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Electronic health records: current and future use

This paper provides an overview of the current state of the electronic medical record, including benefits and shortcomings, and presents key factors likely to drive development in the next decade and beyond. The current electronic medical record to a large extent represents a digital version of the traditional paper legal record, owned and maintained by the practitioner. The future electronic health record is expected to be a shared tool, engaging patients in decision making, wellness and disease management and providing data for individual decision support, population management and analytics. Many drivers will determine this path, including payment model reform, proliferation of mobile platforms, telemedicine, genomics and individualized medicine and advances in ‘big data’ technologies.

Steve G Peters*,1 & Munawwar A Khan2 Division of Pulmonary & Critical Care Medicine, College of Medicine, Mayo Clinic, 200 SW First Street, Rochester, MN 55905, USA 2 Department of Systems & Procedures (Internal Business Consulting), Mayo Clinic, 200 SW First Street, Rochester, MN 55905, USA *Author for correspondence: [email protected] 1

Keywords:  big data • clinical decision support • computerized provider order entry • ­computerized records • electronic health records • electronic medical records • population health • population management • practice analytics

Background Digital versions of medical records have been available for decades, yet their adoption has been inconsistent, and at times stormy and controversial. For the purposes of this review, an electronic medical record (EMR) refers to a digital version of the legal record, including documents, results and orders, typically owned by the clinical provider. The term electronic health record (EHR) has evolved to describe an electronic ecosystem that supports features beyond the core EMR, including sharing of information among multiple healthcare professionals and organizations, interoperability among various clinical and nonclinical systems and participation by patients and family members. Although the terms are sometimes used interchangeably, the EMR data are a commonly considered a subset of the broader EHR capabilities [1–3] . The functions of the EHR are included in the requirements of the federal Meaningful Use (MU) program. Basic components of an EMR record include clinical notes, problem list and diagnoses, medications, allergies,

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orders and clinical decision support (CDS). These basic components replaced the traditional paper medical record that served as a primary link between patients and physicians. EHR functionality also presumes capabilities to exchange clinical information and messages with patients using a portal, and basic health information with other providers and organizations. In 2009, the American Recovery and Reinvestment Act included provisions for Health Information Technology for Economic and Clinical Health, intended as a stimulus for the adoption of EHRs, for electronic health information exchange, and for more automated capture and reporting of quality metrics. The MU program, beginning in 2011, defined requirements for EHR use and for staged incentive payments to eligible providers and hospitals [4,5] . The incentives had the expected effect of accelerating the rate of EHR adoption. However, while a majority of physicians have at least basic electronic records, fewer than half of US hospitals qualify for the first stage of MU, and a small fraction can meet stage 2 require-

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Review  Peters & Khan ments [6] . Further, large urban and academic centers are more likely to meet the requirements albeit with varying adoption among specialties, leaving concerns regarding the extent of penetration and the ­ability to exchange health information [7] . Benefits The benefits of electronic records over paper have been well described and acknowledged. Advantages include timeliness, availability, completeness, legibility and (ideally) accuracy [8–10] . Patient safety is facilitated by basic features such as required checking for allergies at the time of medication ordering, and by more advanced CDS [11] . Structured documentation allows for greater compliance with legislative, regulatory and billing requirements and discrete data are more readily captured to track and report quality metrics. Additional features that are desirable and expected by most users include an intuitive and inviting interface, and assistance with and automation of clerical tasks [12] . Clinical data, including diagnoses, allergies, laboratory results, reports, medications and hospital charting, are readily available and easily found. Documentation includes not just clinical notes, but the assessments of nurses and other providers. Increasingly, provider documentation is being augmented by patient provided information. Computerized provider order entry (CPOE) refers to a process that accurately captures information for a requested service. Computerization facilitates the order cycle, or tracking of orders through scheduling, pending and completed stages. CPOE should allow for rapid and unambiguous requests for tests, procedures, prescription medications and referrals across the continuum of care. CDS should guide appropriate action and help to prevent errors. The impact of CPOE has been studied extensively, as this was expected to be one of the most important components of electronic records and processes [13] . Early studies of the impact of CPOE on medication prescribing showed a major reduction in adverse events [11] . Order sets, or groups of standardized care processes and interventions, can improve quality and efficiency, and add clinical guidance [14] . CDS is a feature of most current electronic records, but offers much greater potential than has been realized yet. CDS should be integrated into the workflow, anticipate the need for guidance without distracting interruptions and guide the provider to the most ideal action [15] . Pitfalls & risks For many years, there have been concerns that EHR systems pose unintended risks and pitfalls [16] . General concerns with the electronic environment include:

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dependence on vendors, expense, multiple standards, frequent maintenance and upgrades, response times, complexity, automation resulting in propagated errors, over-reliance on automated systems, introduction of new processes and the potential for new errors [12,17,18] . In 2012, the Institute of Medicine, which had been an early and strong advocate for EHR adoption, published a report citing concerns regarding the safety of healthcare information technology [19] . Recommendations for improvement include postimplementation review, simulation training for providers, careful review of processes that might change in an electronic environment and assessment of the interoperability of systems [20] . CPOE and medication administration processes have been analyzed specifically as potential sources of errors and patient harm [21–23] . Examples of ordering errors include selection of the wrong patient, wrong medication or dose and failure to recognize conflicting or duplicate orders. Optimizing CPOE and CDS design may help mitigate these concerns as there is room for improvement in the use and adoption of ­current systems [24–26] . Future direction The current state of EHR use has been characterized by great promise and measurable benefits, tempered by cautious adoption, variable acceptance and limited integration or interoperability of systems. Design has been driven not just by clinical need and expected benefit to patients, but by financial incentive requirements, regulatory mandates and payment policies. Improvements in core functionality are expected to continue, but future development and use are likely to be driven by a wide variety of patient and provider expectations, new technologies and additional external incentives and requirements. Payment models

As early EMRs were designed to capture information needed primarily for coding and billing, systems in development will advance usability and accommodate external forces that dictate specific features required for incentive payments or to avoid penalties. Financial incentives are intended to encourage adoption of electronic tools, and to improve communication, safety and quality of care. However, MU stages have been modified and timelines delayed in response to feedback from practitioners and institutions that some measures seem cumbersome, counterproductive and unrealistic. Value-based purchasing is a model that provides incentive payments for achieving and reporting core quality measures. Increasingly, such measures are intended to be captured and automatically reported by the EHR. It has been noted, however, that automatic reporting

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may not reflect actual practice, and that improvement in documentation or capture of information is needed [27,28] . Accountable care organizations typically provide bundled or global payments, regardless of the services rendered, resulting in incentives for efficiency and cost reduction by the clinician. An evolving approach to primary care is the ‘patient-centered medical home,’ referring to a model of team-based care stressing quality and population-based outcomes, while controlling expenses [29] . The medical home model is expected to place demands on the EHR for improvements in functionality including team-based care planning, decision support, care transitions and outcome reporting. It appears likely that varying payment and clinical care models will be accompanied by increasing expectations for documentation and reporting by provider ­organizations, and for specific patient populations. Patient engagement

A trend that is likely to lead to major changes in the design and deployment of EHR systems is the drive for patient-centered care. Patient portals, online tools that support review of personal health information such as medications and clinical notes, will play a large role in patient engagement. Messaging inclusive of appointment requests, prescription renewal and questions to providers is also expected. While decision support has been provided to the clinician, a significant trend is the development of shared decision-making tools that allow the provider and patient to discuss goals and evidence-based outcomes of a variety of diagnostic and therapeutic interventions. In a typical scenario, the provider and patient view a screen that summarizes evidence-based outcomes, risks and costs of alternative approaches to a given condition, with opportunity for further discussion. Examples include the use of statins to lower cholesterol, anticoagulation for atrial fibrillation, bisphosphonates for osteopenia and treatment options for noninsulin-dependent diabetes [30–32] . Similar tools can be made available directly to the patient, incorporating specific results from the EHR, further facilitating engagement and empowerment. Patient-provided information prior to a planned visit (e.g., chief complaint, medications, past history and review of systems) is invaluable to the clinical encounter, but often presents an awkward and inefficient workflow. Electronic capture of the previsit information via the patient portal may greatly facilitate an effective encounter. Portals also allow patients to engage in self-care, wellness strategies and better management of chronic conditions [33] . It remains to be seen, however, whether access to a personal EHR or portal results in improved outcomes. Potential concerns include access to clinical notes and reports without clinical inter-

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pretation, although there has been little documented experience of harm [34] . Patient-reported outcomes have become an important part of quality assessment, especially for surgical procedures. Electronic collection of patient-reported outcomes is beginning to emerge as a method for efficient and timely capture of discrete variables. It is likely that many other patient-centered applications, especially for mobile devices, will have major impact in the delivery of medical care and ­clinical information. Telemedicine

Telemedicine refers to the use of technology to provide clinical monitoring, guidance or direct management of an individual not present at a face-to-face visit [35,36] . As telemedicine gains greater adoption, remote monitoring and device integration with the EHR will be major areas of future investment. Many devices exist or are in development to monitor parameters such as vital signs, weight and blood glucose and to transmit results directly to the EHR. These technologies can help to avoid exacerbations of chronic diseases, reduce the need for hospitalization or readmission and facilitate patient engagement [37–39] . While cost avoidance has been predicted, reimbursement models may be a factor in the pace of development and utilization of such technologies [40] . As another aspect of telemedicine, many providers are turning to nonvisit care or e-visits [41] . Depending on the available technology, patients may be able to share other information such as physiologic data, a digital photograph or radiographic images with their providers. Such convenience also extends to healthcare organizations as they are able to schedule e-visits with the appropriate care team, freeing physicians and other licensed providers for more critical office visits. Virtual consultations may provide improved patient convenience and communication, and allow for subsequent encounters to be more appropriately focused [42] . Advanced CDS

While CDS has been characterized by limited adoption and variable results, the future will likely find EHRs employing advanced CDS with vastly matured capabilities. Traditional pop-up alerts will be replaced by actionable, interactive messages that anticipate provider processes and interventions [43,44] . Moreover, alerts will connect providers to the source of knowledge for specific decision making and external reference information. Infobuttons, an MU standard, are tools within the EHR that provide passive decision support by placing links to relevant websites or other knowledge resources [45,46] . Increasingly, recommendations will be contextual and specific to the individual

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Review  Peters & Khan patient. Current pharmacogenomics rules provide advice regarding high-risk drug–gene pairs [47] . Future availability of large-scale genetic and genomic information will allow for individualized medicine, tailoring treatment for existing disorders and prevention of conditions for which the patient is at a high risk [48,49] . Advanced CDS will activate complex algorithms and integrate tightly with order sets, protocols and care pathways. A trend that will further define advanced CDS is the evolution of ‘big data’ techniques, referring to the ability to search across massive sets of data, structured or unstructured (e.g., text, scanned images and video) [50] . Natural language processing, algorithms that allow for machine translation of free text to structured terms, can facilitate the analysis of unstructured data such as clinical notes, pathology reports and other textual findings [51] . CDS utilizing natural language processing may support clinicians with computer-assisted diagnosis and phenotyping [52] . Big data methods can be utilized with CDS for the benefit the individual patient by processing outside material, interpreting reports, comparing this patient to a larger set of similar cases and providing point-of-care recommendations [53] . For populations, basic analytics include general reports and dashboards. Big data techniques can facilitate predictive modeling, probabilities and forecasting and ultimately yield prescriptive analytics that recommend various options while describing likely outcomes. Comparative effectiveness research (CER) using the EHR can be facilitated by standardizing workflow and data collection, and by the analysis of large datasets [54] . CER can be applied at the point of care to provide comparative outcomes of different procedures or treatment regimens for a given condition. As examples, there are many options for management of heart disease, hypertension and diabetes, each associated with different benefits, risks, side effects and costs. Results of CER may be incorporated into the EHR to promote shared decision making and serve as important adjuncts to advanced CDS. Population management

Population health requires a suite of tools within the EHR to assist in the care of cohorts facing similar health challenges [55] . For efficiency and cost–effectiveness, these tools rely on reporting and analytics for nonvisit care and CDS for face-to-face care. Productivity is gained by multiple views of patient populations and the ability to conduct retrospective, concurrent and prospective analyses [56] . A population management program including electronic records and decision support can be associated with decreased costs and lower utilization of inpatient services [57] . As with

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other modules of the EHR, population health activities must be seamlessly integrated within the workflow of ­providers and ancillary staff. Mobile devices & applications

Beyond the laptop, mobile devices and suitable applications offer exciting potential not only to facilitate clinical workflow, but to enable patients and families to engage in their healthcare in ways that are integrated into their daily lives [58,59] . Significant benefits will be realized in the inpatient setting where providers are responsible for multiple patients in disparate locations; however, it is expected that mobile applications will allow ambulatory practitioners to complete sequential tasks efficiently as well. Nurses and allied staff such as phlebotomists, therapists and technicians will use mobile technology to complete most tasks. In addition to mobile devices, other tools will also empower providers, including voice and gesture recognition [60] . Patient and family members will use mobile devices to access personal health records and portals. They will also be able to generate content by completing questionnaires and providing other personal information. The ongoing explosion of the number and variety of healthrelated personal devices and applications is expected to continue, along with greater integration into the EHR. Practice analytics

Business intelligence and analytics will transform the practice and many operational aspects of healthcare organizations. Providers will be expected to measure care processes while continually improving their practice for better outcomes. Practice analytics broadly refers to a set of tools and methods for review and exploration of large volumes of clinical and financial data. As such, practice analytics supports other components of the EHR discussed in this review, for example, outcomes assessment, quality reporting, registries, genomics and population management. Currently, EHR vendors are developing relatively basic tools for descriptive analytics including static reports, near real-time queries, performance dashboards or provider scorecards. Examples include retrospective analytic techniques to assess improvements in outcomes such as quality metrics, hospital length of stay, readmission rates and financial performance [61–63] . More advanced predictive analytics utilize statistical techniques to develop models for forecasting likely outcomes and associated probabilities, including survival [64,65] . The ultimate goal in this progression is the development of prescriptive analytics, intended to combine the predictive models, big data analysis and advanced CDS to offer recommendations ‘optimized’ by synthesizing all available data and relevant evidence.

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Conclusion The EHR is rapidly evolving from a system of basic information capture and legal record by and for the practitioner, to a patient- and team-centered, dynamic, interactive and multimedia platform for healthcare. While we expect enhanced care for the individual, there will also be collection and analysis of data across populations. Advances in telemedicine and mobile technology will allow for care everywhere. Public health, quality and safety reporting and operational initiatives will require large-scale analytics. Software tools, devices and models of care are constantly improving. While human wellness and illness follow well-known paths, the processes of care delivery, information and ­k nowledge management are continually evolving. Future perspective While there are many drivers that will shape the evolution of the EHRs in the future, perhaps none is stronger than patients themselves taking greater ownership over their personal health. Much of this ownership will stem from greater financial responsibility due to changes in insurance benefits. Patients will take advantage of portals from their mobile devices to access personal health records, make appointments, communicate with providers and pay their bills. Moreover, their mobile devices will also empower them to have

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virtual consultations to avoid unnecessary or costly visits. They will also be able to track their health with an ever-increasing number of wearable devices, many of which will send data to the EHR. During hospital or clinic visits, they will benefit directly from treatment plans individualized to their genetic makeup. Meanwhile, providers will benefit from advanced CDS guiding them toward optimal care based on the latest science. Such knowledge will be a means to manage the health of populations, and to coordinate care for patients with chronic diseases. With the flood of data coming into the EHR from various internal and external sources, providers will require business intelligence and analytics tools for contextual and timely decision making. Finally, the EHR will evolve to be tightly integrated with departmental, scheduling and financial systems. Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties. No writing assistance was utilized in the production of this manuscript.

Executive summary • The current electronic medical record evolved as a digital version of the paper chart, modified to capture provider documentation, diagnoses and interventions as a legal record and for coding and billing. The electronic health record builds upon the foundation of an electronic medical record, and includes information exchange among providers, interoperability with ancillary applications and devices and patient engagement. • Benefits of digital records include availability, legibility and completeness. Computerized provider order entry and clinical decision support can reduce errors and guide appropriate interventions. • Pitfalls and potential harm may arise from over-reliance on automated systems, new processes and new types of errors such as selection of wrong patient, medication or intervention. • Future electronic health record development and usage is likely to be driven by multiple factors including advances in technology, regulatory and legislative directions and new payment and practice models. • Team-based care and population management will lead to the development of enhanced tools for health maintenance and chronic conditions. • Patient engagement and empowerment, portals for information review and transaction and shared decisionmaking tools are in rapid evolution. • New types of medical information, especially genetic and genomic data, will require new methods of data storage and clinical presentation, while allowing for advanced clinical decision support and highly individualized recommendations. • Telemedicine, the ability to provide care at a distance, coupled with the rapid growth of mobile, personal and wearable devices, will revolutionize and irrevocably alter the patient–provider interaction. • Massive amounts of digital data and new analytic techniques will allow for enhanced understanding of current practice, but also provide predictive modeling and ultimately prescriptive recommendations that include likely outcomes and associated probabilities. • Comparative effectiveness research can be applied for individuals at the point of care, or for populations, to compare outcomes of different procedures or treatments. Results of comparative effectiveness analysis may promote shared decision making and serve as important adjuncts to advanced decision support.

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Review  Peters & Khan References Papers of special note have been highlighted as: • of interest; •• of considerable interest

Mcgreevey JD 3rd. Order sets in electronic health records: principles of good practice. Chest 143(1), 228–235 (2013).

15

Bates DW, Kuperman GJ, Wang S et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J. Am. Med. Inform. Assoc. 10(6), 523–530 (2003).

16

Smith SW, Koppel R. Healthcare information technology’s relativity problems: a typology of how patients’ physical reality, clinicians’ mental models, and healthcare information technology differ. J. Am. Med. Inform. Assoc. 21(1), 117–131 (2014).

1

Hsiao C-J, Hing E, Ashman J. Trends in electronic health record system use among office-based physicians: United States, 2007–2012. Natl Health Stat. Report 20(75), 1–18 (2014).

2

Kuhn K, Lau F. Evaluation of a shared electronic health record. Healthc. Q. 17(1), 30–35 (2014).

3

Ancker JS, Silver M, Kaushal R. Rapid growth in use of personal health records in New York, 2012–2013. J. Gen. Intern. Med. 29(6), 850–854 (2014).

17

4

Kocher R, Emanuel EJ, Deparle N-aM. The Affordable Care Act and the future of clinical medicine: the opportunities and challenges. Ann. Intern. Med. 153(8), 536–539 (2010).

Fleurant M, Kell R, Jenter C et al. Factors associated with difficult electronic health record implementation in office practice. J. Am. Med. Inform. Assoc. 19(4), 541–544 (2012).

18

5

Wright A, Henkin S, Feblowitz J, Mccoy AB, Bates DW, Sittig DF. Early results of the meaningful use program for electronic health records. N. Engl. J. Med. 368(8), 779–780 (2013).

Jariwala KS, Holmes ER, Banahan BF, 3rd, Mccaffrey DJ, 3rd. Factors that physicians find encouraging and discouraging about electronic prescribing: a quantitative study. J. Am. Med. Inform. Assoc. 20(e1), e39–43 (2013).

19

Institute of Medicine. Health IT and Patient Safety: Building Safer Systems for Better Care. The National Academies Press, WA, USA (2012). 

••

Major report identifying potential harm from unintended consequences of electronic systems, and proposing recommended improvements.

20

Denham CR, Classen DC, Swenson SJ, Henderson MJ, Zeltner T, Bates DW. Safe use of electronic health records and health information technology systems: trust but verify. J. Patient Saf. 9(4), 177–189 (2013).

21

Koppel R, Metlay JP, Cohen A et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA 293(10), 1197–1203 (2005).



520

14

A timely review of the rate of successful achievement of Meaningful Use adoption by eligible providers, citing the potential benefits but unknown impacts on quality, safety and efficiency.

6

Desroches CM, Charles D, Furukawa MF et al. Adoption of electronic health records grows rapidly, but fewer than half of US hospitals had at least a basic system in 2012. Health Aff. (Millwood) 32(8), 1478–1485 (2013).

7

Kokkonen EWJ, Davis SA, Lin H-C, Dabade TS, Feldman SR, Fleischer AB Jr. Use of electronic medical records differs by specialty and office settings. J. Am. Med. Inform. Assoc. 20(e1), e33–e38 (2013).

8

Mcdonald CJ. The barriers to electronic medical record systems and how to overcome them. J. Am. Med. Inform. Assoc. 4(3), 213–221 (1997).



Important study of the types and severity of potential harm resulting from imperfect use of computerized provider order entry systems.

9

Retchin SM, Wenzel RP. Electronic medical record systems at academic health centers: advantages and implementation issues. Acad. Med. 74(5), 493–498 (1999).

22

10

Delpierre C, Cuzin L, Fillaux J, Alvarez M, Massip P, Lang T. A systematic review of computer-based patient record systems and quality of care: more randomized clinical trials or a broader approach? Int. J. Qual. Health Care 16(5), 407–416 (2004).

Poon EG, Cina JL, Churchill W et al. Medication dispensing errors and potential adverse drug events before and after implementing bar code technology in the pharmacy. Ann. Intern. Med. 145(6), 426–434 (2006).

23

Classen D, Bates DW, Denham CR. Meaningful use of computerized prescriber order entry. J. Patient Saf. 6(1), 15–23 (2010).

24

Simon SR, Keohane CA, Amato M et al. Lessons learned from implementation of computerized provider order entry in 5 community hospitals: a qualitative study. BMC Med. Inform. Decis. Mak. 13, 67 (2013).

11

Bates DW, Teich JM, Lee J et al. The impact of computerized physician order entry on medication error prevention. J. Am. Med. Inform. Assoc. 6(4), 313–321 (1999).

••

Classic study of the potential for computerized provider order entry to reduce medication events with potential harm.

25

Nanji KC, Rothschild JM, Salzberg C et al. Errors associated with outpatient computerized prescribing systems. J. Am. Med. Inform. Assoc. 18(6), 767–773 (2011).

12

Agno CF, Guo KL. Electronic health systems: challenges faced by hospital-based providers. Healthcare Manag. (Frederick) 32(3), 246–252 (2013).

26

13

Radley DC, Wasserman MR, Olsho LE, Shoemaker SJ, Spranca MD, Bradshaw B. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J. Am. Med. Inform. Assoc. 20(3), 470–476 (2013).

Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch. Intern. Med. 163(12), 1409–1416 (2003).

27

Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability and validity of quality measures: a review of the literature. Med. Care Res. Rev. 67(5), 503–527 (2010).

J. Comp. Eff. Res. (2014) 3(5)

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Electronic health records: current & future use 

28

Garrido T, Kumar S, Lekas J et al. e-Measures: insight into the challenges and opportunities of automating publicly reported quality measures. J. Am. Med. Inform. Assoc. 21(1), 181–184 (2014).

••

Valuable study of the limitations of automatically reported measures, compared to manual extraction and the potential implications.

29

Depuccio MJ, Hoff TJ. Medical home interventions and quality outcomes for older adults: a systematic review. Qual. Manag. Health Care 22(4), 327–340 (2013).

30

Branda ME, Leblanc A, Shah ND et al. Shared decision making for patients with type 2 diabetes: a randomized trial in primary care. BMC Health Serv. Res. 13, 301 (2013).

31

Mann DM, Ponieman D, Montori VM, Arciniega J, Mcginn T. The Statin Choice decision aid in primary care: a randomized trial. Patient Educ. Couns. 80(1), 138–140 (2010).

32

Montori VM, Shah ND, Pencille LJ et al. Use of a decision aid to improve treatment decisions in osteoporosis: the osteoporosis choice randomized trial. Am. J. Med. 124(6), 549–556 (2011).

41

Angstman KB, Rohrer JE, Adamson SC, Chaudhry R. Impact of e-consults on return visits of primary care patients. Health Care Manag. (Frederick) 28(3), 253–257 (2009).

42

North F, Crane SJ, Chaudhry R et al. Impact of patient portal secure messages and electronic visits on adult primary care office visits. Telemed. J. E. Health 20(3), 192–198 (2014).

43

Herasevich V, Kor D, Subramanian A, Pickering B. Connecting the dots: rule-based decision support systems in the modern EMR era. J. Clin. Monit. Comput. 27(4), 443–448 (2013).

44

Rothman B, Leonard JC, Vigoda MM. Future of electronic health records: implications for decision support. Mt Sinai J. Med. 79(6), 757–768 (2012).

45

Cimino JJ, Jing X, Del Fiol G. Meeting the electronic health record “meaningful use” criterion for the HL7 infobutton standard using OpenInfobutton and the Librarian Infobutton Tailoring Environment (LITE). AMIA Annu. Symp. Proc. 2012, 112–120 (2012).

46

Del Fiol G, Curtis C, Cimino JJ et al. Disseminating context-specific access to online knowledge resources within electronic health record systems. Stud. Health Technol. Inform. 192, 672–676 (2013).



Example of the power and value of patient engagement and empowerment by shared decision-making tools.

47

33

Ammenwerth E, Schnell-Inderst P, Hoerbst A. Patient empowerment by electronic health records: first results of a systematic review on the benefit of patient portals. Stud. Health Technol. Inform. 165, 63–67 (2011).

Bell GC, Crews KR, Wilkinson MR et al. Development and use of active clinical decision support for preemptive pharmacogenomics. J. Am. Med. Inform. Assoc. 21(e1), e93–e99 (2014).

48

Cahill JE, Gilbert MR, Armstrong TS. Personal health records as portal to the electronic medical record. J. Neurooncol. 117(1), 1–6 (2014).

Gottesman O, Kuivaniemi H, Tromp G et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet. Med. 15(10), 761–771 (2013).

49

Mair FS, May C, O’donnell C, Finch T, Sullivan F, Murray E. Factors that promote or inhibit the implementation of e-health systems: an explanatory systematic review. Bull. World Health Organ. 90(5), 357–364 (2012).

Shoenbill K, Fost N, Tachinardi U, Mendonca EA. Genetic data and electronic health records: a discussion of ethical, logistical and technological considerations. J. Am. Med. Inform. Assoc. 21(1), 171–180 (2014).

50

Peters SG, Buntrock JD. Big data and the electronic health record. J. Ambul. Care Manage. 37(3), 206–210 (2014).



Current description of the approach to big data analytics and methodology by the Mayo Clinic.

51

Wagholikar KB, Sundararajan V, Deshpande AW. Modeling paradigms for medical diagnostic decision support: a survey and future directions. J. Med. Syst. 36(5), 3029–3049 (2012).

52

Chute CG, Pathak J, Savova GK et al. The SHARPn project on secondary use of Electronic Medical Record data: progress, plans, and possibilities. AMIA Annu. Symp. Proc. 2011, 248–256 (2011).

53

Wagholikar KB, Maclaughlin KL, Kastner TM et al. Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening. J. Am. Med. Inform. Assoc. 20(4), 749–757 (2013).

54

D’avolio L, Ferguson R, Goryachev S et al. Implementation of the Department of Veterans Affairs’ first point-of-care clinical trial. J. Am. Med. Inform. Assoc. 19(e1), e170–e176 (2012).

55

Friedman DJ, Parrish RG 2nd. The population health record: concepts, definition, design, and implementation. J. Am. Med. Inform. Assoc. 17(4), 359–366 (2010).

34

35

36

37

38

39

40

Takahashi PY, Pecina JL, Upatising B et al. A randomized controlled trial of telemonitoring in older adults with multiple health issues to prevent hospitalizations and emergency department visits. Arch. Intern. Med. 172(10), 773–779 (2012). Blasco A, Carmona M, Fernandez-Lozano I et al. Evaluation of a telemedicine service for the secondary prevention of coronary artery disease. J. Cardiopulm. Rehabil. Prev. 32(1), 25–31 (2012). Logan AG, Irvine MJ, Mcisaac WJ et al. Effect of home blood pressure telemonitoring with self-care support on uncontrolled systolic hypertension in diabetics. Hypertension 60(1), 51–57 (2012). Seto E, Leonard KJ, Cafazzo JA, Barnsley J, Masino C, Ross HJ. Mobile phone-based telemonitoring for heart failure management: a randomized controlled trial. J. Med. Internet Res. 14(1), e31 (2012). Zanaboni P, Landolina M, Marzegalli M et al. Cost-utility analysis of the EVOLVO study on remote monitoring for heart failure patients with implantable defibrillators: randomized controlled trial. J. Med. Internet Res. 15(5), e106 (2013).

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Important description of the characteristics and importance of defining and establishing a repository of the health characteristics of a defined population.

61

Lee J, Kuo Y-F, Goodwin JS. The effect of electronic medical record adoption on outcomes in US hospitals. BMC Health Serv. Res. 13, 39 (2013).

56

Friedman DJ, Parrish RG, Ross DA. Electronic health records and US public health: current realities and future promise. Am. J. Public Health 103(9), 1560–1567 (2013).

62

57

De Leon SF, Pauls L, Shih SC, Cannell T, Wang JJ. Early assessment of health care utilization among a workforce population with access to primary care practices with electronic health records. J. Ambul. Care Manage. 36(3), 260–268 (2013).

Persell SD, Kaiser D, Dolan NC et al. Changes in performance after implementation of a multifaceted electronic-health-record-based quality improvement system. Med. Care 49(2), 117–125 (2011).

63

Sales AE, Lapham GG, Squires J et al. Organizational factors associated with decreased mortality among Veterans Affairs patients with an ICU stay. Comput. Inform. Nurs. 29(9), 496–501 (2011).

64

Edelstein P. Emerging directions in analytics. Predictive analytics will play an indispensable role in healthcare transformation and reform. Health Manag. Technol. 34(1), 16–17 (2013).

65

Mathias JS, Agrawal A, Feinglass J, Cooper AJ, Baker DW, Choudhary A. Development of a 5 year life expectancy index in older adults using predictive mining of electronic health record data. J. Am. Med. Inform. Assoc. 20(e1), e118–e124 (2013).

58

Strayer SM, Semler MW, Kington ML, Tanabe KO. Patient attitudes toward physician use of tablet computers in the exam room. Fam. Med. 42(9), 643–647 (2010).

59

Carlson E, Catrambone C, Oder K et al. Point-of-care technology supports bedside documentation. J. Nurs. Adm. 40(9), 360–365 (2010).

60

522

Williams KA. Voice-recognition/knowledgebase reporting systems for ambulatory care patient records. J. Ambul. Care Manage. 15(3), 55–66 (1992).

J. Comp. Eff. Res. (2014) 3(5)

future science group

Electronic health records: current and future use.

This paper provides an overview of the current state of the electronic medical record, including benefits and shortcomings, and presents key factors l...
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