groups scored equally high with 85% satisfaction overall. 56 In a study of 86 men living with Parkinson's disease, participants were provided either the usual in-person care or telehealth. There was an increase in satisfaction for the telehealth modality when asked about overall convenience and accessibility. 57

Techniques

Multimodal framework for telehealth education

Educational programs have found it challenging to not only teach telehealth theory but also provide hands-on experiences with technology; this has proven especially difficult within a rapidly changing environment. This article presents a multimodal approach to nurse practitioner telehealth education based on identified learning needs and aligned with nurse competencies and essentials (). Based on a review of the literature, four areas of telehealth nursing education are identified: 1) didactic education, 2) experiential simulation, 3) projects, and 4) formal clinical telehealth experiences.

Didactic education

Most of the literature related to the teaching of telehealth is anecdotal. A majority of the university programs that have implemented a telehealth curriculum have utilized a traditional didactic approach, focusing primarily on theory and practical knowledge. 8, 10, 13 Specifically, didactic telehealth education has been delivered through short online courses, narrated presentations/webinars, videos, and face-to-face lectures. 8, 10, 13, 39 This is a useful approach for providing for knowledge generations; however, it does not address skill development and comfort with the technology.

Experiential simulation learning

To overcome resistance to using telehealth, experiential activities become a must. A safe way that programs have found for introducing telehealth is through simulations. Rutledge et al 11 developed a telehealth simulation workshop at Old Dominion University for their students using standardized patients. Sixty advanced practice nursing students, in groups of 5-6, conducted an interview of a standardized patient and their caregiver using videoconferencing technology. The patient and caregiver were in one room, and the students were in a second room communicating via computer screens. The students interviewed the patient and caregiver as well as practiced assessment of the patient's stroke condition by having the patient ambulate in front of the screen as well as conduct manual exercises. The students were able to develop comfort with the technology, address telehealth etiquette, and realize that they could actually develop a relationship with the patient over the screen. 11

Erickson et al 9 provided a telehealth demonstration for their nurse practitioner students prior to having them participate in a clinical rotation. The students were connected to the information technology (IT) laboratory at a hospital through videoconferencing technology. They then received instruction on the use of the videoconferencing cameras, digital otoscopes, stethoscopes, and examination cameras. 9 Other schools have utilized telehealth equipment in the clinical setting to visit with the student or to observe the student conducting an assessment with patients. 24, 60, 61 Although the students are not participating in a patient/provider encounter utilizing telehealth equipment, they are afforded the opportunity to use the equipment and practice real-time communication with their instructor.

In another program, students were given an opportunity to work with various pieces of telehealth equipment as they rotated in groups through different work stations. 12 The goal was to help the students understand how to use telehealth to support a patient at a distance. The first station allowed the students to use telehealth equipment to interview a standardized patient who was recovering from a stroke. In the second station, students were given individual iPads to use to explore websites that stroke patients and their family members could use to obtain relevant information and communicate with professionals. The third station consisted of utilizing electronic health records to obtain static store-and-forward data such as computed tomography (CT) scans and medical records. The fourth station allowed providers to explore some of the clinical decision-making software and mobile applications available to providers and patients related to stroke care and management. 12

Clinical telehealth experience/projects

Programs have reported success in placing students in clinical settings where they are exposed to the uses of telehealth for patient care. In one program, the students were assigned to participate in an 8-hour clinical rotation at a site that either provided or received telehealth support for patients. 11 In addition to observing telehealth encounters, the students were expected to complete a project in which they surveyed the providers at the site regarding the reason telehealth was chosen, what patient populations they served, which technologies they utilized, how they accessed the technologies, barriers they encountered, and how they overcame the barriers. The students were then expected to develop a plan for implementing telehealth in their own clinical environments. Student placements included a telestroke center, behavior health counseling, emergency room to emergency room, and a rural medical center that sends synchronous and asynchronous data to a tertiary medical center. 11

A second program implemented a 4-hour clinical experience in which students were paired with preceptors in either an ambulatory or acute care setting that used telehealth to provide care to patients. 9 Students were allowed to participate by being at the patient site, with the preceptor, or at the college or health system. Students participated by obtaining the patient's history, conducting a review of systems, or conducting a physical examination with the telehealth peripherals such as the examination camera, digital stethoscope, or otoscope. Students were able to access the patient's medical records as well. 9

Another program focused on provider to provider telehealth encounters by pairing nurse practitioner Doctor of Nursing Practice students and nurse executive Doctor of Nursing Practice students (intraprofessional collaboration). 41 The pair was responsible for connecting using a technology they had never used before to develop a program to address a specific population need. The students then completed a paper addressing what they learned about each other's role, what technology they used, the benefits and barriers in using the technology, and program they developed. The students were overwhelmingly positive about the experience in using the technology and the opportunity to learn about each other's roles. 41

Many programs struggle to provide clinical placements that allow students hands-on practical experience due to lack of available resources, student difficulty in traveling to geographically remote sites making them prohibitive for students, and difficulty in scheduling telehealth opportunities. 9 In addition, student availability does not always match with the provider-patient telehealth encounter. 9 Other programs have found that minimal faculty exposure, knowledge, and comfort may impact their willingness to offer telehealth educations. 15, 16 For nurse practitioners to be prepared in telehealth training in school, these barriers must be addressed.

Educational programs for practicing providers

Due to the fact that many health care preprofessional programs do not include formal training related to telehealth within their curricula, many providers are expected to obtain the necessary training for telehealth on the job. Thus, the telehealth training tends to occur in the clinical setting or through postprofessional certification programs. 18, 24

The Telehealth Resource Centers (TRC), a national clearing house for telehealth information related to use, policy, and program development, helps prepare providers for telehealth. 50 The US has been divided into 14 regional TRCs designed to support each region's efforts related to telehealth (). Within each regional resource center, training modules and certification programs exist. Most programs are designed to deliver information through asynchronous modules with hands-on testing at the conclusion of the program. Webinars and teleconferences are also delivered through the TRCs. Most regions also present a yearly conference that allows providers to have an opportunity to network and develop an understanding of the technology that is being developed for the health care arena. 50

The ATA also provides information and support for health care providers. Information available through the ATA pertains in a large part to program upstart, types of technology and equipment, and policy, laws, and regulations impacting telehealth usage, although they are innovative developers of telehealth practice guidelines. 62 The ATA is considered a leader within the telehealth arena; however, one must be a member of this organization to take full advantage of their educational offerings. Unfortunately, neither the TRC nor the ATA have specific programs for educating providers on how to utilize telehealth in patient care or to educate providers in the skills necessary to practice in the virtual environment and conduct a successful telehealth visit.

Even though there are suggestions regarding the knowledge and skills required to provide telehealth, a clear approach to educating the health care workforce has yet to be mandated. Furthermore, telehealth education requirements have yet to be outlined in nurse practitioner education. This often leaves it up to the individual school of nursing, the practitioner, or their employer to decide whether nurse practitioners should pursue training in telehealth and what specific training they should receive.

Future directions

The infusion of telehealth technology into all aspects of health care creates a growing role for nurse practitioners to integrate and utilize telehealth technologies in practice. However, a gap in knowledge exists in preparing providers for telehealth practice due to various factors, the most obvious being the lack of telehealth education in nursing programs. As supported by the literature, a multimodal telehealth education program is recommended in nurse practitioner programs. This would allow for didactic education, telehealth simulation experiences, clinical telehealth rotations with experienced telehealth preceptors, and engagement in telehealth project development. This will provide a well-rounded telehealth educational experience that aligns with professional competencies and essentials of practice.

Nurse practitioners can effectively participate in the transformation of health care through their roles in clinical practice, leadership, scholarship, research, policy, and business practice by participating in telehealth nurse practitioner education and practice. Evidence is mounting to support the positive effect of telehealth on health care and the health care system. 2, 35 Technology allows nurse practitioners to reach patients in a variety of remote settings, expanding the reach of health care providers. Telehealth enables access to care for those who need specialty care or who have difficulty with travel. It improves outcomes through innovative uses of technology to improve frequency and quality of communications and patient engagement. It reduces cost of health care for the facility by improving management of chronic diseases, reducing readmission rates, and improving provider efficiency. Telehealth technologies enable more frequent communication and more frequent physiologic monitoring of patients, reflecting improved outcomes and better utilization of health care resources.

To prepare nurse practitioners for their role as advocates in optimizing health care outcomes, especially in areas where access is limited, they must be trained in the use of telehealth. Programs must prepare students through experiential learning opportunities such as simulation, clinical rotations, and projects as well as didactic sessions. This approach is effective in assisting students to develop the knowledge, skills, and attitudes needed to maximize the use of telehealth and remove barriers to using telehealth in both primary and specialty care.

Specific areas of focus in education must be on partnering with those developing and/or utilizing telehealth technologies to provide students with access to hands-on opportunities. Didactic programs must address the many aspects to telehealth. Finally, competencies must be developed to direct the education and training of nurse practitioners regarding telehealth.

Conclusion

Telehealth has forever changed health care. Nurse practitioners educated in telehealth can make strong contributions to nursing and health care as they emerge from programs as future nursing leaders. The issues facing health care and health care resource allocation can be improved with telehealth systems of care developed and implemented by nurse practitioners. It is essential that nurse practitioners become empowered with telehealth knowledge and hands-on skills so that they can be a creative force for innovations in telehealth within practice and health care systems. Schools will be at the forefront of health care as they develop telehealth education programs and prepare to respond to the challenges ahead by promoting innovation through nurse practitioner telehealth education.

Footnotes
Disclosure
The authors report no conflicts of interest in this work.

References

2. Totten A, Womak D, Eden K, et al. Telehealth: mapping the evidence for patient outcomes from systematic reviews. Agency Healthc Res Qual. 2016 Jun; Report no 16-EHC034-EF.

18. Papanagno D, Sicks S, Hollander JE. Training the next generation of care providers: focus on telehealth. Healthc Transform. 2015:52-63.

40. Interprofessional Education Collaborative (IPEC) Core Competencies for Interprofessional Collaborative Practice: 2016 Update. Washington, DC: Interprofessional Education Collaborative (IPEC); 2016.

54. Gros DF, Lancaster CL, López CM, Acierno R. Treatment satisfaction of home-based telehealth versus in-person delivery of prolonged exposure for combat-related PTSD in veterans. J Telemed Telecare. 2016 Sep 26; Epub.

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Telehealth and eHealth in nurse practitioner training: current perspectives

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Telehealth is becoming a vital process for providing access to cost-effective quality care to patients at a distance. As such, it is important for nurse practitioners, often the primary providers for rural and disadvantaged populations, to develop the knowledge, skills, and attitudes needed to utilize telehealth technologies in practice.

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We reviewed the Tiger Initiative in the Asynchronous 

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We reviewed the Tiger Initiative in the Asynchronous 

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This is very informative as to how we are trying to improve education and the use of informatics.

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An Initiative for Technology and Health Informatics Education

The Technology Informatics Guiding Education Reform (TIGER) initiative from HIMSS provides the global health workforce with innovative informatics/eHealth tools and resources to transform health for all.

The global TIGER network collaborates to integrate informatics/eHealth into healthcare education, certification, practice and research through an inclusive, interdisciplinary, intergenerational approach.

Education Reform

We create an environment that maximizes the possibilities of technology toward knowledge development and dissemination, driving rapid implementation of best practices as well as competency and curriculum development.

Interprofessional Community Development and Growth

We actively engage and collaborate with the global healthcare workforce, students and adult learners to encourage standardized, person-centered and technology-enabled processes to facilitate teamwork and relationships across the continuum of care worldwide.

Global Workforce Development

We foster a respectful learning community of diverse stakeholders that embrace shared values to drive innovation and technology around the world.

Global Recommendations

While informatics recommendations on competencies and education support the design of curricula and courses, they are unable to inform educators on how to customize content for local needs and requirements. That's why the TIGER International Competency Synthesis Project (ICSP) and the EU*US eHealth Work Project came together to describe and validate the TIGER initiative's framework of global informatics core competencies for health professionals.

Together, these projects compiled a recommendation framework of health informatics competencies (v2.0) to measure, inform, educate and advance development of a skilled workforce throughout the EU and U.S. and around the world. Although the EU*US eHealth Work Project has ended, we are compiling global case studies to continue bringing our findings to life, with a focus on all states within the EU.
Hear TIGER International Task Force Former Co-Chairs Ursula Hübner, PhD, professor, University of Applied Sciences Osnabrück, Germany, and Beth L. Elias, PhD, MS, FHIMSS, adjunct faculty, Panuska College of Professional Studies, University of Scranton, present these important project findings at the HIMSS19 Global Health Conference.
TIGER International Task Force

The TIGER International Task Force, seated under the HIMSS Professional Development Committee, provides our global community of 34 countries with knowledge, leadership and guidance in its efforts to reform technology and informatics education. It provides domain expertise through activities, projects and collaborations within the interprofessional community to support faculty, students, adult learners and providers.

TIGER Resources

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The Technology Informatics Guiding Education Reform (TIGER) initiative offers tools and resources for learners to advance their technology and health informatics skills and for educators to develop valuable curricula.

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https://www.youtube.com/watch?v=72OtVROdPLA

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Inside a movement to revolutionize health informatics education

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I think the best way to have integrated and efficient digital healthcare systems is to have vocabulary that is unified and universal.

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Kathleen A. McCormick, PhD, RN, FAAN, FRCNA, FACMI
Cheryl B. Jones, PhD, RN

The use of vocabulary in nursing is a relatively new phenomenon. It has only been in the past 20 years that we have developed and refined the nursing nomenclatures and classification systems. However, as we become more knowledgeable about the information system infrastructure of health care in this country, the linking of vocabularies from disparate sources becomes more critical. We are now part of an international network of health care, where the term we use in one country greatly influences the impressions of other countries of our health care system. This paper describes some of the challenges to the nursing profession, in allowing us to maintain our local vocabulary, as we integrate into network and universal vocabularies in the future. It discusses some of the new technologies that facilitate the linkages. It defines some of the endpoint of our vocabulary in identifying the outcomes and quality of care that is delivered by the nursing profession. A model is proposed that demonstrate the linking required to move classifications from the point of care, through networks, and into universal levels.

Citation: McCormick, K., Jones, C., (Sept. 30, 1998): "Is one taxonomy needed for health care vocabularies and classifications?" Online Journal of Issues in Nursing. Vol. 3, No. 2, Manuscript 2. Available: www.nursingworld.org/MainMenuCategories/ANAMarketplace/ANAPeriodicals/OJIN/TableofContents/Vol31998/No2Sept1998/Isonetaxonomyneeded.aspx
Introduction
Misinterpretations that arise in every day personal and professional discussions highlight the critical role of vocabulary in communication generally, and in practice specifically. Without the common understanding that comes from vocabulary, these misinterpretations extend to affect care delivery, practice patterns, role differentiation, and, ultimately, patient and organizational outcomes, including quality and costs of care. Communicating information is necessary to objectively make health care decisions, yet information is useless without vocabulary.

The health care industry has a dearth of data upon which to base decisions. In fact, O'Connor (1998) cites "bad data" as the primary reason those decreased health expenditures cannot be attributed to managed care: existing data do not allow that linkage to be fully explored. Further, "bad data" may be one of the reasons that health care expenditures are expected to rise in the future: the costs of data collection and health information system development or redesign can be prohibitive if both direct and indirect costs are considered, and the burden of those costs likely will be passed on to individuals, communities, and society.

Data for making health care decisions are deficient, not simply because of an insufficient amount of data, but because of an insufficient amount of the right type of data. Existing data consists of large amounts of claims data, some administrative and clinical data, and minimal outcomes, quality, and comprehensive cost data. Healthcare environments are creating large databases and repositories, but various healthcare groups are struggling to collect data and build a case for support to collect data that document the contributions of different skill levels and types of healthcare professionals and nonprofessionals. Jacox (1992) addressed the relevance of this issue for nursing, and noted that databases are needed to clearly distinguish care delivered by individual and/or groups of nurses.

In the quest to gather data related to nursing, vocabulary is essential to communicate information and guide data collection. However, there is general disagreement about what the vocabulary should be or how the vocabulary or vocabularies should be developed. This paper will propose those multiple levels of nursing vocabulary, classifications, and taxonomies that will be needed in the future. A model will be presented to demonstrate that multiple vocabularies, classifications and taxonomies are needed, convergence is necessary at a certain level, and that a unicode or unified taxonomy is required if global, international, worldwide, or universal comparisons of nursing care are to be made. Given that knowledge development begets debate, this paper aims to provoke such discourse. Key terms relevant to this discussion are shown in Table 1.
Background
Nursing classification schema have been developed at varying levels of abstraction and offered as ways to organize and categorize nursing phenomena, such as the North American Nursing Diagnosis Association (NANDA) Nursing Diagnosis Taxonomy ( Warren & Hoskins, 1995), the Omaha System ( Martin & Scheet, 1995), the Nursing Interventions Classification (NIC) ( Bulechek, McCloskey, & Donahue, 1995), and the Nursing Outcomes Classification (NOC) ( Johnson & Maas, 1995). However, at a more basic level, the question arises, why does the discipline of nursing need a taxonomy or taxonomies?

Relevant Terms and Definitions

Classification
A systematic arrangement of classes; a structural framework arranged according to similar groups ( Lang, et al, 1995).

Unified
Linked, affiliated, associated to make into a unit or coherent whole ( McCormick, 1988a)

Several reasons can be identified to explain this need. First, employers, insurers, providers, payers, and policy-makers require objective, science-based information to make critical decisions about meeting health care demands, supplying health care services in the marketplace, providing access to services, allocating resources, and determining the costs and quality of care. Second, market forces, or competition, necessitates accountability, and documentation is required to substantiate accountability for processes and outcomes of care. Data on outcomes related to quality and costs will provide information to the public, and ultimately enable consumers and payers to determine health care-related value, and make tradeoffs in level of quality desired for a given cost. Finally, data are a requisite for conducting empirical research; hence, researchers need data to answer research questions related to practice, identify "best" practices, develop and test models of care, design decision-support models, and determine efficient and effective utilization of resources. While no one data source will meet all of these needs, a common vocabulary will enable linkages among data sources to be made.

A discussion of "taxonomy" of any kind naturally polarizes individuals and groups on a philosophical level, given that individuals and groups conceptualize and classify phenomena in different ways. In fact, the question of whether nursing needs one or more taxonomies or many conjures up thoughts of similar disciplinary debates about nursing theory and research methodology: does the discipline need one or many? Each side of the debate has certain advantages and disadvantages, and in some cases, a disadvantage of one side becomes the advantage of the other. The major advantage of advocating one taxonomy is simplicity - if there is one taxonomy, then there is the assumption that everyone is or will be made aware of it, understands the vocabulary and classifications, accepts it, and utilizes the known taxonomy. The existence of one taxonomy eliminates confusion over terminology and meaning, and necessitates disciplinary agreement about the vocabulary and classifications. For example, most nations use the International Classification of Diseases Version 9 or 10 (ICD-10) to describe country-level mortality and health care costs, which, in turn, facilitates the comparison of medical conditions across countries.

However, the opposing argument in a discussion of taxonomy is readily apparent in a national health care environment that supports many types of health care delivery, and that implies multiple service options, open discourses about those options, and the freedom to choose amongst options. For example, Henry (1997) emphasizes that existing nursing classification systems recognized by the American Nurses Association (e.g., NANDA, NIC, NOC) are not sufficient to reflect the entire scope of nursing practice. Additionally, the argument for one taxonomy assumes that one taxonomy would be learned, interpreted, and operationalized in a similar fashion across all individuals who practice nursing. This assumption is clearly not reasonable. For these reasons and others, nurse scholars have debated and opposed the advocacy of one taxonomy for nursing.

From a social policy and economic perspective, one taxonomy is inconsistent with the shift toward devolution of power and increasing autonomy to individual states. For example, certain funds are released to states in the form of block grants. Medicaid, the joint federal and state program aimed at providing health care coverage to the poor, provides shared reimbursement from federal and state governments, the largest proportion of which comes from state sources. In turn, many of the Medicaid regulations are established and implemented at the state level, within certain federal guidelines and limited federal oversight. This variability in funding and decision-making at the state level suggests that there will be variability in health care needs, resource allocation, health care service delivery, and monitoring mechanisms, including data collection, across states. Hence, variability across states and geographic areas necessitates variability of data elements that will be collected across states and regions.

Simultaneously, within health care, responsibility and accountability for decision-making is increasingly decentralized to the clinician closest to the point of care delivery - and, ultimately, and/or to the extent possible, to the individual patient or family. This effort should, in theory, improve the quality of service delivered to the consumer, increase autonomy and job satisfaction for the provider, and decrease negative aspects of the practice environment. In both policy and management, however, the trend toward increasing autonomy, whether to states or individuals, can shift easily back toward a more centralized mode of decision-making whenever dramatic changes occur within political or management authority. These possible shifts necessitate that data are captured in a manner that allows integration across levels and sites of care delivery, providers, and realms of management and control.

The term taxonomy refers to a hierarchical system. As defined in Table 1, taxonomy comprises vocabulary and terms; in turn, vocabulary is made up of terms, or names at the most basic level. This hierarchical system is similar to that of a theoretical system, whereby theories comprise constructs, constructs consist of concepts, and so forth. An important assumption of these two systems is that there are relationships between the levels. While discussions of taxonomy within the context of informatics is not typically linked to theory, the analogy of the two systems is particularly relevant to this discussion, given that useful taxonomies are linked to significant theory (Benzon at www.newsavanna.com/wlb/CE/Arena/Arena07/shtml).

This paper proposes a model that demonstrates where multiple vocabularies, classifications, and taxonomies may be needed, and where they need to converge to fewer taxonomies, and finally at what level a unicode or unified taxonomy might be required if global (international, worldwide, or universal) comparisons are to be made of nursing care.
A Vocabulary Framework
Figure 1 depicts a framework that provides conceptual insight into vocabulary needs for policy and management practice decision-making in healthcare. This model builds on Eisenberg's ( 1998) discussion of healthcare as occurring at the levels of society, health systems, and clinical practice. Similarly, management decision-making occurs at the levels of population, group, and individual. For example, clinical management decisions focus on case and care management: at the individual level through assessment, planning, implementation, and evaluation of an individual's plan of care; at the group level as coordination and facilitation of care across a group of patients with similar care needs; and at the population level as integration of care across aggregates of similar and different groups with common health care needs, most notably morbidity, mortality, health promotion and disease prevention. This figure denotes the reciprocal relationship between management and policy decision-making, and the incorporation of vocabulary at all levels of decision-making.

This model assumes that there are three types of vocabularies needed in health care. The point of care is an "interface" vocabulary that occurs at the individual and practice level, and includes terms that are used between clinician and patient, and/or clinician and clinician to describe and convey related patient and clinical information. Vocabularies at the point of service level emphasize the settings where care is delivered, and are often discipline or specialty focused. Obviously, different vocabularies and classifications are needed to represent point of care vocabulary in nursing across the continuum of care (e.g., prevention to primary care to sub-acute care, to acute care, etc.). Examples of existing information systems that provide interface vocabulary to support decision-making at the patient care level are Oceania ( www.oceania.com) and a Canadian information system called Purkinje ( www.purkinje.com).

Network is represented by terms and phrases that serve as a "reference" vocabulary to link clinicians' documentation across horizontally or vertically integrated systems of care delivery (e.g., a hospital system or primary care clinic system, and health maintenance organization, respectively). Reference vocabularies are based on knowledge derived through interface, and reflect an integration and classification of knowledge. Thus, the individual practice encounter is used to build information and knowledge for decision-making at a group or network level. Vocabularies at this higher level of abstraction synthesize knowledge from multiple settings, disciplines and specialties interacting at the point of care, integrate and classify that knowledge, and build information and knowledge for decision-making to link group and system decisions.

Rationale for use of a reference health care vocabulary in measuring quality, outcomes,and evidence research in nursing

Provides the link between health care knowledge found in the literature and inquiring health professionals who need to keep up with the literature

Facilitates examination of community nursing practice which requires uniformity of clinical data and concepts, or accurate mapping of vocabulary terms that have the same meaning or refer to the same concept

Enables the evaluation of practice innovation impacts, such as practice guidelines, evidence, and other nursing knowledge, on the practitioner closest to the point of patient care, who requires a vocabulary infrastructure that is consistent with financial, administrative, and clinical databases

Provides the ability to conduct research on patient outcomes by linking patient records and patient outcome data to examine the impact of patient conditions and clinical treatments on patients' health status, return-to-work, quality of life, and other outcomes.

Facilitates the integration of new development tools used by purchasers and producers of health care with clinical, financial, and administrative vocabularies to fully examine quality in health care

There are currently no existing systems at the network level that link all health professional vocabularies within systems or groups of providers. However, this level of vocabulary is essential to measuring and monitoring quality, examining health outcomes, determining effectiveness of health care delivery, and developing an evidence base for practice. Table 2 provides further rationale for the network level of vocabulary. SNOMED International (Systematized Nomenclature of Human and Veterinary Medicine) is a complex but comprehensive classification system for "indexing the entire medical record, including signs and symptoms, diagnoses, and procedures. Its unique design will allow full integration of all medical information in the electronic medical record into a single data structure." ( http://snomed.org). SNOMED is investing money to develop a reference or network level health care vocabulary for the United States. Other U.S. vocabularies that are considered beginning network vocabularies are MEDCIN, MEDICOMP, and Dr. Elmer Gabrieli's natural language processing ( Gabrieli, 1993).

Universal or "administrative" vocabulary is the highest level of vocabulary, and links information on populations of people in a community, state, country, or globally (e.g., ICD- 9 or -10 coding system from the World Health Organization, or an International Classification of Nursing Practice (ICNP)). Universal vocabularies build on knowledge and information obtained at the point of service and network levels, and reflect the highest level of integration and synthesis of knowledge, and a combination of vocabularies for societal and population decision-making. For example, population statistics from a community, state or country are synthesized and analyzed to identify health care needs, and the numbers of people who die from certain disease conditions. At the international level the World Health Organization can determine the major conditions causing mortality within different countries by age groups and across the world. Universal vocabulary currently guiding decisions in global health care is the International Classification of Diseases (ICD) Versions 9 and 10 (CM is the Clinical Modification used in the U.S.). The ICD does not completely integrate population and society data, but reflects primarily medical diagnoses and phenomena. The ICD-9-CM is the classification used by the Health Care Financing Administration (HCFA) to reimburse for care delivered to Medicare and Medicaid recipients in the United States.
From Point of Care to Universal
Figure 2 further explicates nursing vocabulary needs from the point of care through universal levels. This figure points out that, at the point of care, nursing needs several vocabularies to communicate relevant information gathered during the patient encounter. These point of service-level vocabularies are used for documentation purposes to translate information into patient records; in turn, information from these records is extracted electronically and communicated to the network level. At this level, vocabularies are aggregated to higher level network classifications. This aggregation of terms into network classifications decreases the likelihood of errors in interpretation that may result from variability in vocabularies, terms, and definitions used at the point of service.

If all the nurses in a network such as Kaiser Permanente used different vocabularies and classifications, the costs and burden of linking information, while technically feasible, will be more time consuming and costly. The convergence of data elements or records from the network level into meaningful national or international data repositories at the universal level will require, for simplicity, a unicode or single taxonomy. Further, with comparisons between regions of a country, which are based on different definitions of terms, different vocabularies and different classifications, the likelihood of error in definition, interpretation, and aggregation would be more common.

As an example, to clarify these relationships, consider the nurse providing care in schools. This nurse uses terms and phrases that convey relevant patient level information unique to the population. Although some terms and phrases used obviously would cross over into other points of care, such as primary care, certain vocabulary would be necessary for understanding particular situations unique to the school. Information gathered by the nurse at the school point of service would be communicated through one or more systems to the network level. At this level, information would be classified into network classifications, and subsequently channeled to data repositories at the universal level. When, and if, nursing has a universal taxonomy, it is possible that even the point of care and network vocabularies and classifications could converge with the universal level taxonomy.

An example of a concept taken from the point of care to the reference and finally to the universal levels is in Table 3. The first example demonstrates that a vocabulary can change from point of care to network to universal levels for a condition such as ear pain. The second example shows a consistency or a unicode that can be and is being used at all three levels to describe stress incontinence.
The UMLS as Rosetta Stone
The Rosetta Stone as a metaphor for classification and vocabulary challenges in health care provides important insight into our current dilemma. The Stone, discovered by one of Napoleon's officers invading Egypt in 1799, unlocked the meanings behind the ancient language of Egypt ( http://tqd.advanced.org/3011/egypt1.htm). Up until the Rosetta Stone was discovered, three distinct and untranslatable languages comprised the ancient Egyptian language: Egyptian Hieroglyphs, Demotic, and Greek. The Stone enabled the ancient language to be decoded because it contained three inscriptions of specific terms and concepts across the three different scripts.

In nursing and health care the development of terminology and vocabularies has occurred via disciplinary knowledge development, and without a "Rosetta Stone," or system to link various vocabularies. Vocabularies have evolved within disciplines, specialties, and settings; yet a system is needed to cross and link the terms used in health care.

The equivalent to the Rosetta Stone in the U.S. is the Unified Medical Language System (UMLS). The UMLS is a long-term research and development effort being conducted through and coordinated by the National Library of Medicine (NLM), designed to facilitate the retrieval and integration of vocabularies and information from multiple machine-readable biomedical sources. The UMLS retrieves information from numerous sources, including bibliographic material, clinical records, databanks, data repositories, knowledge-based systems, and directories. The major barrier to effective retrieval has been the use of multiple vocabularies and classifications used by different health professionals in the US.

The UMLS electronically links vocabularies and classification systems through a system of four knowledge sources ( www.nlm.nih.gov/pubs/factsheets/umls.html): Metathesaurus, Semantic Network, Specialist Lexicon, and Information Sources Map. The Metathesaurus is organized by concept or meaning, and contains semantic information on approximately 476,322 biomedical and related concepts with 1,051,903 different names. The Metathesaurus contains vocabulary terms, classifications, coding systems, and thesauri developed and maintained by various professional organizations, such as the American Nurses Association, and identifies alternate names for the same concept and relationships between different concepts.

The Specialist Lexicon contains syntactic information about health care-related terms and concept names from the Metathesaurus, as well as other non-health related English words used in communication that are not necessarily included in the scope of the Metathesaurus. The Semantic Network is comprised of a network of general categories or classifications which consistently categorize all concepts from the Metathesaurus, and identifies allowable relationships between terms. The Information Sources Map contains information on the available sources of the machine-readable health related information. Each term or concept is defined and cross-mapped to terms or concepts within other classification systems or vocabularies.

All vocabularies and classifications for nursing that have been approved by the American Nurses Association are incorporated into the UMLS. These nursing vocabularies and classifications in UMLS can be extrapolated, resulting in what could be described as a Unified Nursing Language System (UNLS) ( McCormick, et al, 1994; McCormick & Zielstorff, 1995). This UNLS, when extrapolated, could be tested against large scale nursing data repositories to determine if it is also representative of vocabularies such as acute care, primary care, long-term care, outpatient, community, school health nursing, occupational health, and the many realms where nursing care is delivered.
Computer Based Patient Records Require a Structured Vocabulary
One glaring issue related to vocabulary needs is that there are no computer systems currently available in the world that have the ability to integrate vocabulary, classifications, and language from the point of service to network to universal levels. Integrated medical centers and managed care industries are beginning to demonstrate that health care vocabularies can be merged through transcriptionists, scanners, and object-oriented open systems using Internet technology, although given the potential of computer systems, this manner of merging vocabulary is time-consuming and fragmented.

The use of information technology requires uniform, accurate, and automated patient care data to conduct analyses to improve the quality of care. While there are certainly confidentiality and other ethical concerns that are beyond the scope of this paper, these analyses nevertheless would facilitate the assessment of effectiveness and cost-effectiveness of care. The former Center for Information Technology (CIT) within the Agency for Health Care Policy and Research (AHCPR), has funded cooperative agreements with the NLM to examine applications of the Electronic Medical Record, and research on Computerized Decision Support Systems for Health Providers, both of which have stressed the need for developing, refining, and implementing the use of structured vocabulary.

AHCPR has participated in developing vocabulary standards and tools for improving research and policy utilization of content stored in the computer-based patient record. Between 1994 and 1997, the AHCPR and the NLM funded the only horizontal and vertical systems study to strengthen electronic medical record systems, by developing, updating, and maintaining terminology models. This collaborative study took place within the Mayo Foundation (led by Dr. Christopher Chute) and Kaiser Permanente (led by Dr. Simon Cohn). The study measured the relative merits of terminology additions and changes as they affect clinical practice guideline development and patient data retrieval ( Chute, et al, 1996). This study also evaluated the impact of terminology variations on physician practice and satisfaction.

The development of an electronic toolkit for transmitting and linking laboratory data was also supported by AHCPR research funds. Under the direction of Dr. Clem McDonald, principal investigator, this study developed naming conventions and assigned a fully specified unique name and code for laboratory results reporting, and many clinical measurements ( AHCPR, 1996). The system developed, Logical Observations Identifiers, Names and Codes system (LOINC), is available online to the public at: http://www.mcis.duke.edu/standards/termcode/loinclab/loinc.html [As of 10/15/03, this site is no longer available, but the LOINC codes can be retrieved from www.loinc.org/download].

Finally, AHCPR collaborated with the NLM in a large-scale vocabulary test of the use of controlled vocabularies in health care applications ( Humphreys, 1996). This study analyzed the combination of vocabularies currently in the UMLS to determine the extent to which existing vocabularies serve as an accurate source of vocabulary for health data systems and their clinical applications.
New Technologies to Map, Merge, and Integrate Vocabularies and Different Classifications
The advancement of computer technologies has opened up a world of opportunities for nursing and health care. While computer or electronic servers cannot, by virtue of their construction and purpose, be used to aggregate vocabulary to higher and higher levels of abstraction, or from the point of care (interface), to network (reference), to universal (administrative) levels, they can be used, however, to store content created and retrieved by any number of access methods. These access methods involve the use of additional technologies known as chunkers, matchers, mappers, and routers, and are similar to UMLS mechanisms described earlier. For example, information entered manually or into the electronic patient record is sent (virtually) to a router, where information is tagged or categorized, and text and/or objects are channeled to either a chunker or mapper.

For example, information entered into the record for a patient who presents with vague, nonspecific symptoms would likely be routed to a chunker. The chunker electronically extracts units of information, such as terms and concepts, from computerized text. The chunker then sends information to a matcher, which collapses words and nominal phrases into lexical and semantic classes. On the other hand, more specific patient information is sent from the router to a mapper, which further specifies information by referencing routed terms with any and all possibly related terms; these related terms alert the clinician to information that should be considered in clinical decision-making. Finally, information from the matcher or mapper is channeled to the server, which houses information within a database for analysis and evaluation purposes ( Tuttle, 1998).

Together servers, chunkers, matchers, mappers and routers can be used to

1. link nursing literature with a data repository of nursing information extracted, for example, from an academic medical center, a nursing home, and a community care clinic;

2. integrate into NIC, NOC, NANDA, Omaha, and/or the Home Health Classification system; and

3. include all CINAHL terms, and the Metathesaurus of UMLS to form a nursing knowledge server in the U.S.

This nursing knowledge server, in turn could be used in a network of nursing practice, for example, within a managed care enterprise network.
The Internet provides additional technological capabilities. Through the Internet, vocabularies and classifications from different health care organizations, institutions, or systems are being merged in different ways to create data repositories that provide the basis for measuring cost, quality, patient access to care, and outcomes of care. These new repositories incorporate servers containing various data elements which allow the convergence of data from a variety of sources. Techniques such as data mining and Knowledge Data Discovery (KDD) might be used to determine, with knowledge robots, intelligent clients, or administrative agents, where data and vocabulary similarities and discrepancies exist ( Fayyad, U, et al, 1996). The use of KDD versus natural language processors or text readers is yet undetermined.

Extensible Markup Language (XML) is being used as an integrator of terms at the point of data convergence. XML is an extremely simple dialect of the Standard Generalized Markup Language (SGML), as is Hypertext Markup Language (HTML), all of which are languages of the Internet ( www.w3.org/XML/). XML is intended for large-scale Web applications, vendor-neutral data exchange, and processing of Web documents by intelligent clients. The XML documents are made up of storage units called entities, which can contain either parsed or unparsed data. Parsed data is made up of characters, some of which form the character data in the document, and some of which form markup. Markup encodes a description of the document's storage layout and logical structure.

The use of these technologies is leading to quicker convergence of knowledge sources and the identification of terms used in one vocabulary, yet missing from another. The potential of these technologies is to create a feedback loop so that data extracted from computer-based records can be used to identify new terms when, for example, a nurse uses a term that has not been previously used but should be added to the vocabulary or classification scheme.

Object-oriented technology is also advancing the way that vocabularies and classifications are converging from different sources. At the Internet level, XML and HTML are being fit into a Document Object Model (DOM) ( www.w3.org/DOM/). The DOM is a platform- and language-neutral interface what allows programs and scripts to dynamically access and update content, structure and style of all documents. The DOM provides a set of objects for representing HTML and XML documents, a standard model of how these objects can be combined, and a standard interface for accessing and manipulating them. Vendors can support the DOM as an interface with their proprietary data structures, thus increasing interoperability on the Web.

Collectively, XML and the Object Models render content on the Web a meta-data syntax that fits easily within the framework of the World Wide Web. XML has provided a mechanism for defining and documenting object classes. XML can be used for describing terms that are strictly syntactic, or those which indicate concepts and relations among concepts with relational databases. Therefore, all vocabularies and classifications used at the point of care can be converged within networks, and converged yet again at the universal level. The universal level of taxonomy can be a convergence of many into a unicode or single taxonomy or remain several taxonomies that are linked but not assembled at the global level.

For nursing, the simple solution is to have a single unicode taxonomy at the universal level. While it can be argued that the taxonomy may differ across countries, if convergence at a universal or global level of nursing is desired, different languages must be converged and translated into a single taxonomy. Several nursing taxonomies have already been translated into multiple languages. Through the years, they have served as the unifying concept of the nursing profession, and the use of these taxonomies across numerous countries testifies to their ability to capture nursing information for application in several countries. The International Council of Nursing may represent the logical avenue through which to begin examining these taxonomies and to develop an open universal taxonomy for nursing.
Summary
This paper has identified three levels of vocabulary needs for nursing that are required and the technological advances that make integration possible. At the point of care, there will predictably be many nursing vocabularies and classifications used. At the network level of care, there will be several nursing classifications used. Because of technology breakthroughs such as the Web, XML, object-oriented technology, and relational databases, multiple classifications can continue to be used at the site of patient care and within varying health care systems. However, at the universal level, a unicode of a single taxonomy of nursing, with a single classification scheme will make data entry more simple, meaningful, and useful, data retrieval easier, international comparisons possible, and lower costs for data retrieval. In the end, it is anticipated that these efforts will facilitate the measurement and delivery of continuously improving care. Existing nursing structures that are already universally accepted may be the taxonomy to consider adopting at the universal level.
Kathleen A. McCormick, PhD, RN, FAAN, FRCNA, FACMI
Dr. Kathleen A. McCormick is currently senior principal, Health Division Unit, SRA, International, Inc. Prior to that she was senior science advisor, Agency for Health Care Policy and Research. In that capacity she developed a new program in computerized decision support systems. Prior to her retirement, she held the rank of Captain (Navy rank) in the United States Public Health Service. She was the Surgeon-General alternate to the Board of Regents of the National Library of Medicine; and the Secretary's Alzheimers Task Force. She was a member of the former National Information Infrastructure Task Force, Health Information and Applications Work Group. She is on the ANSI-Healthcare Informatics Standards Board (HISB). She was currently project officer for the establishment of codes and a toolkit of information for laboratory and diagnostic imaging standards on national networks conducted by the Regenstrief Institute. Prior to this assignment, Dr. McCormick was Director, Office of the Forum for Quality and Effectiveness in Health Care responsible for developing the methodologies and process of Clinical Practice Guidelines. In that position, she released the first six guidelines, developed the policies and methodology for guideline development, and instituted the first contract guidelines for AHCPR. In addition, she co-authored one of the first guidelines on Urinary Incontinence in the Adult, and directed the development of an additional 12 completed guidelines that have been released. Prior to the AHCPR assignment, Dr. McCormick was an intramural co-director of a National Institute on Aging incontinence clinical research project and developed the database management system for the collection of data in the nursing home. She was also the Principal Investigator of many projects in a Pulmonary Laboratory of the National Institute on Aging. In addition to many publications on incontinence and pulmonary physiology, Dr. McCormick is also author of articles and books on the use of computers in health care. She is co-author of Essentials of Computers for Nurses, 2nd Ed. The first edition of this book was a book of the year award winner. Dr. McCormick graduated from Barry University in Nursing, Boston University in Clinical Specialty Nursing, and the University of Wisconsin in Physiology. Dr. McCormick is an elected member in the Institute of Medicine, National Academy of Sciences, and the Academy of Medicine for Washington, D.C. These honors are in addition to her being a Fellow in 4 specialty associations: she is a Fellow in the American College of Medical Informatics, The Gerontological Society of America, The American Academy of Nursing, and the Royal College of Nursing in Australia.
* Dr. McCormick was employed at the Agency for Health Care Policy and Research when this paper was written. The paper does not reflect the position of AHCPR or DHSS/PHS.
Cheryl B. Jones, PhD, RN
Cheryl B. Jones, R.N., Ph.D., on research leave from her assistant professor position at the University of Virginia School of Nursing, is currently serving a two-year expert appointment on the health care workforce at the Agency for Health Care Policy and Research in the Washington DC area. While at AHCPR, Dr. Jones will focus on issues related to the cost-effectiveness of health care workforce initiatives, and the impacts of changes in the workforce on the quality and costs of health care delivery, including patient, provider, and organizational outcomes. Her previous work in economics and health care has focused on such issues as the cost-effectiveness of nurse retention strategies, econometric modeling of nursing labor market behaviors, and nursing employment trends within the changing health care environment.

AHCPR. Medical Treatment Effectiveness Program. Research Projects 1989 through 1995. Rockville, MD: Agency for Health Care Policy and Research, March, 1996. Publication No. AHCPR 96-R060.

Chute, C.G., Cohn, S.P., Campbell, K.E., Oliver, D.E., Campbell, J.R.(1996) Computer-Based Patient Record Institute's Work Group on Codes & Structures. The content coverage of clinical classifications. Journal of the American Medical Informatics Assoction. 3:224-233.

Eisenberg, J. (1998). AHCPR focuses on information for health care decision makers. Health Administration Press: HSR, 33(4).

Fayyad, U., Piatetesky-Shapiro, G., Smyth, P. (1990) From Data Mining to Knowledge Discovery. In Fayyad, U., Piatetesky-Shapiro, G., and Smyth, P (Eds) Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge, MA. 1-36.

Gabrieli, E.R. (1993) Computer-based medical terminology and knowledge representation. Studies in Health Technology and Informatics, 6, 81-85. Gabrieli Medical Nomenclature. Buffalo, NY: Gabrieli Medical Information Systems.

Henry, Suzanne B., & Mead, C.N. (1997). Nursing classification systems: Necessary but not sufficient for representing "what nurses do" for inclusion in computer-based patient record systems. Journal of the American Medical Informatics Association, 4(3), 222-232.

Humphreys, B.L., Hole, W.T., McCray, A.T., & Fitzmaurice, J.M. (1996). Planned NLM/AHCPR vocabulary test: Using UMLS technology to determine the extent to which controlled vocabularies cover terminology needed for health care and public health. Journal of the American Medical Informatics Association, 3(4), 281-287.

Jacox, A. (1995) Practice and policy implications of clinical and administrative databases. In N.M. Lang (Ed.), Nursing Data Systems: An Emerging Framework. Washington, DC: American Nurses Publishing.

Lang, N.M., Hudgings, C., Jacox, A., Lancour, J., McClure, M., McCormick, K., Saba, V.K., Stenvig, T.E., Zielstorff, R., Prescott, P., Milholland, D.K., and O'Connor, K.S. (1995) Toward a national database for nursing practice. In N.M. Lang (Ed.), Nursing Data Systems: An Emerging Framework. Washington, DC: American Nurses Publishing.

Martin, K.S. and Sheet, N.J. (1995). The Omaha System: Nursing diagnoses, interventions, and client outcomes. In N.M. Lang (Ed.), Nursing Data Systems: An Emerging Framework. Washington, DC: American Nurses Publishing.

McCloskey, J.C.& Bulechek, G.M. (1996) (Ed.) Nursing Interventions Classification (NIC). Second edition. St.Louis: Mosby.

McCormick, K.A. (1988a). A unified nursing language system. In M.J. Ball, K.J. Hannah, U. Gerdin Jelger, & H. Peterson (Eds.), Nursing Informatics: Where caring and technology meet. New York: Springer-Verlag (pp. 168-178).

McCormick, K.A., & Zielstorff, R. (1995). Building a Unified Nursing Language System (UNLS). In N.M. Lang (Ed.), An emerging framework: Data system advances for clinical nursing practice. American Nurses Publishing. 143-149.

McCormick, K.A., Lang, N., Zielstorff, R., Milholland, D.K., Saba, V., & Jacox, A. (1994). Toward standard classification schemes for nursing language: Recommendations of the American Nurses Association Steering Committee on Databases to Support Clinical Nursing Practice. Journal of the American Medical Informatics Association, 1(6), 421-427.

O'Connor, K. (1998). A disaster on the health care horizon. Washington CEO, 9(4), 12-21,24.

Tuttle, M.S. (March 19, 1998) Personal Communication. Vocabulary, Electronic Medical Records, and the Meaning of Life. ( www.lexical.com)

Warren, J.J. and Hoskins, L.M. (1995). NANDA's nursing diagnosis taxonomy: A Nursing database. In N.M. Lang (Ed.), Nursing Data Systems: An Emerging Framework. Washington, DC: American Nurses Publishing.

© 1998 Online Journal of Issues in Nursing
Article published September 30, 1998

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nursing classification: what's in a name?

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Is One Taxonomy Needed for Health Care Vocabularies and Classifications?

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Kathleen A. McCormick, PhD, RN, FAAN, FRCNA, FACMI Cheryl B. Jones, PhD, RN The use of vocabulary in nursing is a relatively new phenomenon. It has only been in the past 20 years that we have developed and refined the nursing nomenclatures and classification systems. However, as we become more knowledgeable about the information system infrastructure of health care in this country, the linking of vocabularies from disparate sources becomes more critical.

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What big data is doing for health informatics

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What big data is doing for health informatics

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This article goes over big data and has a video about it. Understanding how to accumulate and put in all of the various data points we need to synthesize it to be useful is essential.

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"Big Data" is a major buzzword these days.

The topic has been making waves in other industries for some time, but many of its applications in healthcare are still in their early stages. The use of big data shows exciting promise for improving health outcomes and controlling costs, as evidenced by some interesting use cases, but the practice seems to be defined somewhat differently by each expert we ask.

In this article, we'll explain exactly what big data in medicine is and how (and by whom) it's currently being used to improve patient care today.

What is big data in healthcare?

Big data in healthcare refers to the vast quantities of data-created by the mass adoption of the Internet and digitization of all sorts of information, including health records -too large or complex for traditional technology to make sense of.

The problem has traditionally been figuring out how to collect all that data and quickly analyze it to produce actionable insights. But with emerging big data technologies, healthcare organizations are able to consolidate and analyze these digital treasure troves in order to discover trends, better treat patients, and make more accurate predictions.

Big data in healthcare is a major reason for the new MACRA requirements around EHRs and the legislative push towards interoperability.

I wanted to understand what big data will mean for healthcare, so I turned to big data analytics and healthcare informatics expert Dr. Russell Richmond to discuss what the future holds.

Dr. Richmond is a leading healthcare technology authority whose experience includes building large data analytics companies, advising health system executives as a consultant, and serving on the boards of big data organizations.

The biggest big data benefit: more precise treatments

According to Dr. Richmond, one of the most exciting implications for big data in healthcare is that providers will be able to deliver much more precise and personalized care.

With a more complete, detailed picture of patients and populations, they'll be able to determine how a particular patient will respond to a specific treatment, or even identify at-risk patients before a health issue arises.

As Dr. Richmond put it, "More information yields more granular diagnosis, which creates the opportunity for more precise treatment."

Big data is already being used in healthcare-here's how

Understanding the big picture of big data in medicine is important, but so is recognizing the real-world applications of data analytics as they're being used today.

To that end, here are a few notable examples of big data analytics being deployed in the healthcare community right now.

More accurate diagnoses

Right now, data analytics tools exist that provide better clinical support, at-risk patient population management, and cost of care measurement. Many of these systems have established expansive databases-some with billions of data points-that they can then apply sorting and filtering algorithms to in order to rapidly analyze all that information.

One amazing thing that allows users to do is pinpoint how variations among patients and treatments influence health outcomes. Based on these insights, providers can determine more precise treatment plans for individual patients or patient populations.

For example, according to Dr. Richmond, in a world with big data, "general asthma" may no longer be a sufficient diagnosis. Big data's granularity could allow us to detect and diagnose multiple variants of asthma, with different treatment pathways for each. Data mining could point physicians to the precise treatment plan called for by each patient's unique case.

Predictive medicine

Genomics, as Dr. Richmond pointed out in our discussion, is the next frontier of medicine.

The cost of genome sequencing is falling; you can sequence your complete genome for a couple of thousand dollars these days, down from around $100 million a decade ago. As a result, the volume of genomics data is growing rapidly-and so is our ability to take advantage of that data.

Using genomic data is one way we're already able to more accurately predict how illnesses like cancer will progress.

For example, Emory University and the Aflac Cancer Center partnered with a genomic data analytics organization called NextBio to study data related to medulloblastoma, the most common malignant brain tumor among children.

Medulloblastoma currently has a uniform treatment approach: radiation therapy. Emory and Aflac are using NextBio to look at clinical and genomic data to discover biomarkers that can help predict the metastases of cancer in young patients. Providers, in turn, will use this information to pinpoint targeted therapy approaches based on the biomarkers of their individual patients.

Population health management

While big data's main goal for medicine is to improve patient outcomes, another major benefit to data analytics is cost savings.

Some systems are able to collect information from revenue cycle software and billing systems to aggregate cost-related data and identify areas for reduction.

For example, the state of Rhode Island has partnered with InterSystems to use its HealthShare Active Analytics tool to collect and analyze patient data on a statewide level. The state's Quality Institute then found that about 10% of major lab tests performed in over 25% of the state's population were medically unnecessary-a discovery that has since helped Rhode Island reign in spending as well as improve quality of care.

Big data for the small practice

So, this is all well and good for major health organizations that can afford big data analytics tools today, but what does this mean for the independent practice?

Dr. Richmond summarizes the challenge: "We're spending an awful lot of time putting information in [to digital systems like EHRs], but we haven't yet harnessed the insight that comes from using that information once it's in."

Some strides are being made, though. In a recent survey we conducted of medical providers on the impact of the HITECH Act, interoperability was a very common theme. Patient too are eager to see the benefits of more widely shared health data.

Legislators have been talking about empowering medical providers to become more connected for a long time, but only recently has interoperability truly become imperative for Medicare reimbursement qualification. MACRA is now incentivizing interoperability and requiring the use of EHRs that support interoperable functionality.

Outside of federal regulations, investors also see big data as a huge moneymaker-and more investment will lead to more solutions.

In a few years, Dr. Richmond expects big data and the personalized medicine it facilitates to help eliminate "one-size-fits-all" approaches to treatment. And importantly, he says, this ability to better manage care should result in lowered health costs as well.

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What Is "Big Data" in Healthcare, and Who's Already Doing It?

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"Big Data" is a major buzzword these days. The topic has been making waves in other industries for some time, but many of its applications in healthcare are still in their early stages.

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https://www.epic.com/

Epic allows easy input and access to healthcare data and if used universally could help solve some problems we face in the healthcare field with sharing data.

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This article reviews the challenges that our world has faced in light of COVID in regards to electronic health records.

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In these difficult times, we've made a number of our coronavirus articles free for all readers. To get all of HBR's content delivered to your inbox, sign up for the Daily Alert newsletter.

The Covid-19 pandemic presents the U.S. health care system with a mind-boggling array of challenges. One of the most urgent is coping with a simultaneous glut and dearth of information. Between tracking outbreaks, staying abreast of the latest information on effective treatments and vaccine development, keeping tabs on how each patient is doing, and recognizing and documenting a seemingly endless stream of weird new symptoms, the entire medical community is being chronically overwhelmed.

Sorting through large amounts of information and finding the nuggets that apply to a particular patient's situation is something that computers ought to be good at. But we still have problems of knowing what data is important and what is the right treatment and prevention plan for each patient.

During the Obama administration, the federal government supplied billions of dollars - and providers kicked in billions more - to speed the adoption of electronic health records. But even though up to 96% of hospitals and 86% of physician offices have adopted them, we still don't have EHRs that can rise to the information challenges that clinicians face every day, let alone those posed by Covid-19.

Providers still encounter continual frustration on many levels: user interfaces and usability issues, the quality of the data entered, the limited ability of the data to support discovery and interoperability among systems, just to name a few. These limitations have confounded the ability of clinicians to deliver care during the Covid-19 crisis.

An overhaul of the electronic health record is overdue. It must go beyond fixing the user interface or improving interoperability. It must address the fundamental problems exposed by the pandemic. The overhaul must also support the ability of providers to adopt the new value-based-care business model of health care - one that rewards providers for outcomes rather than the volume of services and that shifts their focus from reactive sick care to the proactive management of health.

To address these needs, the electronic health record must transition from an emphasis on a person's medical record to an emphasis on a person's plan for health and from a focus on supporting clinical transactions to a focus on delivering information to the provider and the patient.

From the Record to the Plan

A redesign of the EHR is essential, but what should it look like? EHRs are reasonably good at the "record" part - keeping track of what happened to the patient - but they must evolve to address the "health" part by helping providers plan for what they want to happen. EHRs could become tools for making those plans and keeping them on track if we design them with that goal in mind.

Intermountain Healthcare, Virginia Mason, and Kaiser Permanente are pioneers in adopting the new health care business model. Their experiences point the way to the next generation of EHRs.

What would a "plan-centric" EHR system look like? It would include:

A library of care plans that covers a wide range of situations. Variations in patient circumstances and preferences would dictate variations in the plans. A patient with well-managed diabetes would have a different plan from one who is still struggling for control. A patient who lived alone would have a different plan from one who lived with a large, supportive family.

Algorithms to form a patient's master plan. Patients hardly ever have just one clear, manageable issue. A master plan would combine appropriate algorithms for treating, say, a patient's asthma, arthritis, depression, and weight reduction, automatically resolving conflicts and redundancies.

Care team support. Each team member - the patient's primary care provider, specialists, nurse practitioners, pharmacists, case managers and the patient - would see both the master plan and their own to-do list. Team members could assign tasks to one another.

The ability to traverse care settings, geographies, and different EHRs. The plan would need to travel seamlessly with the patient. Providers would have interoperable systems that could integrate a patient's plan regardless of its origin.

Decision support and workflow logic.The system must remind team members of upcoming and overdue activities, suggest changes in the plan when patient conditions and care needs change, and route messages to the appropriate team member regarding new test results or patient events.

Analytics for both individual patients and populations. The system must be able to assess how well the plan is achieving its goals, both for the individual patient and for the larger population that may be under the provider's care. It should be able to apply lessons learned in treating one patient to other patients.

Imagine a plan-centric EHR ready to deal with Covid-19, incorporating the latest evidence-based treatments into each patient's care plan based on their current status and underlying health conditions, and then feeding back data on how each patient responded in order to improve the plan for the next patient. Such capabilities could transform outcomes and save many lives.

From Transaction-Oriented to Intelligence-Oriented

As befits systems with origins in billing, the design focus of EHRs has been transactional: documenting a visit, retrieving a lab result, sending a prescription to the pharmacy. This focus is not all bad: It has reduced some types of errors and made it easier to generate work lists and logic to help ensure that the clinical order is complete.

However, exquisite transaction support is not enough to address the challenges that afflict care delivery: failure to follow the evidence, brittle operational and clinical processes, and the near impossibility of keeping up with advances in medicine. EHRs can compound these limitations by being very difficult to update.

We must reimagine the EHR not as a document but as a system that supports the generation and tracking of multiple documents, events, and processes. It must surround each transaction and clinical process with intelligence to ensure clinical appropriateness and sound execution.

It should help ensure that care follows the evidence, identify treatment options that result from the dazzling pace of medical discovery, and alert providers that care processes have deviated from acceptable levels of performance. This intelligence must detect acts of commission (the choice of an outdated treatment approach) and omission (a patient has failed to keep an appointment to see a specialist).

The EHR must provide the ability for clinicians to easily analyze patient data to discover new treatments, uncover safety issues, and identify unusual clinical findings. Such capabilities would have enabled, for example, the much faster discovery of blood clotting in Covid-19 patients.

Intelligence can be leveraged to help address clinician concerns with EHR usability. Logic that presents the physician with data and potential actions tailored to reflect the patient's conditions, the physician's preferences, and the medical evidence can save the physician time and improve the quality of care.

Many of these intelligence and plan capabilities are present to some degree in today's EHR. However, the old fee-for-service business model has not rewarded their refinement and extensive use.

Some providers - including Kaiser Permanente, Geisinger, Intermountain Healthcare, and UPMC - are using their EHRs in this way, but these organizations share a key characteristic: They insure a significant percentage of their patients as well as providing their care, and therefore their financial incentives are more like those of payers. They have already embraced the new value-based-care business model that the rest of the industry is moving toward.

Achieving the Intelligent, Plan-Centric IT Foundation

We will always need medical record documentation and transaction capabilities. An accurate, comprehensive health record is critical to the delivery of care and is also a required legal document.

How can we preserve these functions of the EHR while migrating it to the new intelligent, plan-centric design?

One major obstacle to fixing the EHR problem is that the health care industry is in the middle of a transition to the new business model. But the change is happening so gradually - in fits and starts, depending on the payer and the political environment - that it's difficult for providers and EHR vendors alike to gauge the appropriate moment for a system redesign. Consequently, providers will have to juggle two opposing business models for an unknown period of time, and their information technology portfolio will have to support both.

Exasperated users might support the idea of tossing out what we have and starting over from scratch, but that's not going to happen for a number of reasons: the stupefyingly large cost, the enormous development and implementation time, the disruption of operations, and the potential danger to patients during the transition.

Health care should take a lesson from banking. Instead of rewriting legacy systems, the banking community modified current systems, added complementary applications, and "wrapped" legacy systems with newer technologies and capabilities. To transform the EHR from a (quasi) document into the new design, we need a full array of complementary applications that "wrap around" appropriately modified EHRs and provide significant care-plan and intelligence support. Providers can make these investments as needed to match the pace and address the specific needs of their migration to value-based care and measure the return on investment as they go. These applications and capabilities might include the following:

Population health management. Providers will be accountable for the health and health care of populations of people with common health conditions such as diabetes and asthma. Population-health-management systems combine data from diverse sources (EHRs, claims, patient-monitoring devices, census, and other demographic databases that can track social determinants of health). The population-health-management systems "surround" the EHR so that the provider can view the plan from the record and the population health management system can send alerts and messages to the EHR inbox.

Health information exchanges. Connecting a wide variety of health care organizations in a region or state, the HIE enables them to exchange data about a patient. For example, when a patient presents at an emergency room, the care team can use the exchange to retrieve patient data from other care settings and get a complete clinical picture of the patient. Some HIEs have developed applications that measure regional care quality and costs, portals that enable patients to see their aggregated clinical data, and alert systems that tell a provider when one of its patients has been seen elsewhere.

Patient-health-management applications. These enable consumers to aggregate their health data, view their health status, track their appointments and prescription refills, converse with their care team, participate in care communities, view and alter their shared care plan, and research health issues.

Big data analytics systems. These aggregate very large amounts of health and health care data to compare the effectiveness of treatments, identify medication and device safety problems, facilitate medical discovery, and analyze shifting patterns of patient characteristics and diseases. Artificial intelligence can be used to support automatic correction of data inconsistencies and extraction of data from images, sound, and free text: for example, going through free text and pulling out quality measures or problems that were not previously in a patient's problem list.

Many health systems have begun to adopt the strategy of surrounding their EHRs with the next generation of intelligent, plan-centric capabilities. As the health care business model evolves, organizations such as Mass General Brigham (formerly Partners HealthCare), Memorial Hermann, Geisinger, CommonSpirit Health, and Cedars Sinai are vigorously implementing population health, big data analytics, and patient-health-management applications.

Embracing transformation

Health care delivery is in the early stages of an extraordinary change. This change is being driven by the relentless movement to the value-based care model and the problems exposed by the Covid-19 crisis. This ongoing transformation is paving the way for a new EHR design: a platform that fuses the current EHR with complementary systems, capabilities, and technologies.

Achieving the intelligent, plan-centric health care platform will require a level of industry cooperation that is unlike, and in some ways antithetical to, the way we've always done things. The pandemic has shown us health care collaboration at its best. In that respect, the response to the pandemic mirrors the new business model that we are trying to build.

If our content helps you to contend with coronavirus and other challenges, please consider subscribing to HBR. A subscription purchase is the best way to support the creation of these resources.

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It's Time for a New Kind of Electronic Health Record

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Well before the Covid-19 pandemic struck, electronic health records were the bane of physicians' existences. In all too many cases, EHRs seemed to create a huge amount of extra work and generate too few benefits. The pandemic has made the deficiencies of these systems glaringly apparent.

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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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With healthcare being mostly online digital records keeping, it is truly astonishing how many different electronic medical records systems there are in the US. This article reviews the various number of electronic records. Estimated to be 1100 different EHR systems within the US.

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If you've made any major purchases in the last 10-15 years (and who hasn't?), there's a good chance you've been overwhelmed by decision fatigue , or the anxiety that comes with having too many options and no single "best" choice. It's a nagging feeling, and one that's only become more common in the digital era: When buying a cellphone can be as stressful as picking out a new car, it's easy to think technology might've come a little too far.

Individual consumers aren't the only ones to feel the burn, however. In the healthcare field, an estimated 1,100 electronic healthcare record (EHR) system vendors have products available. And this figure was reported all the way back in 2014, making it a near certainty that even more options are available to medical organizations today.

This surprising figure leads to a simple question: are there so many EHR systems? While it seems like the medical tech market would simply fail to support so many products, the huge variation in options has been credited with causing widespread interoperability problems and even burnout among physicians . This suggests a high level of variability between the purchasing decisions individual healthcare organizations make - and a definite need for better interoperability between these products, particularly those with a large share of the overall EHR market.

A Post-Scarcity World

The factors behind this concern are as numerous as the EHR options available. Foremost is the idea that software products aren't bound by the "usual" rules of scarcity . While a brake-pad manufacturer or kitchen-scale maker must invest in every product that rolls off the assembly line, digital products can be endlessly copied, tweaked, and reiterated with little cost beyond initial development.

This idea applies on the other end, too. In the digital era, a potential buyer will likely use the internet to research options and schedule sales-pitch meetings, opening the door to a list of features and products so big they could never hope to wade through them all. That's a far cry from the limited shelf or storage space a hospital-supply vendor faces when stocking vinyl gloves or saline bags.

A Niche for Every Need

Following this logic, competition doesn't work the same way in software. Intangible products and a large consumer base mean near-endless space for vendors to push their wares. Throw in a healthcare scene where every individual provider or facility requires a specific set of features and you have what amounts to a warm petri dish for EHR system builders: a product for every need, and room for every product.

Think about the sheer number of healthcare organizations out there today. Hospitals. Nursing homes. Telehealth orgs and retail clinics and individual practices. Though all these organizations need some form of electronic recordkeeping, the specifics differ. The average hospital will need a system that allows different wings and internal/external services (labs, pharmacies, etc.) to effectively communicate, for instance, while the individual clinic's required features will largely center around patient health, prescription history, referrals, and other factors.

These disparate needs frequently result in situations where healthcare systems are forced to throughout the various "faces" of their operations; in turn, a need for multiple systems can occur as a natural side effect of business, where companies change directions and implement multiple EHR systems implement new methods and systems of care. It can also present significant issues when mergers and acquisitions are involved , leading one expert to say, "The most misleading statement in business is 'Nothing will change after the merger.'"

A Shrinking Problem

As noted above, a highly visible interoperability crisis has emerged at the center of a huge EHR market. Because solutions vendors have little economic motivation to make a product that plays nice with competing offerings - and because competing organizations that may ultimately send patients to one another face the same problem regarding their software purchases - interplay between disparate systems has been a complicated, oft-expensive affair. Historically, companies have openly grumbled over transferring data to outside systems, with government intervention attempting to soften the blow .

The good news: Things are looking up on this front. Spurred by governmental and public pressure, major EHR vendors have shown an increased willingness to play ball with one another; even better, they've bought into standardized frameworks such as Carequality , making interoperability between future offerings a guarantee instead of an expensive hassle.

This shows particular promise for organizations harangued by decision fatigue. Though it may shrink the EHR marketplace - with so many leading vendors signing into the framework, it may make little sense for organizations to choose smaller alternatives that haven't bought in - purchasing connected products mitigates the risk of making a bad purchase, and makes everything from mergers to system switches easier. It also means less complexity for medical professionals.

So, Why Are There So Many EHR Systems?

With the huge variety in healthcare organizations and settings, an abundance of EHR systems is expected. The class of software simply covers too many needs and carries out too many functions. More, a larger plate of options means a better chance at granular control for the healthcare companies buying the products.

That said, there's certainly room for progress. With vendors now showing a more share-friendly outlook, life will likely improve for end users and their patients soon. Providers' best bet is to plan accordingly - however overdue this renaissance in electronic records system may feel.

How many EHR systems do you employ? Do they work well together? Let us know in the comments!

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Why Are There So Many EHR Systems? Analyzing a Bloated Market | Healthcare Tech

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If you've made any major purchases in the last 10-15 years (and who hasn't?), there's a good chance you've been overwhelmed by decision fatigue , or the anxiety that comes with having too many options and no single "best" choice.

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This article reviews the challenge that gets presented in healthcare when doctors cannot see or have access to health information from outside hospitals/physicians offices. It proves to be a big challenge that we as health care providers currently face in the informatics world.

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Few U.S. hospitals can fully share electronic medical records

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Less than one in three U.S. hospitals can find, send, and receive electronic medical records for patients who receive care somewhere else, a new study suggests.

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This article reviews what datasets are and how they are important within healthcare. They go over what major data sets exist within certain organizations such as WHO, CDC etc. It is a nice article to go to if you ever wonder what organizations contribute to major datasets.

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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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This article is interesting as it goes over the predictive abilities of computer systems based on datasets it reviews. These types of technology are how we have SIRS alerting criteria within health charting and other quality care objective standards we can reach.

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Predictive analytics in health care using machine learning tools and techniques

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When we have a huge data set on which we would like to perform predictive analysis or pattern recognition, machine learning is the way to go. Machine Learning (ML) is the fastest rising arena in computer science, and health informatics is of extreme challenge.

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This article goes over what is going on in telehealth now. It also goes through what needs to be done in order to ensure that telehealth continues to be successful in the future.

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The future of telehealth: Informatics, scalability and interoperability

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A Philips executive describes what’s happening now with virtual care – and what needs to happen to ensure a solid future for telemedicine and remote patient monitoring.

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The article goes over the challenges and infrastructure needs for COVID-19 care. It also reviews specific benefits that concern telehealth and the treatment/management of COVID-19

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Leveraging health system telehealth and informatics infrastructure to create a continuum of services for COVID-19 screening, testing, and treatment

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AbstractObjectives. We describe our approach in using health information technology to provide a continuum of services during the coronavirus disease 2019 (COVI

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This is a systematic review of how gamification affects patients in relation to more serious games. This is an excellent review that also goes over cost benefit of gamification in healthcare.

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A systematic review of gamification in e-Health

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Gamification is a relatively new trend that focuses on applying game mechanics to non-game contexts in order to engage audiences and to inject a littl...

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This article goes over what gamification is. It also reviews how it can help to educate patients within the healthcare domain.

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It's no secret that technology continues to shape the healthcare industry, providing new avenues for the delivery of patient care, the management of patient records and more. Demonstrating its wide impact, healthcare technology isn't always patient-facing nor directly applicable to care contexts. In fact, some of the most significant innovations involve the use of technology in a more behind-the-scenes capacity, including in the training and development of clinical professionals.

Gamification techniques, in particular, have provided new channels for instruction and professional development among doctors, nurses and other clinicians. These platforms and techniques draw on the tropes and structures of video games, but employ them in a more focused educational context.

One of the best ways to learn about the use and impact of gamification techniques in healthcare education is to connect with a forward-thinking online healthcare degree program, where the latest innovations are made central to the student experience. The right education can provide a valuable familiarity with gamification technologies, and prepare students to implement these technologies efficiently and meaningfully.

What is Gamification?

Gamification can be defined as the use of gaming concepts in non-gaming settings. For example, structural elements that are native to video gaming can be employed in a wide range of training settings, enabling students or practitioners to compete alone or against themselves as they seek to hone their skills.

Gamification techniques can be employed not just in professional training settings, but also in everyday life. For example, any consumer fitness apps and step-counters are heavily gamified, enabling users to track their progress, try to beat their personal records or connect with other users in friendly competition. The goal of gamification is to motivate the user into forward momentum, and also provide a way of quantifying and rewarding progress.

Gamification Techniques to Improve Education

Some of the most promising aspects of gamification pertain to education. Looking at healthcare in particular, gamification can take the form of simulations, virtual reality (VR) constructs and games designed with educational purposes in mind. Patient-centered apps may be used to help monitor the progress of an individual's nutrition, daily physical activity, physical therapy and more.

Healthcare Professional Training

There are a number of ways in which using gamification can improve the process of training healthcare workers. For instance, traditionally, professors in medical schools use multiple choice quiz games to test students and measure their retention of key concepts. Alternatively, some professors use virtual reality simulations to gauge their students' ability to treat patients, allowing them to practice certain skills in a realistic and high-pressure environment, but without the need to involve actual patients. There may also be examples of gamification that don't require technology, even if that's something as simple as having medical students compete to find creative solutions to hypothetical healthcare dilemmas, or using a point system to reward those who most rigorously follow medical protocols and procedures.

Patient Education

Additionally, medical organizations implement gamification techniques to capture the attention of patients and improve their healthcare education. Organizations do this through a range of healthcare and therapy apps as well as remote patient monitoring. Examples range from CardioTeach, which provides patients with an overview of healthy heart functions, to the Decide app, which helps patients make informed choices about the kinds of care they should seek.

The goal of healthcare is to provide patients with the best medical care, and gamification techniques make this possible. Games and interactive apps can help patients become their own care advocates, helping them develop their understanding of the best nutrition, exercise, rest, and recovery principles and empowering them to monitor their own progress along the way. This also provides patients with a better understanding of their own health and wellness needs, enabling them to be more collaborative and communicative with providers as they work together toward a treatment plan.

Learn Gamification Techniques to Improve Education

As students pursue careers in healthcare, they may encounter some uses of gamification in the classroom. Additionally, there is great merit to learning how to harness and implement gamification techniques within a professional setting, whether to help develop team members or educate patients.

There are a number of key skills necessary to implement gamification in these ways. Here are a few common examples.

Technological savvy is required to leverage the most recent innovations and stay up to date on how gamification techniques are evolving.

It is also important to master the art of teamwork, which is necessary to implement gamification on a broad level and maintain consistent standards.

Leadership skills are necessary to set a vision and provide a clear sense of direction for how gamification can be used.

Customer-relations skills are also required, as it may be necessary to help patients or staff understand and troubleshoot their applications.

Sales and marketing skills can be helpful as healthcare providers seek buy-in for their gamification efforts.

Organizational, decision making and goal setting skills can all be invaluable for building a professional culture in which gamification is valued and innovation is sought.

An excellent way to develop these skills is to pursue a degree from a leading-edge organization in a field that is related to healthcare but also inclusive of foundational technology skills.

Discover the Benefits of Gamification in Healthcare

Through the online healthcare degree programs at the University of Central Florida, students can discover how gamification techniques can be beneficial in today's healthcare environments - and how they can reshape the future of patient care. Learn more about the wide range of healthcare degree programs offered online at UCF.

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Gamification Techniques in Healthcare to Improve Education | UCF Online

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It's no secret that technology continues to shape the healthcare industry, providing new avenues for the delivery of patient care, the management of patient records and more. Demonstrating its wide impact, healthcare technology isn't always patient-facing nor directly applicable to care contexts.

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In this article, it goes over how engagement with patients can be affected by patient portal access. It is a systematic review that describes how safety and efficacy is affected by patient access to their patient portal.

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Monitoring Editor: Gunther Eysenbach

Reviewed by Bin Xie, Ann McAlearney, Gillian Strudwick, and Taya Irizarry

Ronald Dendere, PhD,#¹ Christine Slade, PhD,#² Andrew Burton-Jones, PhD,#³ Clair Sullivan, MBBS (Hons), MD, FACHI,#1, ⁴ Andrew Staib, MBBS, FACHI,#5, ⁶ and Monika Janda, PhD #¹

Abstract

Background

Engaging patients in the delivery of health care has the potential to improve health outcomes and patient satisfaction. Patient portals may enhance patient engagement by enabling patients to access their electronic medical records (EMRs) and facilitating secure patient-provider communication.

Objective

The aim of this study was to review literature describing patient portals tethered to an EMR in inpatient settings, their role in patient engagement, and their impact on health care delivery in order to identify factors and best practices for successful implementation of this technology and areas that require further research.

Methods

A systematic search for articles in the PubMed, CINAHL, and Embase databases was conducted using keywords associated with patient engagement, electronic health records, and patient portals and their respective subject headings in each database. Articles for inclusion were evaluated for quality using A Measurement Tool to Assess Systematic Reviews (AMSTAR) for systematic review articles and the Quality Assessment Tool for Studies with Diverse Designs for empirical studies. Included studies were categorized by their focus on input factors (eg, portal design), process factors (eg, portal use), and output factors (eg, benefits) and by the valence of their findings regarding patient portals (ie, positive, negative, or mixed).

Results

The systematic search identified 58 articles for inclusion. The inputs category was addressed by 40 articles, while the processes and outputs categories were addressed by 36 and 46 articles, respectively: 47 articles addressed multiple themes across the three categories, and 11 addressed only a single theme. Nineteen articles had high- to very high-quality, 21 had medium quality, and 18 had low- to very low-quality. Findings in the inputs category showed wide-ranging portal designs; patients' privacy concerns and lack of encouragement from providers were among portal adoption barriers while information access and patient-provider communication were among facilitators. Several methods were used to train portal users with varying success. In the processes category, sociodemographic characteristics and medical conditions of patients were predictors of portal use; some patients wanted unlimited access to their EMRs, personalized health education, and nonclinical information; and patients were keen to use portals for communicating with their health care teams. In the outputs category, some but not all studies found patient portals improved patient engagement; patients perceived some portal functions as inadequate but others as useful; patients and staff thought portals may improve patient care but could cause anxiety in some patients; and portals improved patient safety, adherence to medications, and patient-provider communication but had no impact on objective health outcomes.

Conclusions

While the evidence is currently immature, patient portals have demonstrated benefit by enabling the discovery of medical errors, improving adherence to medications, and providing patient-provider communication, etc. High-quality studies are needed to fully understand, improve, and evaluate their impact.

Keywords: patient portal, electronic medical record, electronic health record, patient engagement, digital hospital

Introduction

The increasing adoption of electronic medical records (EMRs) by hospitals presents an opportunity for patients to access their clinical data and actively participate in their care via the EMR. Hospitals and other health care organizations can facilitate patient access to their EMR information through patient portals. Patient portals can provide secure, online access to personal health information [ 1] such as medication lists, laboratory results, immunizations, allergies, and discharge information [ 2]. They can also enable patient-provider communication using secure messaging, appointments and payment management, and prescription refill requests [ 2, 3].

The increase in patient portal implementation is, in part, due to some preliminary evidence that they may improve patient engagement [ 4] and health outcomes such as medication adherence [ 5- 10]. Government incentive programs and regulations also influenced some health care organizations to implement patient portals [ 11, 12]. For example, in the United States, implementing patient portals was a way to meet the requirements for Meaningful Use, Stage 2, of the Healthcare Information Technology for Economic and Clinical Health Act [ 13].

Promoting patient involvement in health care delivery may lead to improved quality and safety of care [ 14, 15] by enabling patients to spot and report errors in EMRs, for example [ 6]. Some patients recognize the role of patient portals in their health care, reporting satisfaction with the ability to communicate with their health care teams and perform tasks such as requesting prescription refills conveniently [ 3, 16]. Portal use may reduce in-person visits, visits to emergency departments, and patient-provider telephone conversations [ 3, 8- 10, 12, 16]. Despite the potential of portals, already used in the ambulatory setting for some time, implementation in the inpatient setting has only recently gathered momentum [ 17- 19]. The inpatient setting presents additional challenges for implementing patient portals [ 18, 20]. Clinical conditions leading to hospitalization are often acute and the amount of medical information generated during this time can be extensive, which may overwhelm patients [ 20] and challenge information technology to rapidly display this information.

The aim of this study was to review literature describing patient portals tethered to an EMR in inpatient settings, their role in patient engagement, and their impact on health care delivery in order to identify factors and best practices for successful implementation of this technology and areas that require further research. Our review aims to inform researchers, health care organizations, and policymakers.

Methods

Search Strategy

The PubMed, CINAHL, and Embase databases were searched for articles published between 2005 and 2017 using keywords related to patient engagement, electronic health records, patient portals, and their associated subject headings in each database: the full search terms for each database are provided in Multimedia Appendix 1.

Study Selection and Quality Assessment

Figure 1 shows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram for the systematic search and selection process. Inclusion criteria for articles were (1) written in English, (2) hospital inpatient setting, and (3) patient portals tethered to a hospital EMR. The initial combined database search produced 703 articles, and an additional 16 were identified by scanning their reference lists. After eliminating duplicates, the article abstracts were independently reviewed by three authors to identify articles that did not meet the inclusion criteria. This led to 617 articles being excluded. Full-text screening was conducted for the remaining 102 articles, leading to the identification of 62 articles that did not meet the inclusion criteria. At each stage, the authors met to reconcile, by consensus, any disagreements about article inclusion. An independent coder also coded the 102 articles for inclusion/exclusion using our criteria, and interrater agreement was high (Cohen kappa=.75). In cases of disagreement, we opted to include the article if it addressed a potentially important policy issue (eg, privacy issues, rural/urban divide). To ensure we included as many up-to-date papers as possible, we periodically conducted database searches for new articles after the initial search. This step was performed by just one author because by this stage, the authors had established a well-developed understanding of the inclusion/exclusion criteria. This periodic update, up to August 2018, identified another 18 articles for inclusion.

The included articles were assessed for quality. Two authors independently scored each article's quality using the most recent version of A Measurement Tool to Assess Systematic Reviews (AMSTAR 2) [ 21] for review articles and the Quality Assessment Tool for Studies with Diverse Designs (QATSDD) [ 22] for qualitative, quantitative, and mixed empirical studies. We classified the overall AMSTAR 2- or QATSDD-derived score for each paper on a 5-point scale (very low, low, medium, high, very high), thus establishing the qualities of all articles on one scale. The ratings by the authors and the independent coder were highly correlated ( r=.81). A third coauthor then independently reviewed and reconciled the scores. We assigned valence ratings to each article to characterize the overall findings in each article regarding patient portals as positive, negative, or mixed [ 23].

Data Analysis

We analyzed the information extracted from the included articles by categorizing the themes related to the implementation of patient portals into inputs, processes, and outputs. The inputs are the material (eg, hardware and software) and nonmaterial (eg, leadership) components that facilitate or impair the establishment or use of the portal. Processes include the interactions of the users with the portal. Outputs comprise the results of the implementation or the use of the portal. Through the analysis, we identified 14 themes within these three categories, shown in Textbox 1.

Portal design: umbrella term for all design-related aspects of the portal including portal interface, content, features, and functions

Usability: extent to which a patient portal has the property of being able to be used by patients, caregivers, and health care teams to enhance patient engagement with effectiveness, efficiency, and satisfaction

Barriers: factors that hinder widespread adoption or portal use

Facilitators: factors that motivate or enable users to sign up for or actively use a portal

User training: equipping patients and health care team members with the necessary skills and knowledge to effectively use a portal

Organizational factors: culture of a health care organization; decisions and actions it takes when an initial consideration is made to implement a patient portal

Adoption: from a patient perspective, adoption is the registration for a portal account; from a health care provider perspective, portal adoption refers to acceptance and promotion of the portal [ 16]

Use: active engagement and continued use after signing up for a portal [ 4]

Information: all aspects associated with providing patients with clinical and nonclinical information via a portal

Communication: all aspects associated with portal-based patient-provider communication

Patient engagement: active involvement of patients in their own health care

User perceptions: thoughts, feelings, and opinions of patients, caregivers, and health care team members about their experiences with a patient portal

Health outcomes: impact of patient portals on clinical indicators

Benefits: value provided by patient portals to the health care delivery process

Results

Overview: Search Strategy, Study Selection, and Quality Assessment

Details of the 58 articles included in this review are given in Multimedia Appendices 2 and 3. The majority of the articles (41) are indexed in all three databases; 13 articles are indexed in two databases, and only 4 are in indexed in a single database. The PubMed and Embase databases each index 56 articles while CINAHL indexes 41 articles. Nineteen articles described qualitative studies, 18 quantitative studies, and 9 mixed method studies, while 12 were reviews. Our quality assessment placed 19 articles in the high- or very high-quality categories, 21 in the medium category, and 18 in the low- or very low-quality categories. Twenty-nine articles were assigned mixed valence, 16 articles were assigned positive valence, and 2 were assigned negative valence. Valence was not applicable or could not be drawn in 11 articles (ie, those that examined portals but that were not focused on evaluating them in any way).

There was a spread of articles addressing themes over the inputs, processes and outputs categories (see Multimedia Appendix 3). However, only one-third of the articles (19/58) addressed the full spectrum of categories.

Textbox 2 shows that there was corroboration among studies for some findings (eg, information) but contradicting findings in some themes (eg, health outcomes). We did not observe trends in the findings that were related to study quality or design (ie, there was no association between study quality or study design and reported findings).

Artificial intelligence was used to enhance portal designs [ 28]

Portals can be designed for specific diseases or medical conditions [ 29]

Lack of appropriate training [ 33]

Doubt of the portal's usefulness [ 38]

Anxiety associated with viewing personal medical information [ 38]

Data security and privacy concerns [ 34, 40]

Lack of encouragement from providers [ 3, 40]

Patients were trained using videos and reading material [ 24, 33]

Rural hospitals were more likely to report costs and obtaining staff cooperation as barriers to health information technology (HIT; including patient portals) adoption than urban hospitals [ 46]

Small hospitals were more likely to report cost-related barriers than large hospitals [ 46]

Leaderships had crucial roles in the implementation of patient portals, working closely with developers in system design, developing policies to guide user training, and integrating portals into clinical workflows [ 33]

Implementation of patient portals varied across organizations due to different interpretations of government legislations by the health care organizations [ 47]

Some patients preferred to have access to their entire EMR, including doctors' notes [ 24, 41]

Patients and caregivers expressed interest in using portals to communicate with health care staff [ 6, 24, 30, 36] but not many actually used this feature [ 30, 44]

Some patients wanted an option to send messages to specific staff members and an indication of whether a message had been read and when to expect a response [ 33]

Some portals did not significantly improve patient engagement [ 17, 50] but others did [ 6, 41, 43, 51, 52]

Health care teams had preimplementation concerns about disruptions to workflows and potential for large volumes of patient messages, but such concerns did not materialize [ 45]

Patients who used a disease-specific portal were more satisfied than those who used a generic portal [ 29]

Improved adherence to medication [ 3, 9, 39, 43, 52, 57]

Patient Portal Inputs

Forty articles addressed themes in the inputs category. We identified 22 articles that addressed portal design. In an iterative design approach, feedback from patients, including requests for electronic games and other functions, was used to refine designs. Enhancements included links to medical education, artificial intelligence techniques, and disease-specific design.

As shown in Textbox 2, poor portal designs caused usability difficulties for some patients. Those difficulties were among the barriers to portal use. Textbox 2 also shows that more barriers to portal use than facilitators were identified in the reviewed literature. Various methods were used to train patients and staff to use the portals. However, training methods were not optimal [ 5, 33, 44]. For example, in one study, patient training was delivered via an 11-minute video that was not well received, with less than a third (26.3%) watching the entire video [ 33]. In another study among health care staff, doctors had the lowest confidence in a patient portal, and they doubted their training was sufficient to allow them to effectively use it [ 5].

Organizational factors (leadership, staff support, and key decisions, etc) was the least addressed theme. However, the findings summarized in Textbox 2 indicate that organizational factors are likely the most crucial in determining whether or how patient portals are implemented.

Patient Portal Processes

Thirty-six articles addressed themes categorized as processes. Five articles addressed portal adoption while use was addressed in 18 articles (see Multimedia Appendix 3). Sociodemographic characteristics of patients such as race, age, gender, level of education, and social status were predictors of both portal adoption and use [ 4, 6, 33, 48]. In addition to sociodemographic factors, there was higher portal use among patients with greater disease severity (eg, advanced cancer) [ 4], but at any given time, patient condition influenced portal use as described in Textbox 2.

Patient-provider communication, usually in the form of secure messages, is a key feature of inpatient portals [ 26]. Despite expressions of interest in this feature, actual use was low [ 30, 44]; in a pediatric study with 296 parents participating, only about 6% sent messages to health care teams via the inpatient portal [ 30]. Most portals enabled patients to send messages to a single mailbox that was accessed by staff members on duty, but some patients wanted to communicate with specific staff members [ 33]. Similar to adoption and use, sociodemographic factors were predictors of which patients used the messaging feature (see Textbox 2).

Patient Portal Outputs

The outputs category was addressed by 46 articles, and 24 articles addressed patient engagement. Results of patient engagement were mixed: portals in some studies did not cause statistically significant improvement, but patients in other studies reported that portals enabled better engagement in their care.

User perceptions was the most commonly addressed theme across all categories (35 articles), and Textbox 2 shows that users perceived some functions of portals (eg, access to information) as inadequate but perceived other functions (eg, communication) as very useful. Despite dissatisfaction and concerns with some aspects of the portals, users' perceptions of the patient portal concept as a whole were mostly positive. For example, in one study, 90% of participants reported overall satisfaction with the portal, 89% thought that the portal reduced errors, and 94% agreed that the portal improved care delivery [ 30]. In another study, 84% of participants described a patient portal as useful and 90% reported that they would recommend it to their peers [ 32]. Health care staff appreciated the reasons for implementing patient portals and their own roles and responsibilities in the process, but they stated that they would like to receive sufficient training [ 5].

Twenty articles addressed the association between patient portals and health outcomes, such as medical errors, readmissions, and mortality. Results were mixed as some studies did not show positive associations between portal use and health outcomes [ 2, 4, 20]. For example, a retrospective study of cancer patients found no association between portal adoption or use and adverse events [ 4] and another retrospective study found no association between portal use and 30-day readmission or inpatient mortality [ 20]. However, in a third study, active portal use significantly improved glycated hemoglobin levels [ 54]. Importantly, patient portals in several studies facilitated discovery of EMR errors by patients, particularly medication errors [ 24, 26, 30, 54- 56].

Finally, 7 articles addressed the benefits of patient portals, and Textbox 2 shows that portals offers a wide range of benefits for patient care.

Discussion

Principal Findings

This systematic review examined 58 articles studying inpatient portals. Although there was overlap in the themes reflected in these studies, there was also significant variation in the setting, patient population, software, outcomes assessed, and study methodology, making it hard to come to a definitive conclusion on whether inpatient portals are beneficial. This is further shown by the higher number of included studies that were judged to relay mixed valence than those with positive valence. However, more studies relayed positive than negative valence, showing that patient portals may be beneficial for health care. We discuss below the patient portal input, process and output factors that contribute to this assessment, and the areas of research that need focus in order to improve patient care. Figure 2 provides a visual summary.

Patient Portal Inputs

When assessing common themes within the inputs category, portal design, usability, and barriers were more widely covered than user training and organizational factors. Many articles showed that good portal design is crucial for usability and adoption by patients. While most studies involved users in the design process, more needs to be done to overcome design-related barriers, particularly for people with low health literacy [ 6, 19, 24]. Increasing the use of enabling technology such as voice-commanded digital assistants, artificial intelligence, and natural language processing could make the systems cater to a wider range of audiences. Designing platforms so that they can be displayed on users' personal mobile devices could be another enabler of portal adoption.

Patients' concerns regarding privacy and security of their medical information [ 34, 40] are particularly relevant in cases where caregivers need access to patients' EMRs. Discussion of privacy issues in cases of caregiver access to patient medical information in the literature is limited. In the absence of formal policy for caregiver access, patients may opt to disclose personal log-in details to their caregivers, which is not recommended [ 26].

The results of user training showed that reliance on a one-size-fits-all approach may not be effective for educating users because of varying preferences. Training could be enhanced by providing information that directly addresses common patient concerns (eg, information security) and health care staff concerns (eg, workflow changes).

Information on the best timing to deliver training material to patients is lacking in the literature. While staff training can be scheduled ahead of deploying a patient portal, patients may only encounter a portal upon hospitalization. Furthermore, the severity of their condition may limit their ability to focus on or understand the training material. Innovation may be needed to inform patients about portal services prior to hospitalization. For example, patients could sign up for outpatient portals which would be similar in design to the inpatient portals to ensure seamless transition between the two portals. Hospitals could also collaborate with medical insurance providers to make training material available to potential patients already signing up for hospital insurance.

Patient Portal Processes

Associations between patient sociodemographic characteristics and portal use [ 4, 6, 33, 48] indicate that patients who are most vulnerable (eg, those with low health literacy or seriously ill) would be least likely to benefit from patient portals. Recruitment and participation bias when engaging participants in user testing mean that actual use by patients may not reflect the use in the initial testing phases. Therefore, health care organizations may need to conduct multiple studies to iteratively address factors that influence portal use within the communities they serve.

The varying preference among patients for level of access to EMRs [ 6, 18, 24, 26, 30] and staff concerns with unlimited patient access to sensitive information (information that could cause anxiety for patients) [ 24, 41, 49] present challenges for health care organizations. Health care organizations could fulfill these diverse information needs by flagging sensitive information and warning patients that despite having access to the information, they can opt to receive it in person from a health care team member. Some hospitals already have guidelines for releasing sensitive information to outpatient portals [ 26], and those guidelines could be adapted to the inpatient setting.

Health care organizations may view patient requests of nonclinical information and functions such as electronic games [ 18, 19, 24, 30] as fringe requests that could raise developmental costs. However, granting these requests may improve portal adoption and use and overall patient satisfaction. Also, patients have requested background information on their health care staff, but staff perceptions about the disclosure of such information to their patients is not known [ 19, 24]. Further research is needed to uncover staff perceptions in this regard and explore ways of implementing this service.

Portal-based patient-provider communication is potentially beneficial but may also be disruptive [ 26]. Although staff concerns about potential interruptions caused by constant patient messaging did not materialize [ 45], in practice, staff members may be overwhelmed by messages at any time. Structured messaging may be a solution that ensures patients communicate only important and relevant information [ 26]. If health care organizations decide to enforce structured messaging, they should prioritize patient safety and therefore avoid restrictions that could prevent patients reporting genuine concerns.

Patient Portal Outputs

The literature explored some, but not all, potential outputs of patient portal implementations. Most of the studies assessed implementation of patient portals using interim outcomes such as user perceptions, and few studies addressed important objective outcomes such as length of stay, morbidity, or mortality [ 2, 4, 20, 54].

Research in the evaluation of patient portals is also currently limited. Standardized evaluation frameworks and measures are needed to enable better comparisons of patient portal implementation and outcomes in the future.

Limitations

While an extensive search was undertaken, the majority of the included studies were conducted at single locations, used outcome measures that were not comparable to those used in other studies, and had small sample sizes. That means results of those studies may not be generalizable to other population groups. Also, a number of the studies were conducted in controlled settings, such as closed-door observations and interviews, which would not be representative of hospital settings. The absence of standardized evaluation tools means the results could not be compared or synthesized, and we were thus limited to providing a descriptive summary of findings only. Most studies that addressed user perceptions or patient-reported results depended on the opinions of those who completed end-of-study questionnaires or interviews; such results could be biased as they may lack feedback from participants who felt uneasy about giving negative feedback. Finally, our conceptual framework enabled us to group the findings gleaned from the included articles, although there was some overlap in the categories caused by interdependence in some of the themes.

Conclusion

The review results suggest that the available evidence for inpatient portals is currently immature. Standardized outcomes assessment and more high-quality studies with objective outcomes (length of stay, mortality, and morbidity) are required to fully understand the impact of such portals.

Acknowledgments

This study benefited from the support of the Australian Research Council (ARC FT130100942), the National Health and Medical Research Council Translating Research into Practice Fellowship (APP 1151021), and the University of Queensland.

Abbreviations

AMSTAR

A Measurement Tool to Assess Systematic Reviews

EMR

electronic medical record

HIT

health information technology

QATSDD

Quality Assessment Tool for Studies With Diverse Designs

Multimedia Appendix 1

Search terms for the PubMed, CINAHL, and Embase databases.

Multimedia Appendix 2

Detailed summary of each article selected for inclusion in the review.

Multimedia Appendix 3

Themes addressed by each article.

Footnotes

Conflicts of Interest: None declared.

References

48. Mikles SP, Mielenz TJ. Characteristics of electronic patient-provider messaging system utilisation in an urban health care organisation. J Innov Health Inform. 2014 Dec 18;22(1):214-221. doi: 10.14236/jhi.v22i1.75. doi: 10.14236/jhi.v22i1.75. [[ PubMed] [ CrossRef] [ CrossRef] Google Scholar]

Articles from Journal of Medical Internet Research are provided here courtesy of Gunther Eysenbach

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Patient Portals Facilitating Engagement With Inpatient Electronic Medical Records: A Systematic Review

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Monitoring Editor: Gunther Eysenbach Reviewed by Bin Xie, Ann McAlearney, Gillian Strudwick, and Taya Irizarry Ronald Dendere, PhD,# 1 Christine Slade, PhD,# 2 Andrew Burton-Jones, PhD,# 3 Clair Sullivan, MBBS (Hons), MD, FACHI,# 1, 4 Andrew Staib, MBBS, FACHI,# 5, 6 and Monika Janda, PhD # 1 Engaging patients in the delivery of health care has the potential to improve health outcomes and patient satisfaction.

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This article goes over the pros and cons of patients having access to their patient portal. This also goes over the challenges with patients not knowing or understanding diagnostic values.

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Patient portals have become more accessible over the last decade. Patients can arrange appointments, review diagnostic test results, request prescription refills, communicate with clinicians, and access their medical records. In addition, portals typically allow proxy access for caregivers of children or older patients, thereby facilitating family engagement and oversight where appropriate. Patient portal use, especially in the primary care setting, has increased patient engagement, decreased costs, and improved communication and care coordination.

Patients who are active users on healthcare portals can contribute to and potentially improve the ED diagnostic process in several ways. For patients seeking care in an ED that is not affiliated with their usual health network, access to their "home" portal may allow patients and their ED providers to review medical records from the external system, view previous test results, and access additional clinical details to guide the diagnostic process. Similarly, patients who are discharged from the ED without a definitive diagnosis can share ED provider notes and diagnostic testing results with their primary care provider (PCP).

Despite the obvious benefits of portal access and patient engagement, recent studies have shown relatively low portal use in the ED setting. One study assessed portal use by ED patients at an academic medical center and found that less than 10 percent of all radiology and laboratory test results ordered in the ED were ultimately reviewed by patients via the portal. Rates of portal use were higher among patients ages 18-60 years and patients who self-identified as Asian or Caucasian, compared with patients in other age groups and patients who self-identified as Black or Latinx.

The relatively low use of portals underscores the need to enhance the acceptability, accessibility, and usefulness of portals for use after ED visits. These results are also consistent with previous studies that have demonstrated disparities in portal use by underrepresented minorities and highlight the importance of future work to mitigate disparities in technology access and adoption.

Several implementation and usability issues specific to ED portal use require further investigation. For example, patients seeking emergency care outside of their primary healthcare system must be identified and expeditiously granted access to the portal when they visit the ED. In addition, portals should enable patients to directly share or forward information about test results and diagnoses from their ED visit with their PCP, if the PCP cannot access that system.

Other concerns relate to patients' ability to interpret test results and access their healthcare team to understand the implications of those results. For example, the ED is not structured in a way that allows postvisit followup, and research has shown concerns surrounding how patients understand and interpret both normal and abnormal test results communicated via a portal.

Future research in ED portal use should address issues related to user access, usability challenges, interoperability between different electronic health record systems, and best practices for disclosing and explaining results of diagnostic testing given the constraints of the ED environment.
Return to Contents

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Health Information Technology for Engaging Patients in Diagnostic Decision Making in Emergency Departments

# data[].attributes.attachment_link.description
Patient portals have become more accessible over the last decade. Patients can arrange appointments, review diagnostic test results, request prescription refills, communicate with clinicians, and access their medical records. In addition, portals typically allow proxy access for caregivers of children or older patients, thereby facilitating family engagement and oversight where appropriate.

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This is a useful resource for guidelines regarding security and privacy. This includes links to HIPAA information and other laws and guidelines.

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Health information technology promises a number of potential benefits for individuals, health care providers, and the nation's health care system.

It has the ability to advance clinical care, improve population health, and reduce costs. At the same time, this environment also poses new challenges and opportunities for protecting individually identifiable health information.

Federal policies and regulations are in place to help protect patient privacy and guide the nation's adoption of health information technology.

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Privacy, Security, and HIPAA Health information technology promises a number of potential benefits for individuals, health care providers, and the nation's health care system. It has the ability to advance clinical care, improve population health, and reduce costs. At the same time, this environment also poses new challenges and opportunities for protecting individually identifiable health information.

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This article reviews the new challenges healthcare faces with security and privacy. The new informatic and electronic records present new problems for privacy and security such as cyber security.

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doi: 10.1109/IEMBS.2006.260060.

Affiliation

¹ Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, CA 94720, USA. marci@eecs.berkeley.edu

Abstract

The face of health care is changing as new technologies are being incorporated into the existing infrastructure. Electronic patient records and sensor networks for in-home patient monitoring are at the current forefront of new technologies. Paper-based patient records are being put in electronic format enabling patients to access their records via the Internet. Remote patient monitoring is becoming more feasible as specialized sensors can be placed inside homes. The combination of these technologies will improve the quality of health care by making it more personalized and reducing costs and medical errors. While there are benefits to technologies, associated privacy and security issues need to be analyzed to make these systems socially acceptable. In this paper we explore the privacy and security implications of these next-generation health care technologies. We describe existing methods for handling issues as well as discussing which issues need further consideration.

Electronic Health Interventions in the Case of Multiple Sclerosis: From Theory to Practice.

Scholz M, Haase R, Schriefer D, Voigt I, Ziemssen T. Scholz M, et al. Brain Sci. 2021 Feb 2;11(2):180. doi: 10.3390/brainsci11020180. Brain Sci. 2021. PMID: 33540640 Free PMC article. Review.

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national center for biotechnology information

# data[].attributes.attachment_link.title
Security and privacy issues with health care information technology - PubMed

# data[].attributes.attachment_link.description
The face of health care is changing as new technologies are being incorporated into the existing infrastructure. Electronic patient records and sensor networks for in-home patient monitoring are at the current forefront of new technologies. Paper-based patient records are being put in electronic for ...

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Video about the beauty of Data. He explains how data can make things easier to visualize.

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The beauty of data visualization - David McCandless

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This video goes over why companies make bad decisions. Why humans are imperative to going through data.

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The human insights missing from big data | Tricia Wang

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This article goes over why visualization of data is important in healthcare data. This also gives examples as to how it can be helpful.

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Healthcare Data Visualization: Examples & Key Benefits

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

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This article reviews how providers view data visualization. It also reviewed the provider's feedback regarding data visualization in practice.

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Monitoring Editor: Gunther Eysenbach

Reviewed by Saif Khairat, Karen Colorafi, and Thiago Cruvinel

Uba Backonja, RN, MS, PhD, 1, ² Sarah C Haynes, MPH, ³ and Katherine K Kim, MBA, MPH, PhD ³

Abstract

Background

There exists a challenge of understanding and integrating various types of data collected to support the health of individuals with multiple chronic conditions engaging in cancer care. Data visualization has the potential to address this challenge and support personalized cancer care.

Objective

The aim of the study was to assess the health care practitioners' perceptions of and feedback regarding visualizations developed to support the care of individuals with multiple chronic conditions engaging in cancer care.

Methods

Medical doctors (n=4) and registered nurses (n=4) providing cancer care at an academic medical center in the western United States provided feedback on visualization mock-ups. Mock-up designs were guided by current health informatics and visualization literature and the Munzner Nested Model for Visualization Design. User-centered design methods, a mock patient persona, and a scenario were used to elicit insights from participants. Directed content analysis was used to identify themes from session transcripts. Means and SDs were calculated for health care practitioners' rankings of overview visualizations.

Results

Themes identified were data elements, supportive elements, confusing elements, interpretation, and use of visualization. Overall, participants found the visualizations useful and with the potential to provide personalized care. Use of color, reference lines, and familiar visual presentations (calendars, line graphs) were noted as helpful in interpreting data.

Conclusions

Visualizations guided by a framework and literature can support health care practitioners' understanding of data for individuals with multiple chronic conditions engaged in cancer care. This understanding has the potential to support the provision of personalized care.

Keywords: cancer care facilities, informatics, patient-centered care, patient-generated health data, precision medicine, visualization

Introduction

Background

About 1 out of every 4 people in the United States lives with multiple chronic conditions (MCC), which include cancer and other conditions such as hypertension, diabetes, and heart disease [ 1]. About 40% of individuals with cancer also live with 1 or more chronic conditions [ 2]. The attendant complexity and potential confounding factors in managing treatment-intensive illnesses such as cancer among individuals with MCC beg for personalized care approaches. Personalized care, as described in precision medicine and personalized medicine, involves the collaboration between health care practitioners (HCPs) and patients and considers the perspectives, experiences, and health-related data of the person receiving care [ 3]. A recent Cochrane review suggests that personalized care can support the physical and psychosocial health of individuals with MCC [ 3], which can include those engaged in cancer care. Person-generated health data (PGHD) such as symptoms, medication use, physical activity, and health goals are important information for personalizing care of MCC. Organizations, including the United States Department of Health and Human Services [ 4, 5] and the Healthcare Information and Management Systems Society [ 6], recommend that PGHD be captured and used in decision making, care planning, and coordination. Furthermore, mobile technologies can support collection and access to PGHD to support the management of chronic conditions and personalized care [ 7- 12].

Although PGHD is quite varied, it may be collected at a different velocity and magnitude and in different and nonstandard formats [ 11]. To be useful to HCPs, individuals with MCC (including those engaged in cancer care), and caregivers, the cognitive burden of understanding and synthesizing this information must be minimized, and the opportunity to make good decisions must be maximized. This is particularly pertinent in the care of individuals with MCC engaging in cancer care, which is the scope of the research described in this manuscript. Coordination of cancer care involves many people-the individual engaging in cancer care, their HCPs, caregivers, family, and health care staff-who need to integrate the large amounts of data; these data are used to support understanding of an individual's health status, completion of health-related tasks, and care-related decision making [ 13].

Data visualization offers an approach to address this challenge of integrating and using large amounts of data collected to support the personalized care of individuals with MCC engaging in cancer care. Data visualizations are representations of data through the application of visual encodings (eg, position and color) [ 14- 17]. Visualization can leverage a user's cognitive strengths such as pattern recognition, and it helps them overcome their cognitive limitations including calculating and remembering strings of numbers. This can ultimately support understanding, task completion, and decision making. Visualizations that are designed with guidance from potential end users can be particularly valuable. User-centered design, within the field of human-centered design, is an approach to systems development that involves potential end users to understand their behaviors, tasks, and needs, among other factors [ 18, 19]. User-centered design can help the designer fully render the users' needs to improve the decisions that the visualizations are meant to support. User-centered design has been used previously in the development of visualizations [ 20, 21] including visualizations of patient-reported outcomes [ 22, 23].

The benefits of data visualizations-supporting understanding, task completion, and decision making-are especially critical in health-related settings such as cancer care facilities. In these settings, data are used to support important, critical, and time-restricted decisions that impact the health of individuals. Data visualizations are increasingly being incorporated into clinical care through integration of dashboards into health record systems. A recent review suggests that dashboards that integrate visualizations have the potential to support the cognitive work and decision making of intensive care unit clinicians [ 24]. However, there have been few examples of the effective use of person-generated data in personalized cancer care, particularly to enable shared decision making or care coordination [ 25]. A recent study found that patients with solid tumors who used a Web-based system to report symptoms experienced longer survival compared with usual care [ 26]. There is also little research on the value of visualizations within systems that integrate patient-generated data in cancer care. A pilot study conducted in Italy suggested that a dashboard that integrated remote monitoring and symptom-tracking data could be useful to HCPs and patients [ 27]. However, this work did not specifically evaluate the visualizations, and rationales for the visualization designs were not described. Therefore, a gap exists in the literature and practice regarding the development of informatics solutions that integrate and visualize person-generated data to support understanding and decision making regarding personalized cancer care among individuals with MCC.

Prior Work

OnPoint is a mobile app developed by the authors to support care coordination for individuals with MCC [ 25, 28, 29]. Previous studies by the authors' research group that were conducted to support heart failure and oncology patients resulted in the development of a mobile app that featured patient health goals, proactive symptom assessment, comprehensive medication list and medication reconciliation, and tracking for patient and caregiver use. On the basis of this prior work, researchers identified the need for visualization of data collected from the app and integrated into the electronic health record for communication to HCPs. This inspired the study described below.

Study Purpose

The purpose of this study was to assess HCPs' perceptions of and feedback regarding visualizations developed to support the personalized care of individuals with MCC engaging in cancer care.

Methods

Design, Time Frame, and Setting

This user-centered study took place from May to June 2017 at a large, urban academic medical center in the western United States.

Recruitment of Participants

We sought 8 medical doctors (MDs) and registered nurses (RNs); a sample size considered adequate for this type of qualitative user-centered design study [ 30- 32]. HCPs were either known to the researchers or identified by referral of HCPs who had participated in previous studies in the development of the OnPoint mobile app [ 25, 28, 29]. HCPs were eligible to participate if they were potential end users of a health information system to support cancer patients with MCC and HCPs currently providing care to cancer patients.

Visualization Development

Paper mock-ups of data visualizations were developed by UB (author) guided by Munzner Nested Model for Visualization Design [ 17]. We applied all model constructs (italicized in the following paragraphs) except for the algorithm design construct, which is suited for software development rather than for our focus on presoftware development.

The domain problem addressed was the need to support the care of individuals with MCC engaging in cancer care-the problem addressed by the OnPoint mobile app [ 25, 28, 29].

For operation and data type abstraction, we identified operations (tasks) and data types from the previous studies [ 25, 28, 29]. These data types were blood pressure (mm Hg), weight (kg), blood glucose levels (mg/dL), medication adherence (medications not taken at the time or frequency as prescribed), and symptoms from the Canadian Oncology Symptom Triage and Remote Support (COSTaRS) practice guides [ 33, 34].

For visual encoding and interaction design, mock-up encodings and designs were guided by literature on (1) visualizing data [ 14- 17, 35- 44]; (2) health data visualization [ 23, 45- 47]; and (3) a mock patient persona and scenario [ 48, 49]. The mock patient was a 56-year-old woman with uterine cancer and type 1 diabetes. She had recurrent work and personal constraints on Thursdays that interfered with taking medications as prescribed and managing her blood glucose levels. This patient also recently experienced weight gain due to fluid retention.

The following visualization mock-ups were created based on the nested model constructs: (1) a 4-week overview of medication adherence, blood pressure, weight, and blood glucose alone ( Figure 1 ) and with pop-ups providing details on demand ( Figure 2 ), (2) a 4-week view of line graphs indicating blood glucose readings alone ( Figure 3 ) and with a pop-up providing details about a specific blood glucose reading ( Figure 4 ), and (3) a 2-week view for blood pressure ( Figure 5 ) and with a pop-up providing details about a specific blood pressure reading ( Figure 6 ). In addition, 3 additional versions of the 4-week overviews of medication adherence, blood pressure, weight, and blood glucose were created ( Figures 7 -). Finally, a visualization of self-reported symptoms generated from the COSTaRS protocols was developed ( Figure 10 ).

Indication of target levels and ranges were shown with lines and colors. For example, in Figures 3 and , the gray bands indicate the target blood glucose range (80-130 mg/dL before a meal and <180 mg/dL 2 hours after the start of the meal [ 50]). In Figures 5 and , lines indicate (1) mock patient average systolic and diastolic blood pressure and target blood pressure (120/80 mm Hg [ 51]). Line graphs were purposefully chosen based on previous research indicating that position and color of dots on a chart (eg, individual glucose readings) can support quantitative interpretation [ 37- 39, 41]. Blue, orange, and yellow colors were used because they can be distinguished by individuals with color blindness [ 52- 54].

Measures and Procedures

This study applied user-centered design methods to engage potential end users early in the design process to understand their needs, priorities, and values. UB and SH conducted one-on-one interviews; participants were provided with pens, colored pencils, and markers and encouraged to draw and take notes on the paper visualizations as they reviewed them. They were also prompted throughout the interview to think aloud about what they saw and thought while reviewing the visualizations [ 55, 56].

Interviews were conducted in 3 steps: (1) going through the scenario during which they thought aloud while viewing paper mock-ups and responding to prompts (see Textbox 1 for the scenario), (2) ranking of alternative visualizations of overview mock-ups, and (3) providing overall impressions and usefulness for care coordination. This step sequence was used to guide the participants in using the visualization as they might in their clinical practice, which can help elicit reflection from participants [ 48].

1. You have arrived at the clinic before you start seeing patients. You want to see how your first patient of the day is doing. Her name is Deb Lee (age 56 years). Three weeks ago she completed chemotherapy for uterine cancer. She also has type 1 diabetes that was diagnosed in childhood.

2. You have done your typical chart review of Deb's clinical data using the clinic's electronic health record. After that chart review of electronic health record data, you want to see how Deb is doing at home. Recently the clinic started supporting patients in collecting data at home. Data include:

if the patient took her medications as prescribed

blood pressure (measured twice a day)

weight (taken once a day)

blood glucose (taken periodically throughout the day using a traditional finger-prick monitor)

3. You'd like to see some specific information about a specific data point. You hover over this orange dot. [Researcher points to dot on "This week," Thursday at 12:00 pm]

4. You'd like to see some specific information about a specific data point. You hover over this orange dot. [Researcher points to dot for weight on "This week," Thursday]

In step 1, participants first viewed an overview of visualization ( Figures 1 and ), then specific measures ( Figures 3 and ), then sought details on demand for those measures ( Figures 5 and ). This approach aligns with Shneiderman Visual Information Seeking Mantra of "Overview first, zoom and filter, then details-on-demand" [ 42]. While viewing the visualizations, participants explained what they saw, the impression of the patient, what additional information they would want included, what aspects supported their understanding of the patient, and what aspects were confusing.

In step 2, the researcher presented alternate versions of the 4-week overview ( Figures 7 -). Ordering of the versions was varied from participant to participant so that the order in which the versions were presented did not influence responses. While viewing the alternate versions, participants described their overall impressions, aspects they perceived as helpful, and aspects they perceived as confusing. Then, the then researcher gave the clinicians the original 4-week overview ( Figure 1 ) and asked participants to order this overview and the alternate versions from most helpful (ranked first) to least helpful (ranked fourth). Participants explained aloud their rationale for the ordering while sorting the versions.

In step 3, participants viewed the visualization that provided summaries of longitudinal patient-reported symptoms ( Figure 10 ). Again, the participants were asked to describe their overall impression of the visualization, aspects that they perceived as helpful in understanding the patient, and aspects they perceived as confusing in understanding the patient. At the end of the interview, the researchers asked whether and how the visualizations could help personalize the care and asked for suggestions.

Interviews were recorded, transcribed, and supplemented by field notes taken by researchers during the interviews. This study was approved by the affiliated institutional review board. All participants provided verbal consent after receiving and reading the study consent form. Participants were provided with a US $50 Amazon gift card for engaging in the interview.

Analyses

Transcripts of participant interviews were analyzed independently by 2 researchers using directed content analysis [ 57]. Categories used to guide the development of codes and the content analysis were developed from the data and refined as described below. At first, 2 researchers independently coded 3 randomly selected sections of transcripts from different participants to identify themes. The unit of analysis was a distinct idea within a participant's statement. After each of 3 rounds of independent coding, the researchers discussed how content was coded and any new themes that emerged for which codes needed to be added. After the third round, the researchers concurred that the codes adequately covered all themes, thus yielding the final codebook used for the remainder of transcript coding. Inter-rater reliability was calculated using an estimate of inter-rater reliability as described by Topf [ 58]. The agreement was 84.5%. The researchers discussed the discrepancies in coding and ultimately came to a consensus for a final inter-rater agreement of 100%. Researchers then independently coded the transcripts using NVivo (v11.4.1, QSR International, Melbourne, Australia). Rankings for preferences of the 4 versions of the overview visualization ( Figure 2 ; Figures 7 -) were tabulated, and mean ranks and SD were calculated for each overview version.

Results

Participants

A total of 8 HCPs participated in the interviews. Out of these, 4 were MDs; 1 was a pain management specialist (participant MD1), and 3 were oncologists (participants MD2, MD3, and MD4), and 4 were RNs (participants RN1, RN2, RN3, and RN4). Each participant provided care to cancer patients in the cancer center. Interviews lasted for approximately 25 to 42 min.

Themes

We identified 7 themes. Of these, 2 themes were not directly relevant to the visualizations; therefore, for the purpose of this paper, we report the following 5 themes: data elements, supportive elements, confusing elements, interpretation, and use of visualization. See Table 1 for descriptions and specific content regarding the themes.

Table 1

Theme

Description

Specific content regarding the theme

Data elements

Existing or future or potential data elements (eg, weight, blood pressure, medication adherence, and symptoms)

data useful for specific role in cancer care included weight and medications ( Figures 1 ,, and ) and the list of symptoms ( Figure 10 ) [MD a1 and RN b 2]

suggestions for additional data elements or information included additional measures such as heart rate [MD1 and RN4], temperature [RN1 and RN2], body mass index [MD3], lab values [RN1], meal times or what eaten [RN1, RN2, MD3, and MD4], physical activity engagement [RN1], sleep [MD1], swelling [MD2], symptoms that may be particular or specific to certain cancer therapies [MD2, RN2, and MD3]

patient-identified symptoms [RN2 and MD3]

a legend defining the symptoms [MD1]

meaning of the symptom scale ratings [MD1]

reasons for missed medications [MD2, MD3, and RN1]

values and description of the normal values and ranges for blood glucose and blood pressure [MD2]

goals of care [RN1]

treatments [RN1]

patient-reported reasons for abnormal values [RN2]

Supportive elements

Aspects of the visualization that supported the participant's understanding of the patient or that they thought were helpful

the color orange drew attention and helped participants find data points or patterns in the data that might require attention or indicate something abnormal more easily [MD1, MD3, MD4, RN1, RN3, and RN4]

calendar format and line graphs were helpful because clinicians are accustomed to them [MD1, RN1, RN2, and RN4], are used in practice [MD1 and RN1], and help see trends [MD1-MD4 and RN1-RN4]

having details on demand was helpful [MD1-MD4 and RN1-RN4] and does not to lead to overpopulation of data within the visualization [MD3]

suggestions for additional supportive elements included a pop-up with a numeric scale for normal ranges [MD4] or an indication of how the normal range was derived [MD3]

Confusing elements

Aspects of the visualization that the participant does not understand or finds confusing or unhelpful

Figure 1 : Unsure if blank spaces indicated that measurements were normal or not taken [MD2, MD3, and MD4]

Figure 7 : Circles confusing or overwhelming [MD2, MD4, RN2, RN3, and RN4]

Figure 8 : Unsure if the empty rectangles indicated normal readings or no measures taken that day [RN2, RN3, RN4, and MD4]

Figure 10 : Lack of clarity about the meaning of the numeric scale [RN3 and RN4]; unsure about threshold values for the numeric scores that led to values being highlighted in orange or whether the thresholds were the same across all symptoms [RN1]; miniature line graphs hard to interpret [MD1, MD2, MD4, and RN3]; and hard to understand, compare, and interpret the 2 different ways of quantifying symptoms [RN1, RN2, and RN4]

Interpretation

Information obtained or conclusions drawn about the patient from the visualization

Use of visualization

Ways the participant would or could use the visualization

visualizations could help clinicians gain an understanding of patient outside the clinic [MD1 and MD2], help them prepare specific questions regarding Thursdays [MD1-MD4, and RN4] and the cause of rapid weight gain [MD2], discuss the patient's symptom experiences or management [RN4 and MD4], make clinic time more efficient [MD1 and MD2]

visualizations could help patients remember health experiences [MD2] and empower patients to engage in health management [MD4]. Together clinicians and patients could use visualizations to support personalized cancer care [MD1-MD4, and RN1-RN4], facilitate interactions that focused more on the patient and their specific needs [MD1, MD2, RN2], and better guide conversations between clinicians and patients [RN3, RN4, MD3, and MD4]

Data Elements

There were several data elements within the visualizations that participants indicated as useful and supportive for their collaborations with patients. Participants noted the usefulness of measures and behaviors portrayed in Figures 1 - (eg, weight) and symptoms portrayed in Figure 10 (eg, pain levels). Certain data were noted as being less relevant given their roles (see Table 1 ). Participants indicated several additional data elements that could be helpful (see Table 1 ). This included symptoms identified by patients as relevant or important (n=2). For example, MD3 suggested patient-driven modifications of the symptoms list:

Can we plan another category that I want the patient to monitor? For example, if they're having bleeding-vaginal bleeding-can they use the category of vaginal bleeding to show me...?

Supportive Elements

Participants described several visual elements that supported their understanding of the patient. The elements included color, the calendar format, use of line graphs, and the ability to get "details on demand." All participants (n=8) stated that color supported their understanding of the data. Several indicated that icons helped differentiate graphs (n=4) and that the gray bands indicating normal ranges helped identify abnormal data points (n=4). Participants stated that the calendar format and line graphs were helpful because they are accustomed to them (n=4), are used in practice (n=2), and help them see trends (n=8):

[The line graph] gels with what practitioners could be used to...You don't want to have something too novel where you have some bizarre bar graph or some kind of odd, interesting pattern that's in 3D...that people haven't seen.

Three participants stated that the calendar format allowed them to see trends such as missed medications on Thursdays (RN1, RN2, and RN4):

...you see a pattern...that helps you identify that there is a regimen and that there's a schedule...it enables you to see something missed in the pattern by seeing the...[entire] month.

All participants (n=8) reacted positively to "details on demand" features such as hovering over a data point to get a pop-up with detailed information:

...it's good that it [the visualization] doesn't overpopulate the numbers right there and then because I mean I would just be overwhelmed with actual numbers, so this hovering thing is really good.

Confusing Elements

There were several visual elements in the 4-week overviews that participants found confusing. These included not understanding the meaning of blank spaces in Figure 1 (n=3) or Figure 8 (n=4), being confused or overwhelmed by the circles in Figure 7 (n=5), and lacking clarity about how the weight increase was calculated for Figures 1 and (n=3).

Several participants noted issues with interpreting visualizations for patient-reported symptoms ( Figure 10 ). This included lack of clarity about the meaning of the numeric scale, threshold values for the numeric scores that led to values being highlighted in orange, and whether the thresholds were the same across all symptoms (n=3). Participants also found it difficult to interpret the miniature line graphs (n=4) and to understand, compare, and interpret the 2 different ways of quantifying symptoms (n=3).

Interpretation

All participants (n=8) used visual elements to interpret data-finding patterns and viewing trends-to support understanding and decision making. They identified missed medications including the pattern of missed medications on Thursday using the calendar views (n=6). When seeing the pattern on Thursdays, participants were prompted to think about what could cause the patterns:

I wonder what's going on Thursdays that she always forgets the medications.

Identifying this pattern supported RN4's decision making to investigate the cause of the pattern:

I'm not sure why [she is missing her medications consecutively on Thursdays] so you would have to find out why is she missing her drugs on Thursday.

MD4 similarly described how using the visualizations supported understanding of the patient, reasoning about what might be causing abnormal readings, and ruling out potential causes:

...she just is not taking her medications for some reason...I can use the visualization and the colors to figure out some of her difficulties...[about] why she may not be adherent with her health [behaviors]and medications.

RN4 echoed how the visualizations could facilitate clinician reasoning, stating that a clinician could postulate about what might be causing the issues on Thursdays by bringing in symptoms and other data:

...you could really get I think a good picture.

Trends in line graphs helped participants interpret temporal glucose, blood pressure, and weight data (n=5). Participants reflected on several weeks' worth of data, comparing normal and abnormal points over time as well as visual elements indicating missed medication, to formulate whether they believe the patient had well-controlled blood glucose (n=2). For example, MD4 stated:

[if the medication being missed is] related to her insulin...and her blood sugars aren't controlled, [then] the general impression probably is that her blood sugars aren't controlled and her diabetes isn't controlled.

Participants viewed trends across different measures to infer relationships between measures. When viewing Figures 5 and , MD1, MD2, RN1, and RN4 viewed the line graph trend for blood pressure, guided by the gray bands indicating normal readings and color coding of the data points, to inform their reasoning about what might have caused blood pressure to stay within normal range but weight to increase (as indicated by the line graph trend and color-coded data points). Participants used the calendar structure to see if weekly patterns were consistent:

I also see that the same kind of pattern I've seen on this very day and the week before.

Use of Visualizations

Participants stated that visualizations could help gain an understanding of the patient outside the clinic and prepare specific questions to facilitate discussions with the patient about their self-management outside the clinic. This included discussing circumstances on Thursdays that made health management challenging (n=5), asking questions to help investigate the cause of rapid weight gain (n=1); and understanding symptom experiences or management (n=2). MD2 stated that the visualizations provide insights that "might open up a door to other questions that you normally wouldn't ask if you didn't [see trends]."

In addition, participants noted that visualizations could be helpful for patients. Having the visualizations during clinic visits could help patients remember symptoms; MD2 stated that the visualizations provide "another way to understand if the patient had any symptoms but forget to mention [them] to us or we forgot to ask [about them] during the clinic visit." Visualizations could also support patients feeling empowered; MD4 stated that a visualization tool could empower patients to engage in health behaviors such as taking medications and "be more aware of their symptoms" regularly.

All participants (n=8) mentioned the use of visualization to personalize visits with patients. Visualizations helped them identify issues specific to the patient that needed to be addressed, making interactions more focused on the patient and their specific needs (n=3). Visualizations could also give a clearer and focused picture of the patient, their health status, and needs that can better guide conversations and interactions with patients (n=4):

I think it would cause us to get a good picture, get a fast picture, evaluate that with the patient so we don't walk into an assumption, but dive a little bit quicker if we needed to.

MD3 stated that the visualization could personalize visits by bringing "attention to the important things" and focus "conversations with the patient directly to what's the issues or the problems that now I see [using the visualizations]..." RN4 stated that a benefit of the visualizations through personalizing visits with patients could be earlier identification of issues "rather than waiting until things [snowball]."

Ranking of Overview Visualizations

Participants varied in their preferences for the 4-week overviews. On average, participants ranked Figure 2 as most helpful (mean 1.8 [SD 1.2]) and Figure 7 least helpful (mean 3.9 [SD 0.4]; see Table 2 ).

Table 2

Figure version

MD a 1

MD2

MD3

MD4

RN b 1

RN2

RN3

RN4

Ranking c, mean (SD)

Discussion

Principal Findings and Comparison With Prior Work

In our user-centered design study, we found that MD and RN participants' understanding of physiological and symptom data for individuals with MCC engaged in cancer care was supported by visualizations we developed by applying a visualization framework and relevant literature. Both MD and RN participants found that various visual encodings such as color, and familiar presentation such as calendar formats and line graphs supported their interpretation of the presented data. This research used foundational knowledge in data visualization in a novel way to develop visualizations that both MD and RN participants found helpful and effective in integrating various health-related data. Our MD and RN participants also noted the potential usefulness of the visualization in supporting personalized care. This user-centered design study offers findings from potential clinician users of the output of patient-generated health data from the OnPoint app. These results will be used specifically to inform the integration of visualizations into OnPoint system in the next phase of the project.

We demonstrated that using paper prototypes early in the design process allowed us to engage potential end users, gather useful insights, and explore suggested changes efficiently before investing in technical resources to build the system. We found that there were similar reactions by MDs and RNs to the visualizations. For example, all MD and RN participants found bands representing normal ranges and details on demand to be helpful, and they perceived the visualizations as helpful in providing personalized care. Both MDs and RNs reported that color helped them pick out important data points and that the blood glucose graph dips and peaks helped them think about what might have caused abnormal readings. This suggests that careful design of visualizations that incorporate fundamental guidance of data visualization can support a wide range of users. Although personalization and customization of a visualization interface based on different users' needs could increase usefulness and usability [ 59- 61], it is possible to minimize the extent to which visualization versions differ when they are designed thoughtfully and purposefully.

Although we cannot assume that an interactive tool incorporating visualizations for use by individuals with MCC engaged in cancer care would necessitate the same design as a tool for clinicians, we do believe that this study offers a starting point for features to consider for users who are patients.

This study has the potential to inform the growing domain of research in integrating visualizations into informatics solutions that support personalized patient care [ 45, 62- 71]. This includes work on integrating home monitoring data for individuals engaging in cancer care [ 27] as well as health-related quality of life data for prostate cancer care [ 72].

Our study findings are congruent with guidance and best practices described in the visualization literature. Both MD and RN participants noted that color helped them see patterns in the data or pick out data that require attention, congruent with work described by Ware [ 44]. They also were able to use the line graphs to identify meaningful patterns in the data; this aligns with recommendations based on work by Cleveland and McGill [ 37- 39] and Mackinlay [ 41]. In particular, position rather than other data encodings (eg, area) supports more accurate interpretation of the data being represented by the encoding. Although we did not compare our line graphs with other graph types in this study, participants responded positively to our design choice that was guided by the data visualization literature.

Implications for Developing Health-Related Visualizations

On the basis of the findings from our study and the current literature of integrating visualizations into clinical care, we propose the following design recommendations: (1) applying knowledge from both health informatics and visualization domains to guide the creation of visualizations and (2) applying previous research can facilitate the development and testing of systems that integrate health data visualization. First, using Munzner Nested Model for Visualization Design [ 17] supported the design process by making it efficient, and it can facilitate integration of our findings with other research using the same model [ 73].

Second, providing users with options on how to visualize the same data may support use of the visualization. In our study, we found that among the 4-week calendar view options, there was not 1 that was consistently favored. Following 1 of Nielsen usability heuristics-flexibility and efficiency of use-visualization tool developers could allow users to customize how data and information are displayed [ 59- 61].

Finally, engaging potential end users early in the design ideation process was feasible and insightful. To minimize time and burden on HCP participants, researchers can carefully develop study protocols so they can maximize opportunities for participants to provide insights such as using mock patient personas and scenarios to guide eliciting feedback about mock-ups. Using personas and scenarios is advocated within the human-computer interaction domain [ 48, 49], and it has been used to support the development of health informatics tools [ 74, 75].

Limitations

There were limitations to our study. Our sample was limited to MDs and RNs; these visualizations could be useful to other HCPs supporting individuals with MCC engaging in cancer care, such as care coordinators, dieticians, pharmacists, and social workers. In addition, inputs from patients themselves and their family members must be collected to understand their informational needs. This study was conducted at a single cancer center; therefore, it has limited generalizability to other cancer centers. Mock-ups were on paper rather than on a device that a clinician would use to view visualizations in practice (eg, computer tablet and desktop computer). The data visualization literature used to guide the development of our mock-ups has not been tested extensively and empirically within health-related apps for cancer care; our work can support the building of evidence regarding the application of the visualization literature within this health domain. Although the visualizations are intended to be delivered via the electronic health record, we did not explicitly address how this might be accomplished. This work will be pursued in a future phase.

Conclusions

This study suggests that visualizations guided by a framework and literature can support HCPs' understanding of data to support personalized cancer care for individuals with MCC. By integrating health informatics and visualization literature and applying user-centered design methods, we were able to develop and elicit feedback on visualizations for health-related data including person-reported data. Future research could apply these methods toward the development of visualizations to support the care of other populations and the development of functional systems integrated into clinical and personal health care.

Acknowledgments

Funding for this study was provided in part by the Boston University-Center for the Future of Technology in Cancer Care (National Institute of Biomedical Imaging and Bioengineering award U54 CFTCC U54-EB015403-05) for authors UB, SCH, and KKK; and Gordon and Betty Moore Foundation grant to the Betty Irene Moore School of Nursing for SCH. The authors would like to thank Petra Backonja for her editorial support.

Abbreviations

COSTaRS

Canadian Oncology Symptom Triage and Remote Support

HCPs

health care practitioners

MCC

multiple chronic conditions

MD

medical doctor

PGHD

person-generated health data

RN

registered nurse

Footnotes

Conflicts of Interest: None declared.

References

2. Edwards BK, Noone AM, Mariotto AB, Simard EP, Boscoe FP, Henley SJ, Jemal A, Cho H, Anderson RN, Kohler BA, Eheman CR, Ward EM. Annual report to the nation on the status of cancer, 1975-2010, featuring prevalence of comorbidity and impact on survival among persons with lung, colorectal, breast, or prostate cancer. Cancer. 2014 May 1;120(9):1290-314. doi: 10.1002/cncr.28509. doi: 10.1002/cncr.28509. [[ PMC free article] [ PubMed] [ CrossRef] [ CrossRef] Google Scholar]

3. Coulter A, Entwistle VA, Eccles A, Ryan S, Shepperd S, Perera R. Personalised care planning for adults with chronic or long-term health conditions. Cochrane Database Syst Rev. 2015 Mar 3;(3):CD010523. doi: 10.1002/14651858.CD010523.pub2. doi: 10.1002/14651858.CD010523.pub2. [[ PMC free article] [ PubMed] [ CrossRef] [ CrossRef] Google Scholar]

11. West P, Van Kleek M, Giordano R, Weal M, Shadbolt N. Information quality challenges of patient-generated data in clinical practice. Front Public Health. 2017;5:284. doi: 10.3389/fpubh.2017.00284. doi: 10.3389/fpubh.2017.00284. [[ PMC free article] [ PubMed] [ CrossRef] [ CrossRef] Google Scholar]

32. Macefield R. How to specify the participant group size for usability studies: a practitioner's guide. J Usability Stud. 2009 Nov;5(1):34-5. [ Google Scholar]

34. Stacey D, Macartney G, Carley M, Harrison MB, The Pan-Canadian Oncology Symptom TriageRemote Support Group (COSTaRS) Development and evaluation of evidence-informed clinical nursing protocols for remote assessment, triage and support of cancer treatment-induced symptoms. Nurs Res Pract. 2013;2013:171872. doi: 10.1155/2013/171872. doi: 10.1155/2013/171872. [[ PMC free article] [ PubMed] [ CrossRef] [ CrossRef] Google Scholar]

Articles from JMIR Human Factors are provided here courtesy of JMIR Publications Inc.

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Data Visualizations to Support Health Practitioners' Provision of Personalized Care for Patients With Cancer and Multiple Chronic Conditions: User-Centered Design Study

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Monitoring Editor: Gunther Eysenbach Reviewed by Saif Khairat, Karen Colorafi, and Thiago Cruvinel Uba Backonja, RN, MS, PhD, 1, 2 Sarah C Haynes, MPH, 3 and Katherine K Kim, MBA, MPH, PhD 3 There exists a challenge of understanding and integrating various types of data collected to support the health of individuals with multiple chronic conditions engaging in cancer care.

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This article goes over how important legal and ethical considerations are in regard to healthcare informatics. It also reviews challenges to meet these ethical standards.

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Health informatics studies health care information and communication, both of which have been greatly affected in recent years by the use of computers and the web by both clinicians and patients. Many practices now use electronic medical records (EMRs) and computerized provider order entry (CPOE). Patients often surf the web seeking health information. But some uses of computers in health care are controversial and raise ethical concerns, especially when patient safety and outcomes are at stake.

For guidance about ethics, health care often relies on the principles of respect for autonomy, nonmaleficence, beneficence, and justice.1 Respecting autonomy means allowing individuals control over their lives. Nonmaleficence requires refraining from harming patients, while beneficence entails providing beneficial care. (Beneficence also includes preventing harm - as opposed to refraining from harm, which is nonmaleficence.) Justice seeks a fair distribution of resources and responsibilities.

These principles have relevance to health informatics practices. In the discussion over the ethics of such computer use in health care, we see a recurring theme of tensions among the principles of respect for autonomy, beneficence, and nonmaleficence.

For example, EMR systems can benefit patients by improving care access, quality, coordination, and cost.2 If the ideal use of EMR and CPOE systems provides significant patient benefit, there would seem to be a moral obligation for clinicians to use them. But some clinicians find the inconvenience of such systems to be a burden, and patients can experience clinician use of such systems during medical encounters as intrusive. Furthermore, what if clinicians using such systems in actual practice engage in documentation techniques that may be dangerous to patients? Would that call into question that use of such systems is a moral obligation? Or only that if used, such systems must be used in a morally responsible fashion? Tension among the ethical principles also occurs in the area of patient use of the web and social media. How can we respect the autonomy of patients to use these technologies while at the same time preventing possible patient harm from inaccurate information and illicit prescription medication purchases?

Confidentiality and Privacy in a Wired World

Clinicians and institutions using EMR systems must ensure patient health information confidentiality and protect patient privacy. With computer systems this seems a difficult task because records are easily shared with more people: physicians, nurses, other clinicians, clerks, coders, managers, information technology staff, insurance personnel, the government, patients, vendor staff, and even patients themselves. Health information exists not only in an institution's electronic medical records but is also made available through health information exchanges, personal health records, and patient portals.

Those working in health care have a moral (and legal) obligation to protect information confidentiality and patient privacy. Electronic records and associated systems are thus monitored and secured to prevent privacy breaches. The moral basis of such an obligation is in respecting patient autonomy (keeping patients in control of their health information), and refraining from harming and preventing harm to patients (nonmaleficence and beneficence), as might occur, for example, through disclosure of embarrassing information. Patients who feel their health information will be kept confidential will more likely share it willingly with providers, which is important for optimal care.

Yet despite the existence of the HIPAA (Health Insurance Portability and Accountability Act) regulations and institutional monitoring of records, snooping, hacking, public breaches, and health care identify thefts occur.3, 4 Perhaps even greater security technologies and policies are warranted. But strict security measures are often in tension with clinician needs for convenient and timely access to such information. Clinicians already chafe at what they see as overly strict and burdensome security requirements such as having to constantly log in after a short auto-log out, change passwords at regular time intervals (and meet technical requirements for password acceptability, making them difficult to recall, and be unable to reuse previous passwords), remember multiple user names and passwords for different systems, be unable to store chart notes on computers that leave the facility, establish VPN (Virtual Private Network) connections to access hospital records from home, and authenticate/justify one's role before accessing particular patient records. Information technology staff need to work with clinicians and seek their input in security techniques, otherwise they risk clinicians trying to bypass security measures out of frustration.

Respecting patient autonomy may even entail allowing patients to weigh in on some policy decisions. Hospitals debate whether patient consent to Health Information Exchange (HIE) participation should be in the form of an opt-out or opt-in policy. Opt-out policies may increase patient participation. But allowing patients to opt-out of participation in a Health Information Exchange may not safeguard patient autonomy as much as would requiring patients to opt-in. How much information needs to be provided to patients about the HIE for an opt-out policy to constitute consent that is truly informed? From reading a technical paragraph in a consent form presented by a receptionist, do patients fully understand what information is being made available where? These questions will be even more relevant if a nationwide HIE (a National Health Information Network (NHIN) or Nationwide Health Information Network (NwHIN)) is realized because patients may object to having to opt-out to prevent their personal health information being made available practically anywhere to any provider at any institution. Some patients are so apprehensive about loss of control they worry about health information stored in an EMR quite apart from an HIE - some desire an opt-out policy for certain information in the EMR.5

Clinical Documentation Practices

Not only are confidentiality, privacy, and security requirements sometimes controversial, so too is the trade-off between documentation accuracy and convenience. Clinicians have a moral responsibility to make sure medical records are accurate, but accuracy is sometimes sacrificed to convenience through questionable EMR documentation practices.6

Copy and Paste

One area of concern is the use of what has been termed "content importing technology" (CIT).7 EMR systems commonly allow copy and paste techniques from one note to another, even between different patients, and they allow copying from the notes of other providers. Many EMR systems allow the clinician to import almost all of the content of a previous note (a note documenting a past encounter) into a new note (a note for a different encounter). This particular functionality may be called "pull previous" or "copy forward." Identical sections appearing in completely different notes created using such techniques may be considered instances of "cloned documentation." EMR systems also often feature templates, or preconfigured note structures that may already be prepopulated with section contents for a specific type of procedure or even for a specific disease. Other common shortcuts are capabilities to automatically include a complete and normal review of systems and to import lab results directly into a chart note. Providers insist on such techniques because they can be tremendous documentation time-savers.

But used carelessly such shortcuts can result in inaccurate notes and may create legal liability problems.8 When copying, importing, or using templates, the provider is supposed to carefully review the note for faithfulness to what actually happened in the current encounter. Medical decisions made on the basis of inaccurate notes may compromise patient safety or outcomes. Copying and pasting another clinician's content without proper attribution might be considered plagiarism or fraud.

Such documentation problems may be the result of not just carelessness but unfamiliarity with the system. To be used optimally, EMR systems require user training.9 It is often difficult for providers to fit training into a busy patient schedule, but institutions have a moral responsibility to provide clinicians with adequate training and providers have the responsibility to avail themselves of it.

Computer in the Exam Room

Another controversy is over the presence of the computer in the exam room, and this too can become an ethics matter if it affects what occurs during the clinician-patient encounter. Some providers interact with EMR and CPOE systems while at the same time trying to engage the patient. A computer being used for documentation in the exam room can function as a physical or psychological barrier between provider and patient. Patients who feel physicians interact more with the computer than with them may feel alienated and consequently decline to share important concerns.10

Structured Data

A final documentation issue concerns structured data. Increasingly, the federal government (meaningful use requirements) is requiring at least some of the information in a note be structured data. Structured data exists when such information is in a discrete form that can be optimally stored in the fields of a database. In such a form, and aggregated, patient information can be used for secondary data analysis. Data querying and data mining could yield important previously undetected patterns about patient symptoms, diseases, treatments, and complications. Such information could also be used for public health surveillance.11 Structured data is also often needed to trigger decision-support features in the system.

Because computers cannot yet fully "understand" natural language sentences, the traditional narrative text of a chart note does not result in structured data in the database. Current natural language processing programs may be able to convert some of narrative text to structured data, but by and large to get structured data into a database the clinician must enter it that way. Hence the existence of drop-down menus, data entry boxes, and the like for entering vitals and creating problem lists and medication lists, for example.

Some providers find entering data by such techniques saves time, while others find it tedious. Unfortunately, though, it is the provider who bears the burden of having to enter data in a way the computer requires, not in a way that they might find more convenient (narrative dictation). Completeness may also suffer. Chart notes created by structured data entry techniques may resemble an outline more than a detailed story, which may make it difficult for other clinicians reading the note to get an adequate sense of the "patient's story." Can one tell the unique story of a particular patient by using drop down menus without something being left out? Will the provider then resort to using both structured data entry and accompanying narrative text, which would seem to take longer than narrative text alone?

Secondary data analysis made possible by structured data raises questions about privacy and consent and thus ultimately patient autonomy.12 Some seek to justify use of patient information in secondary data analysis on the basic of serving the public good.13 But a patient may object that their consent to use of personal health information for treatment, payment, or operations (TPO) does not automatically include consent to use for scientific research, no matter how noble; they never agreed to details of their clinical encounters being used for that, even in aggregated, de-identified form.

Decision Support

EMR and CPOE systems often include decision-support features, such as diagnosis aids, medication recommendations, customized information displays (such as graphs showing trends), clinical calculators (for example, for dosing), available guidelines and protocols, and warning messages and alerts (for example, about possible drug-drug interactions). As mentioned, these functions sometimes key off of structured data. For example, dosing recommendation depend on the patient's weight being entered in a special data-entry box rather than being buried in a narrative paragraph. Drug-drug interaction problem detection depends on a medication list completed by drop-down menu choices rather than a narrative paragraph listing medications.

Ethical

Several ethical issues arise about the development and use of decision-support technologies. If such a technology is available, and might help prevent patient harm or improve patient outcomes, is the provider morally obligated to use it? Since the provider is the ultimate decision-maker, what if the provider relies on decision-support recommendations that prove incorrect? Is the provider morally (or legally) at fault? Does the vendor bear any moral responsibility for incorrect recommendations or faulty software? What if the problem is not faulty information in the system, but rather a poorly designed display that misleads or is easily misinterpreted by the provider?

Legal

On the other hand, there may be legal liability issues if the provider decides not to make use of available decision-support recommendations.14 Government regulations and the courts would decide whether particular software programs could be held legally at fault under standards of ordinary negligence or strict product liability. Vendors commonly write "indemnify and hold harmless" clauses into contracts with purchasers to avoid such responsibility.16 But one could argue that vendors nevertheless bear moral responsibility for using best practices to develop and test their software systems.

Functionality

In some cases, decision support functionality can become intrusive and counterproductive. For example, too many warnings can cause "alert fatigue."16 Furthermore, if the provider cannot override an alert when legitimately needed, the system may hinder, rather than enhance, proper patient care.17

Social Media

Information and Advice

The explosion of social media during the last decade has created many opportunities in health care for using the web and mobile devices; some of these are characterized as "consumer informatics." Associated informatics-related fields or subfields are mHealth (mobile devices) and eHealth (electronic delivery, such as online eVisits). On the web patients can obtain health-related information, interact in blogs and support groups, purchase health-related products (such as exercise equipment, monitoring devices, and nutritional supplements), engage in eVisits, access medical records and pay hospital bills through patient portals, maintain personal health records, and even buy prescription medications.

Web sources have a moral obligation to ensure health information provided is accurate, since to do otherwise may harm patients. It is not easy for the patient to discern what is accurate, and much health information on the web is inaccurate.18 Online blogs, forums, and support groups may spread incorrect information or imprudent health advice that may be physically or psychologically harmful.

Prescription Medication

Of questionable morality is the alleged practice of some pharmaceutical companies of operating support group sites whose real purpose is clandestine market research. Also controversial is pharmaceutical companies engaging in online direct-to-consumer (DTC) advertisement of drugs in an attempt to have patients "pressure" physicians to prescribe them and perhaps also to subtly advertise to physicians directly. Such advertisements may appear to patients searching for information about associated diseases or participating in related online forums. Patients may be unaware they are being targeted.

Patients use the web for more than information - they can buy prescription medications from online pharmacies. While some legitimate pharmacies have an online sales presence and require prescriptions for purchases, others are from unknown businesses and may provide prescription medications to patients without prescriptions. In such cases the patient has no way of knowing what he or she is really buying. It may be illegal for those in the United States to buy from online pharmacies located outside the country, but the fact that some consumers buy medications from such sources raises possible ethical concerns in itself. Patients may think they are getting a better price. Are American drug prices so high some patients cannot afford them? Or they may think their provider will not meet their medication needs. Do patients have trouble obtaining needed medications from their local physician and pharmacy? In cases where cost or access is an issue the principle of justice, interpreted as fairness to all patients in provision of health care resources, may be relevant.

The ethical issue for health care professionals is about what guidance to give patients about use of sites dispensing information, advice, or medications. Steering patients away from all such sites seems unrealistic. Recommending specific reputable sites may be more helpful, but obviously self-diagnosis from web information is no substitute for seeing a provider. Some hospitals and practices themselves make health information available on the web in an attempt to provide a reliable source of information for patients. Cautioning patients about online prescription purchases from questionable sites would also seem important. Again, though, sometimes here we see a tension between following the obligation to respect patient autonomy and preventing harm to patients (beneficence).

Patient Portals

To further engage patients in responsibility for their health, patient portals are now available for viewing and paying on financial accounts, as well as accessing limited clinical data. Some facilities allow patients to access their own medical records or lab test results via a patient portal. Critics contend lab results could be easily misinterpreted by a patient. Some hospitals offer personal health records that download information from the hospital records. Obviously if data must be entered into PHRs by patients a concern is possible inaccurate or missing information. Other patient delivery modalities are eVisits, telehealth, and email communication. eVisits have found limited use to help with monitoring and management of chronic conditions for existing patients but it would be arguably irresponsible to attempt to provide extensive management of complex conditions or seeing new patient via eVisits. Telehealth, similar to eVisits but often for monitoring purposes only or making more extensive use of video interaction, also is better suited to some usages (emergency trauma transport) than others (more complicated initial diagnoses). Some physicians are willing to engage in email exchanges with patients but for only limited purposes. Confidentiality concerns, unrealistic patient expectations of service or for immediate replies, time constraints, and reimbursement issues make email of limited usefulness.

Clinician Use

Clinicians themselves need to exercise caution on the web. They should never post patient pictures or protected health information (PHI) on social media sites, such as Facebook, even if the patient is not easily identifiable. Griping about patients, other clinicians, or health care management should also be avoided. Some physicians feel the same should hold for patient behavior and so refuse to take patients who will not sign a pledge to refrain from negative reviews of the physician on websites. While the desire to avoid unfair damage to one's reputation is understandable, a coercive stipulation like this seems difficult to square with the moral obligation to always place the patient's interests above one's own.

The use of EMRs and CPOEs is seemingly becoming a standard of care.19 The challenge will be to use this new standard of care in a morally responsible manner.

Biography

Win Phillips, PhD, is Associate Teaching Professor in the Department of Health Management and Informatics at the University of Missouri School of Medicine.

Contact: ude.iruossim@niwspillihp

References

1. Beauchamp TL, Childress JF. Principles of biomedical ethics. 6th edition. Oxford University Press; 2009. [ Google Scholar]

4. Sittig DF, Singh H. Legal, ethical, and financial dilemmas in electronic health record adoption and use. Pediatrics. 2011;127(4):e1042-e1047. [[ PMC free article] [ PubMed] Google Scholar]

5. Sittig and Singh. 2011

8. Sittig and Singh. 2011

9. Sittig DF, Singh H. Rights and responsibilities of user of electronic health records. Canadian Medical Association Journal. 2012;184(13):1479-1483. [[ PMC free article] [ PubMed] Google Scholar]

14. Sittig and Singh. 2011

15. Sittig and Singh. 2011

16. Sittig and Singh. 2011

17. Sittig and Singh. 2012

Articles from Missouri Medicine are provided here courtesy of Missouri State Medical Association

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Ethical Controversies About Proper Health Informatics Practices

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Health informatics studies health care information and communication, both of which have been greatly affected in recent years by the use of computers and the web by both clinicians and patients. Many practices now use electronic medical records (EMRs) and computerized provider order entry (CPOE). Patients often surf the web seeking health information.

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This article discusses the ethical dilemmas that face the "e-health" world. It also goes over what needs to be done moving forward to address ethical issues.

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E-Health promises to be a cost-effective and efficient way of providing healthcare at an affordable cost to patients who would otherwise be excluded or underserviced. However, e-health also comes with a series of ethical and legal challenges which, if not met before its implementation, could undermine its success. Among other things, changes in the nature of the healthcare professional-patient relationship, in informed consent requirements and in the apportionment of liability are implicated. Privacy concerns also arise and the position of health informatics professionals as well as of service providers is also affected. A further complicating factor is outsourcing.
Common perception and official pronouncements notwithstanding, what healthcare is delivered - and how - is not simply a function of health needs, financial resources and the number and training of available healthcare professionals. To a considerable degree, it is a matter of politics.

Politics, however, is not a rational enterprise nor is it driven by algorithms and formulae, despite the existence of healthcare economists and decision-theorists. It is driven by a mix of public attitudes and the values and agendas of politicians. This is a volatile mix that is subject to many influences. One of the most profound of these is the technological imperative: If something is technologically new, sophisticated and promises to provide a 'rational' solution to a particular problem, then it must be tried. The influence of this imperative is not confined to policy making. It can also be detected at the level of hands-on care.

Indeed, it has been suggested that, for one reason or another, modern healthcare as a whole is obsessed with technology, and that decision-makers and professionals alike cannot wait to apply technological innovations - much like children who must try out every new toy - and that e-health in particular is implicated.

E-Health and its Challenges

Of course such a sweeping view of e-health is unfair. If its proponents are to be believed, e-health has the potential of overcoming barriers of geography, professional availability, limitations of transportation and infrastructure, and even problems caused by socio-economic disparity. Moreover, it holds out the promise of maximising effectiveness and efficiency at the lowest possible cost without seriously interfering in patients' lives. Arguably, therefore, e-health is not so much an instance of the technological imperative as the wise and considerate choice by responsible healthcare planners.

However, the fact remains that to allow technologically grounded effectiveness and efficiency considerations to be the sole determinants of e-health planning is to fall prey to the technological imperative, because e-health comes with a number of potentially serious problems. These are not insurmountable. However, they should be addressed and solved prior to implementation, lest downstream difficulties undermine what otherwise would be a beneficial development.

Technical Reliability and Appropriateness

For example, there are challenges that are rooted in the technology itself. Device safety and standardisation are obviously important issues; as are the technology's ability to ensure data integrity and reliability and its power to gather and communicate data accurately with appropriate back-up measures to guard against malfunction or interruption.

As well, e-health is predicated on the assumption that the instrumentation will provide patient data without constant technical supervision, since this would constitute an intolerable intrusion into patient lives. Not only healthcare providers but also vendors and health informatics professionals (HIPs) are here affected. While similar concerns exist in the standard healthcare setting, the fact that the relevant technology is expected to function independently and reliably in unsupervised and geographically removed settings adds new parameters to the traditional picture.

Privacy, EHRs and Unique Patient Identifiers

Privacy issues also acquire a new dimension. By and large, national laws and international conventions (such as the European Union Directive 96/46/EC) stipulate that healthcare professionals and institutions have a duty to protect the confidentiality of patient data to the best of their ability, and that breaches in this regard should be communicated to the subjects of the data in due time and in an appropriate manner.

Over the past few years, healthcare providers have been at pains to ensure compliance with these requirements and have developed such things as authorisation protocols, password protection, encryption and the like. However, these techniques are geared to a professional context and to an institutionalised setting that can be finely adjusted and controlled. E-Health introduces the patient and the patient's home environment into the mix. Here these measures may be neither possible nor appropriate. Nevertheless, the privacy requirements remain.

Moreover, e-health uses electronic health records (EHRs). This means not only that the records must be accessible on an asneeded basis but also that they contain unique patient identifiers (UPIs) so as to guarantee that the right patient gets the right intervention at the right time. Quite aside from any technical issues about the implementation of an integrated EHR architecture, there is also the issue of UPIs themselves. Both ethical and legal concerns have been raised in this regard relative to such issues as possible 'function creep' in their use.

Patients as Co-Deliverers of Healthcare and Liability

However, challenges are not confined to the technical sector. It would be disastrous for healthcare providers to forget that e-health turns patients (and sometimes their significant others) into active co-participants in the delivery of care. With this, the issue of liability acquires an entirely new form.

That is to say, healthcare has traditionally been based on the assumption that the data underlying healthcare decision-making have been developed by healthcare professionals, and that control of the data gathering process lies in the hands of these professionals and of the technical staff who assist them. Data-related mistakes or misadventures, therefore, are a matter of professional care, diligence and competence.

With e-health, patients become involved in the care delivery process not simply as subjects but as participants. When e-health is not entirely automated through indwelling telemetry, patients have to report the relevant data - and they may make mistakes either in measuring or in reporting values. Even when the process is automatic, it may happen that patients accidentally interfere with these automated measurements or their transmission. Liability apportionment therefore assumes a new aspect, and when family members or significant others are involved the issue of their co-responsibility also arises.
To be sure, there are juridical precedents from other areas of healthcare when patients contribute to negative outcomes. However, these are predicated on the traditional model of the healthcare professional- patient relationship which is based on three premises: First, that healthcare involves a direct patient-professional encounter; second, that the patient's contribution to the functioning of the techniques and technologies employed by the professional is essentially non-existent; and third, that the professional's own expertise determines the availability and reliability of instrument-based patient data and is independent of the patient's skills in the use or functioning of these tools. E-Health importantly changes this picture. Therefore it is doubtful that e-health can rely on the traditional approach to liability. Even the standard consent model may no longer be applicable.

E-Health, therefore, requires not only the development of appropriate patienttraining modules but also the development of new consent and liability models that acknowledges the patient's (and the significant others') expanded agency in the care process.

Furthermore, HIPs play a much more expanded role in e-health since they are the technical lynch-pins of the whole system. Of course HIPs are also integral to any intervention that uses electronic devices in standard healthcare. However, in e-health their role goes beyond providing technical support. The reason is that e-health requires patients to understand the functionally important aspects of the technology with which they are involved because their actions (or lack thereof) may importantly interfere with or alter the functioning of the relevant protocols and devices. True expertise in this regard does not lie in the domain of medicine and healthcare but in the arena of the health informatics. It would therefore be inappropriate to turn communication and patient training in this regard into an addon responsibility for physicians. With this, however, HIPs now acquire an educational and informed-consent duty that supplements those of physicians. It goes almost without saying that the issue of liability apportionment also has to be revisited from this perspective.

Moreover, purely technical issues such as transmission characteristics, bandwidth issues etc., are significant factors in ehealth. Decision-making about what is functionally appropriate in this regard is different from decision-making about whether to use an MRI or some other diagnostic device. The latter is a matter of physician expertise and responsibility; the former, however, is a matter of technical expertise. With e-health, therefore, the role of the HIP expands beyond the traditional scope of purely technical expert and includes giving advice on the choice of the technology itself - with attendant healthcare implications.

Interoperability and Legacy Systems

There is also the following consideration: While e-health is an innovation, it is not an innovation that supplants current healthcare delivery. It extends it. This means that e-health is not a stand-alone modality but something that has to integrate seamlessly into whatever system of healthcare delivery is in place.

This also has ethical and legal implications:

First, the fact that e-health must function in an environment that involves distinct kinds of material items and protocols means that it can be implemented only if it can be incorporated seamlessly into the legacy systems that form part of the established healthcare structure. Interoperability is therefore a necessary material condition for its success, since otherwise treatment may be impaired by the technology and the fiduciary obligation of the attendant healthcare professionals and institutions may be put at risk - with serious legal consequences. This goes beyond guaranteeing interoperability within the institutional setting that is hospital-based. It also includes the problem of integrating e-health with the databases and operating systems that are used by individual physicians and other healthcare providers whose patients participate in e-health.

Second, as a new modality, e-health is subject to close scrutiny regarding appropriateness, safety and the like. However, quality assurance being what it is, this means that such scrutiny will expand beyond the immediate context of e-health to include the existing healthcare structures, so that in case of unusual incidents the operational flaw can be correctly identified as belonging either to the existing structures or to e-health - or to the interface between them. While it is highly likely that sooner or later general quality assessment of existing structures would be undertaken anyway as a matter of continuous quality management, the introduction of e-health may well trigger and accelerate this process. The introduction of e-health, therefore, would not only have ethical and legal implications but financial ones as well that go beyond the costs associated with the development of e-health itself.

Third, like any electronics based technology, e-health is subject to Moore's law. Consequently, given the rapid changes in ICT, diagnostic technology etc., there lies a corresponding duty, rooted in the fiduciary nature of the healthcare provider-patient relationship, to ensure that e-health systems contain within themselves appropriate measures to ensure a seamless legacy structure that integrates as its various aspects, protocols and components as these become obsolete and are replaced. Planning for e-health, therefore, cannot ethically proceed without making appropriate plans in that direction. In other words, it is not a modality that is complete and can be "forgotten" once it is in place. Again, ethical, legal and financial implications stand in the wings.

Outsourcing and its Associated Problems

Another parameter that may deserve attention is outsourcing. Since the decision to become involved in e-health is usually based on cost-effectiveness and cost-benefit considerations, healthcare planners and administrators sooner or later turn to global players both for the applicable technology itself as well as for the provision of relevant services simply because, as a matter of scale and of disparate wages, global players are generally capable of providing the relevant technologies and services at a lower cost.

This means that international corporations specialising in information- and data-management may come to provide such services while themselves being headquartered or located in another jurisdiction. Also, because e-health requires intensive monitoring and a fast turn-around time, and because trained staff is scarce or unavailable on a constant basis, institutions may be tempted to turn to international medical diagnostic and consultative service providers. This is not a speculative scenario. It has already happened in other contexts and on several occasions services that were originally provided by national or local agencies have become outsourced to international providers. Radiographs originating in Chicago have been read in Bangalore or Zurich, billings originating in Berlin or Mexico City have become outsourced to Bloomington or Chennai. Even medical notes that have been taken in one country have been outsourced for transcribing to other countries where the native language of the transcribing individuals is other than that of the note-taking medical professionals. In other words, there is the distinct possibility that in order to employ the relevant technology cheaply, and purely for the sake of "rationalising" the associated costs that accompany the implementation e-health, outsourcing may become a major factor.

Such practices, however, raise privacy concerns to a whole new plateau. International technology- and service-providers are bound not simply by the contracts through which they provide their services but also by the laws of the countries in which they are based as corporate entities. This may make it impossible for the outsourcing parties to guarantee the privacy standards that are mandated within the jurisdiction in which they themselves are incorporated and in which they provide the services that they have outsourced.

Professional standards present another challenge in this connection. Patients expect, and healthcare providers are legally obligated to provide, care that meets the professional standards of the jurisdictions in which the care is actually provided. If outsourcing occurs, an effective and enforceable mechanism must be in place to ensure that there is some means for holding the distant party responsible if professional errors occur, where this mechanism will not be more burdensome or more cumbersome than what is in place in the outsourcing jurisdiction. Otherwise, outsourcing services will occur at the price of patient rights.

E-Health is a technically sophisticated modality of healthcare that is designed not only to provide continuous care where none was possible before, but also to provide such care in a qualitatively unexceptionable manner and at a reasonable cost to a great variety of patients. Moreover, it presents the promise of rationalising health expenditures by limiting institutional admissions and interventions to truly appropriate cases rather than operating in a one-sizefits- all and cost-intensive manner and treating potentially ambulatory patients as institutional cases.

However, like any new modality, e-health is not without its challenges, and these are not merely technical in nature. They include value-issues that go to the very nature of healthcare itself, to the nature of the healthcare provider-patient relationship, to the role and responsibilities of the informatics professional, and they include such issues as informed consent, privacy and liability.

Finally, and perhaps above all, e-health presents a human challenge. Data show that while some patients welcome e-health as an indication of concern for patient wellbeing that is not limited by geographic boundaries, other patients reject it as an unacceptable medicalisation of the home environment and as an intolerable intrusion into their homes and private lives.

None of these issues detract from the promise of cost-effectiveness and efficiency that is presented by e-health. However, this promise should not blind anyone to the fact that, like any technological solution to an existing problem, the solution brings problems of its own, that these are not simply technical in nature - and that they must be addressed before the technology is applied. To forget this would indeed be to act on the basis of the technological imperative with potentially disastrous implications.

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The Ethical and Legal Challenges of E-Health

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E-Health promises to be a cost-effective and efficient way of providing healthcare at an affordable cost to patients who would otherwise be excluded or underserviced. However, e-health also comes with a series of ethical and legal challenges which, if not met before its implementation, could undermine its success.