research proposal on electronic medical records

• Search Menu
• Advance articles
• Editor's Choice
• Focus Issue Archive
• Open Access Articles
• JAMIA Journal Club
• Author Guidelines
• Submission Site
• Open Access
• Call for Papers
• About Journal of the American Medical Informatics Association
• About the American Medical Informatics Association
• Journals Career Network
• Editorial Board
• Advertising and Corporate Services
• Self-Archiving Policy
• Dispatch Dates
• For Reviewers
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Primary care in the u.s., perspective on traditional paper-based record-keeping, definition of electronic ambulatory medical record, motivation for electronic medical records in primary care, return on the emr investment, models of successful emr implementation in primary care, research and education, barriers to adoption of emrs, risks of failure to adopt emrs, recommendations, conclusions, a proposal for electronic medical records in u.s. primary care.

• Article contents
• Figures & tables
• Supplementary Data

David W. Bates, Mark Ebell, Edward Gotlieb, John Zapp, H.C. Mullins, A Proposal for Electronic Medical Records in U.S. Primary Care, Journal of the American Medical Informatics Association , Volume 10, Issue 1, January 2003, Pages 1–10, https://doi.org/10.1197/jamia.M1097

• Permissions Icon Permissions

Delivery of excellent primary care—central to overall medical care—demands that providers have the necessary information when they give care. This paper, developed by the National Alliance for Primary Care Informatics, a collaborative group sponsored by a number of primary care societies, argues that providers' and patients' information and decision support needs can be satisfied only if primary care providers use electronic medical records (EMRs). Although robust EMRs are now available, only about 5% of U.S. primary care providers use them. Recently, with only modest investments, Australia, New Zealand, and England have achieved major breakthroughs in implementing EMRs in primary care. Substantial benefits realizable through routine use of electronic medical records include improved quality, safety, and efficiency, along with increased ability to conduct education and research. Nevertheless, barriers to adoption exist and must be overcome. Implementing specific policies can accelerate utilization of EMRs in the U.S.

To provide all U.S. citizens with good quality, affordable health care, every primary care provider must be given the opportunity of using an electronic ambulatory information system, including a fully functional electronic medical record and with ability to access needed clinical information at the time and place of care. Vision statement of the National Alliance for Primary Care Informatics

Primary care is critical to the provision of excellent medical care. 1 , 2–4 A 1996 Institute of Medicine report defined primary care as the provision of integrated, accessible health care services by clinicians accountable for addressing most personal health care needs, developing a sustained partnership with patients, and practicing in the context of family and community. 4 More people receive care in primary care than in any other clinical setting. 5 In the U.S., the majority of office visits are to primary care providers 6 for care ranging from acute to chronic and preventive, including “priority” conditions such as heart disease, asthma, diabetes, and depression. 7

Primary care providers manage information (from patients and other sources), integrate it with biomedical knowledge, and decide, with patients, on courses of action ( Figure 1 ). Generally, this task is accomplished with pen and paper, despite the availability of many electronic medical record (EMR) systems ( Table 1 ). 8 , 9 Currently, only about 5% of U.S. primary care providers use EMR systems. 10

The flow of information in primary care practice. (Adapted with permission from MH Ebell and P Frame.) 65

Twenty-eight Outpatient Computerized Patient Record Systems 67

Even though U.S. medical care is the world's most costly, its outcomes are mediocre compared with other industrialized nations. A recent World Health Organization (WHO) report ranking the world's health systems placed the United States 37th. 11 The 2001 Institute of Medicine Report, “Crossing the Quality Chasm,” characterized the U.S. system as fundamentally broken 12 and called for major federal investment in information technology as crucial to achieving necessary changes, such as “elimination of most handwritten clinical data by the end of the decade.” Better use of information technology is essential to providing better care at lower cost. 12

Despite its information-intensive nature, the health care industry invests only 2% of gross revenues in information technology, compared with 10% for other information-intensive industries. 13 Increased investment in health care information technology is clearly needed. We believe that the federal government, as the largest purchaser of American health care, should be integral in financing the adoption of electronic records. In the U.S., of $1.3 trillion spent on health care in 2000, public funds (including state sources) accounted for $589.4 billion, or 45%. 14 For health care providers, the federal government recently created unfunded mandates, including complex legislation like the Health Insurance Portability and Accountability Act of 1996 (HIPAA) and the Clinical Laboratory Improvement Amendments. Adopting EMRs would alleviate some of the financial burden of these initiatives, and the federal government should participate financially in this solution.

We argue that the federal government should take the lead in financing an infrastructure to accelerate adoption of electronic records. A public-private partnership should be formed and charged with developing a strategic framework to facilitate EMR implementation, which would result in dividends to public and private health care, emergency preparedness, and the national community.

Family physicians, general internists, pediatricians, nurse practitioners, and physician assistants comprise the main work force delivering primary care in the U.S. Substantial evidence suggests that excellent primary care is important to health. 2 , 3 , 15 , 16 In one study, the quality of primary care physician-patient partnerships correlated with three key outcomes: adherence, satisfaction, and improved health status. 17 Patients value the first-contact and coordinating role of primary care physicians. 18

Primary care providers also deliver important preventive services. In one large study of women, receiving primary care at their regular site strongly correlated with obtaining preventive services. 16 Primary care providers are effective in helping smokers to quit 19 and are central to the treatment of chronic conditions such as diabetes, asthma, and heart disease. 20 In addition to being complex and demanding to treat, these conditions also can be expensive. 21 A cohort study showed that improved glycemic control in a diabetic population was associated with lower costs within 1–2 years. 22 The primary health care system also serves essential national interests by providing an infrastructure for detecting unusual health events and a vehicle for rapidly disseminating information and care during a national emergency. 23

Compared with specialists, primary care providers are reimbursed poorly for the care they give. 24 If viewed narrowly, primary care often loses money. As a result, health care organizations are often reluctant to invest in primary care. To generate investment in information technology for primary care, incentives will be required.

Historically, providers have documented and delivered care using paper records because of their simplicity, low implementation cost, and widespread acceptance. However, paper records have significant disadvantages: availability to only one person at a time; frequent illegibility; inability to be accessed remotely or at the time and place needed; growing so thick as to be unwieldy; low utility and large overhead as vehicles to evaluate quality; and segmentation with multiple volumes and multiple storage sites. The most serious problem with paper records is that they impede provision of clinical decision support; data stored in inaccessible formats cannot incorporate or trigger decision support tools.

Ambulatory EMRs typically include a problem list, medication list, allergy list, notes, health maintenance information, and results retrieval (for laboratory, radiology, and other testing results). Most EMRs include a computerized prescribing tool, and many also include computerized ordering. These feature tools for both displaying and capturing data such as notes. While having complete, legible, and organized information is advantageous, benefits can multiply when decision support is provided using electronic applications.

The unaided human mind simply cannot process the current volume of clinical data required for practice, especially relevant given the broad scope of primary care. Tolentino suggests that “voltage drops” occur in the transmission of medical knowledge. 25 As information becomes obsolete, it is not refreshed, and new knowledge cannot be integrated. Thus, physicians take “short cuts,” using clinical experience and heuristics rather than pursuing organized investigations. The advent of genomics will only make this problem worse.

Primary care providers have many important information needs that are not being met. 26 Studies of these needs 27–29 suggest that physicians have about 8 unanswered questions for every 10 ambulatory visits. If physicians adopt EMRs, one benefit may be to improve access to electronic information resources.

Because of their integrating and coordinating function, electronic records are especially important for care of certain populations, such as rural residents, children, pregnant women, lactating mothers, and the elderly, who depend heavily on primary care physicians. 30 Poor and underserved populations may require different primary care services.

The dream of converting from paper to electronic charts has a long history. 31–33 Three recent developments make it time for this dream to become a reality. First, given the widely dispersed nature of primary care services, the Internet can now play a critical role in this transformation. 34 High-speed connections from physician offices can provide web-based clinical tools using an application services provider (ASP) model (see Table 1 ). Second, the speed and power of readily available computers are increasing and their costs decreasing. Third, computers and software are evolving rapidly, so that mobile devices can be easily linked to wireless medical networks. 35 Handheld computers can be useful sources of drug and other information 36 and in the near future will likely help to extend desktop networks.

Although the full range of EMR benefits will not become clear until more systems are implemented and more processes computerized, 37 EMR systems can already improve efficiency and quality. The costs of “chart pulls” can be eliminated, and dictation costs can be substantially reduced. Providers can also receive decision support regarding the costs and selection of drugs, laboratory tests, and radiographic studies. By making a number of changes identified by EMR data, such as identifying the least expensive drug within a class, providers were able to reduce drug costs by 18% (personal communication, J. M. Overhage, September 2001). Several studies showed that displaying charges for tests, 38 the last test result of that type, 39 and prediction whether a specific future result would be abnormal given prior results 40 independently reduced laboratory test use by 10–15%. The EMR is available 24 hours daily, 7 days a week; can be viewed by more than one user at a time; is available from remote locations; can nearly always be found; and is legible. A covering physician can rapidly get a sense of a patient's problems by quickly reviewing those problems, medications, and recent notes in the EMR.

Even more than improving efficiency, quality may be the greatest benefit of computerization. Computer-ization of reminders and prevention guidelines benefits patients. 41 Reminders are also effective in care of chronic conditions, such as diabetes ( Figure 2 ). 42 Computerization of medication prescribing improves safety; in one study of inpatients, the medication error rate was reduced by more than 80%. 43 Com-munication between patients and providers appears to represent a particular problem in outpatient care, 44 and computerization may be helpful in this domain. Another quality improvement benefit will likely come from monitoring and tracking abnormal results and ensuring that appropriate follow-up occurs. Moreover, electronic records can be linked with public health surveillance, which may be extremely important in emergencies such as a bioterrorism attack or an epidemic. 23

“Face Sheet” for a typical patient. When a primary care provider sees a patient, the EMR typically provides a snapshot of key information, including but not limited to the patient's demographics, problem list, medications, and health maintenance information. These and other data can be used to generate a set of reminders, which improve the likelihood that a patient will actually receive needed care. 66 Without such decision support, it is extremely hard to rapidly determine what actions are due.

To deliver the same or better care at similar or lower costs, we need to measure quality routinely. The public and payers are increasingly demanding quality measurement, 45 which becomes vastly easier when using EMRs, since aspects of chart reviews can be automated. 46 EMRs facilitate sharing medical information between patients and providers. 12 A related variety of patient-centered and community-based EMR experiments are ongoing. 46

Electronic medical records will also have important benefits for specialty care. For example, poor communication plagues the current referral process 47 and could be ameliorated through computerization. Poorly coordinated care can lead to adverse drug events, unnecessary tests and treatments, and higher costs. Critical linkages between specialty services and primary care cannot be established until the EMR is developed sufficiently to interface across a spectrum of settings.

The overall return on investment for introducing electronic medical records into primary care remains to be determined. Few studies have been performed, most by system vendors; thus, the results must be viewed with caution. Nevertheless, the limited available data suggest that this return is excellent. 37 , 48 For example, Renner evaluated the costs and benefits of implementing an electronic medical record for a 40-physician primary care group and found that its net present value was $279,670 in 1996 dollars based on a 5-year model. 37 Reducing drug costs and preventing adverse drug events appear to be areas of greatest benefit in primary care. 48 Further independent analyses are clearly needed.

The degree of benefit of EMRs to a health care organization depends on the reimbursement system, with return being lower in a fee-for-service environment and higher with capitation. In a fee-for-service system, third-party payers will realize many of the benefits. For example, unless providers are at financial risk for medications, savings in drug costs accrue to payers. However, from the societal perspective, return on investment is high. Thus, it is critical that the costs of EMRs must be borne in part by payers, including the federal government. 37 , 49

Both Australia and England have implemented highly successful national programs to promote the use of electronic medical records in primary care. 50–52 Other countries, including New Zealand and the Nether-lands, have also achieved substantial success. 53 In terms of speed, Australia's results have been most dramatic. In May 2000, 70% of general practices stated that the majority of physicians in their practice were using a computer in their consulting room to generate most of their prescriptions, compared with only 15% of general practitioners reporting computer use for any purpose in October 1997. 50 Australia achieved this remarkable transition by providing general practitioners with financial support to help purchase a computer, supporting system implementation for those who needed it, and offering incentives for providers to submit claims electronically.

England has made greater progress, albeit more slowly. Currently, 98% of general practitioners have access to an EMR on their desktop. Nearly all use it for prescription refills, and 30% report that their practices are paperless (personal communication, Michael Bainbridge, November 4, 2001). Just three vendors supply these records; accreditation is required for the sale of systems. An application called Prodigy interacts with these applications and provides evidence-based decision support; the plan is to distribute this application to all primary care clinicians. 54

Each of these countries made a national investment in a coordinating group to develop a strategic framework and identify standards. Development of the actual records has been carried out by private vendors, who have benefited from having a common set of goals and standards. In addition, each country developed incentives for providers to make the transition from paper to electronic records.

While primary care electronic medical records and clinical decision support are useful, additional fundamental research is required. Early research in informatics took place in the outpatient setting; many exemplary EMRs were ambulatory systems, including Costar, the Regenstrief Medical Record, and The Medical Record. 33 , 55 More recently, research has focused on hospital settings. While electronically generated clinician reminders have proved effective in multiple clinical settings, only limited information exists about why clinicians often fail to follow computer-generated advice. Questions exist regarding the most effective ways to deliver reminders and decision support advice. Additional research is needed to begin to consider how information should be organized and delivered, how patients can become involved, what role patient-managed records should play, and how communication between providers and patients can be improved. Research investment is essential if we want to improve the way evidence is provided at the point of care.

Electronic media will become central to the delivery of medical education. As recognized many years ago, electronic records have the potential to provide information at the “teachable moment.” 31 Yet few data are available regarding how physicians and other clinicians learn from such records, how information and knowledge can best be delivered, and what the impact of making it available would be. Testing and evaluating alternative strategies are desperately needed. Training opportunities in primary care informatics are limited, typically involving only enrollment in existing “generic” medical informatics programs.

Several barriers exist to adoption of EMRs, but none are insurmountable. A major impediment is the initial cost of EMR systems. Identifying who will make the investment is difficult. The 1991 Institute of Medicine report on the Computerized Patient Record (CPR) suggested that “the cost of CPR systems should be shared by those who benefit from the value of the CPR.” 56 However, a 1997 update on progress related to the 1991 recommendations by Detmer and Steen 57 concluded that “no progress can be reported” in this area and that “this remains a significant hurdle.” Many financial benefits of EMR usage accrue to third-party payers and purchasers of health care rather than to the provider groups and networks who invest in them. An exception is large integrated delivery systems such as Kaiser and Group Health, which have gained from making large EMR investments. The Leapfrog Group, a consortium of the nation's largest employers, may soon recommend adoption of physician-office–based clinical information systems, which could promote higher reimbursement for providers using EMRs (personal communication, Arnold Milstein, November 12, 2001).

Another barrier has been the transience of vendors; many early EMR developers are in precarious financial positions or even defunct. Consequently, primary care practices see implementing EMRs from current vendors as risky. The risk could be reduced if vendors adopted common data standards. 58 Transferring of legacy data to a new system would then be more practical, even though the significant overhead of workflow disruption due to system changeover would remain.

Physician resistance to use of EMR systems is another issue. The authors believe that this resistance stems from physicians' perceptions that EMR usage negatively affects their workflows. For example, data entry may take extra time, and time is the most precious commodity to physicians. 59 , 60 Even though the learning curve for system usage may be steep, a recent study demonstrated that by using a well-developed and properly refined primary care electronic record, clinician ordering can become faster than with use of a paper record. 61 With proficient use, EMRs can reduce the overall time spent by clinicians on related activities. 61 Improved speed and efficiency may not be realized with all systems; thus, this criterion should be a key issue in vendor selection. As physicians become more adept with computers, resistance based on lack of familiarity or facility will diminish.

Concern about security and confidentiality of electronic information is an important issue, 62 and to develop and assess security strategies much work remains. However, the basic technology to ensure data safety is available today. 63

Several risks may ensue by not pursuing EMR implementation. The United States is falling behind other countries because of its failure to computerize information related to common, important problems managed in the primary care setting. Many people may benefit from new drugs and devices that depend on computerized information. Failure to computerize will lead to missed opportunities in public health care delivery and preventative services, and efforts to deal with bioterrorism may suffer. Without a national plan or standards, each insurer could promote its own EMR that is incompatible with others, resulting in an automated Tower of Babel.

A joint effort by stakeholders in primary care is needed to handle issues related to adopting EMRs. Without a central organizing group to coordinate efforts, many desired benefits will not be realized. For example, widespread implementation of messaging and other types of standards is critical to enabling information exchange among providers, hospitals, ancillary groups (such as laboratories and radiology departments), and payers. Good standards exist for many domains. For clinical messaging, the National Center for Vital and Health Statistics has recommended HL7 as the core standard to the secretary of Health and Human Services. 58 Endorsement of this recommendation by HHS, followed by mandating HL7 use—especially by the Center for Medicare and Medicaid Services—would be a major step forward.

Other prior efforts to form a central organizing group around EMRs have been made. In 1992, following a recommendation of the Institute of Medicine, the Computer-Based Record Institute (CPRI) was incorporated. 56 The CPRI has produced a range of white papers and programs: functional descriptions of the computer-based patient record (CPR); promotion of EMR-related standards; the Davies Recognition Award program for Excellence in CPR Implementa-tion; and others. In 2000, CPRI and Healthcare Open Systems and Trials (HOST) were consolidated to form CPRI-HOST.

To advocate for the steps needed to increase the likelihood that electronic records will be adopted in primary care, we have established a centralized coordinating group, the National Alliance for Primary Care Informatics. This group includes key stakeholders (see Table 2 ) interested in strategic planning and activities that can rapidly incorporate EMRs into American primary care. Given the present financial climate in health care, individual organizations are unlikely to be able to provide adequate financial backing.

Organizational Endorsements

We believe it imperative that the federal government proffer funds to coordinate infrastructure development for and implementation of EMR systems in primary care. The “Crossing the Quality Chasm” report recommended $1 billion for developing a National Information Infrastructure. 12 Part of this investment should support a primary care group that would convene key working groups, develop the strategic framework, and undertake specific projects. The coordinating group would represent a public-private partnership and should be located within the federal Department of Health and Human Services. The Agency for Healthcare Research and Quality (AHRQ) or the National Library of Medicine (NLM) should be home for the center. The existing locus of national efforts to improve patient safety, AHRQ has a track record of highly successful interagency collaboration and cooperation. 64 The NLM also should be involved, as it has supported relevant informatics research for decades and has successfully distributed biomedical data and bibliographic databases worldwide.

Specifically, we recommend the following:

To facilitate adoption of EMRs in primary care, a coordinating infrastructure should be established, with $20,000,000 initial funding.

This group should be multidisciplinary and include representation from providers, payers, vendors, government, employers, and consumers.

This group should work to promote selection of standards for specific content areas and should work closely with existing standards-setting groups such as HL7.

The group should foster large national pilot studies of specific strategies to accelerate EMR adoption.

The group should recommend specific practices and policies, such as zero-interest loans and increased reimbursement, for users of electronic medical records, electronic prescribing, and electronic decision support, which would require additional investment beyond that described above.

Electronic medical records provide many benefits, especially to primary care. EMRs will eventually become the standard of care. However, the initiatives and investments recommended in this paper can lower the costs of and accelerate EMR adoption as well as facilitate achievement of benefits such as common national standards for clinical data. Because this effort would benefit the entire population and favorably affect the large health care portion of the federal budget, the government should act to facilitate development of a public-private partnership to encourage adoption of electronic medical records in primary care.

The authors thank the members of the Primary Care Working Group of the American Medical Informatics Association and Anne Kittler for assistance with manuscript preparation.

Starfield B . Primary care and health. A cross-national comparison . JAMA 1991 ; 266 : 2268 – 2271 .

Google Scholar

Starfield B . Health care reform: the case for a primary care imperative . Health Care Manage 1994 ; 1 : 23 – 34 .

Starfield B . Primary care . J Ambul Care Manage 1993 ; 16 : 27 – 37 .

Institute of Medicine Primary Care: America's Health in a New Era . Washington, DC , National Academy Press , 1996 .

Google Preview

Green LA Fryer GE Jr Yawn BP Lanier D Dovey SM . The ecology of medical care revisited . N Engl J Med 2001 ; 344 : 2021 – 2025 .

National Center for Health Statistics . National Ambulatory Medical Care Surveys . Public use data file and documentation 1999 .

Green LA . Is primary care worthy of physicians? Robert Wood Johnson Foundation Conference Proceedings [in press].

Epic Systems Corporation . Information for the Continuum of Care . Available at: http://www.epicsys.com/ . Accessed August 23, 2002.

Medscape . EMRBusiness Benefits Available at: http://www.medicalogic.com/emr/business_benefits/index.html . Accessed October 22, 2001.

Anderson JD . Increasing the acceptance of clinical information systems . MD Comput 1999 ; 16 ( 1 ): 62 – 65 .

Anderson GF Poullier JP . Health spending, access, and outcomes: trends in industrialized countries . Health Aff (Millwood) 1999 ; 18 : 178 – 192 .

Institute of Medicine . Crossing the Quality Chasm: A New Health System for the 21st Century . Washington, DC , National Academy Press , 2001 .

Raymond B Dold C . Clinical Information Systems: Achieving the Vision . Prepared for the Meeting “The Benefits of Clinical Information Systems” Sponsored by the Kaiser Permanente Institute for Health Policy 2001 .

Heffler S Levit K Smith S Smith C Cowan C Lazenby H et al.  . Health spending growth up in 1999; faster growth expected in the future . Health Affairs 2001 ; 20 : 193 – 203 .

Franks P Nutting PA Clancy CM . Health care reform, primary care, and the need for research . JAMA 1993 ; 270 : 1449 – 1453 .

Bindman AB Grumbach K Osmond D Vranizan K Stewart AL . Primary care and receipt of preventive services . J Gen Intern Med 1996 ; 11 : 269 – 276 .

Safran DG Taira DA Rogers WH Kosinski M Ware JE Tarlov AR . Linking primary care performance to outcomes of care . J Fam Pract 1998 ; 47 : 213 – 220 .

Grumbach K Selby JV Damberg C Bindman AB Quesenberry C Jr Truman A et al.  . Resolving the gatekeeper conundrum: what patients value in primary care and referrals to specialists . JAMA 1999 ; 282 : 261 – 266 .

Cohen SJ Stookey GK Katz BP Drook CA Smith DM . Encouraging primary care physicians to help smokers quit. A randomized, controlled trial . Ann Intern Med 1989 ; 110 : 648 – 652 .

Renders CM Valk GD Griffin SJ Wagner EH Eijk Van JT Assendelft WJ . Interventions to improve the management of diabetes in primary care, outpatient, and community settings: a systematic review . Diabetes Care 2001 ; 24 : 1821 – 1833 .

Davis RM Wagner EH Groves T . Managing chronic disease . BMJ 1999 ; 318 : 1090 – 1091 .

Wagner EH Sandhu N Newton KM McCulloch DK Ramsey SD Grothaus LC . Effect of improved glycemic control on health care costs and utilization . JAMA 2001 ; 285 : 182 – 189 .

Overhage JM Dexter PR Perkins SM Cordell WH McGoff J McGrath J et al.  . A randomized, controlled trial of clinical information shared from another institution . Ann Emerg Med 2002 ; 39 : 14 – 23 .

Physician income trends vary by specialty . Managed Care 2001 ; 10 : 17 .

Tolentino, Herman D. Medical Informatics . Available at: http://www.veranda.com.ph/hermant/meded.htm . Accessed August 23, 2002.

Westberg EE Miller RA . The basis for using the Internet to support the information needs of primary care . J Am Med Inform Assoc 1999 ; 6 ( 1 ): 6 – 25 .

Covell DG Uman GC Manning PR . Information needs in office practice: are they being met? Ann Intern Med 1985 ; 103 ( 4 ): 596 – 599 .

Gorman PN Helfand M . Information seeking in primary care: how physicians choose which clinical questions to pursue and which to leave unanswered . Med Decision Making 1995 ; 15 ( 2 ): 113 – 119 .

Ash JS Gorman PN Lavelle M Lyman J Delcambre LM Maier D et al.  . Bundles: meeting clinical information needs . Bull Med Library Assoc 2001 ; 89 ( 3 ): 294 – 296 .

Fryer GE Green LA Dovey SM Phillips RI Jr . The United States relies on family physicians unlike any other specialty . Am Fam Physician 2001 ; 63 : 1669 .

Weed LL . Medical records that guide and teach . N Engl J Med 1968 ; 278 : 593 – 600 .

McDonald CJ . Protocol-based computer reminders, the quality of care and the non-perfectability of man . N Engl J Med 1976 ; 295 ( 24 ): 1351 – 1355 .

Stead WW . Zydis: A new dosage form can be confused with a drug name: A quarter-century of computer-based medical records . MD Comput 1989 ; 6 ( 2 ): 74 – 81 .

Shortliffe EH . The evolution of electronic medical records . Acad Med 1999 ; 74 : 414 – 419 .

Shah M . msJAMA . Grassroots computing: palmtops in health care . JAMA 2001 ; 285 : 1768 .

Clinician use of a palmtop drug reference guide . J Am Med Inform Assoc 2002 ; 9 ( 3 ): 223 – 229 .

Renner K . Cost-justifying electronic medical records . Healthcare Finan Manage 1996 ; 50 : 63 – 64 .

Tierney WM Miller ME McDonald CJ . The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests . N Engl J Med 1990 ; 322 : 1499 – 1504 .

Tierney WM McDonald CJ Martin DK Rogers MP . Computerized display of past test results. Effect on outpatient testing . Ann Intern Med 1987 ; 107 : 569 – 574 .

Tierney WM McDonald CJ Hui SL Martin DK . Computer predictions of abnormal test results. Effects on outpatient testing . JAMA 1988 ; 259 : 1194 – 1198 .

Balas EA Weingarten S Garb CT Blumenthal D Boren SA Brown GD . Improving preventive care by prompting physicians . Arch Intern Med 2000 ; 160 : 301 – 308 .

Lobach DF Hammond WE . Development and evaluation of a computer-assisted management protocol (CAMP): improved compliance with care guidelines for diabetes mellitus . Proc Ann Symp Comp Appl Med Care 1994 ; 787 – 791 .

Bates DW Teich J Lee J Seger D Kuperman G Ma'luf N et al.  . The impact of computerized physician order entry on medication error prevention . J Am Med Inform Assoc 1999 ; 6 : 313 – 321 .

Gandhi TK Bates DW Burstin HR Cook EF Haas JS Brennan TA . Drug complications in outpatients . J Gen Intern Med 13 ( suppl ): 46 . 1998 .

Bates DW Gawande AA . The impact of the internet on quality measurement . Health Aff (Millwood) 2000 ; 19 ( 6 ): 104 – 114 .

Gawande AA Bates DW . The Use of Information Technology in Improving Medical Performance: Part III . Patient-Support Tools. Medscape General Medicine 2 ( 1 ), 2000. Available at: http://www.medscape.com/viewarticle/408035 . Accessed August 23, 2002.

Gandhi T Sittig DF Song J Franklin M Teich JM Komaroff AL Bates DW . The outpatient referral process: what is the diagnosis and treatment? Proc Ann AMIA Fall Symp 1998 .

Bates DW Studer J Reilly CA Cureton EI Spurr CD Kuperman GJ . Evaluating the Impact of a Computerized Ambulatory Record . Proc Ann AMIA Fall Symp 2000 .

Nutting PA Green LA . Practice-based research networks: reuniting practice and research around the problems most of the people have most of the time . J Fam Pract 1994 ; 38 : 335 – 336 .

Kidd MR Mazza D . Clinical practice guidelines and the computer on your desk . Med J Austr 2000 ; 173 : 373 – 375 .

Mount CD Kelman CW Smith LR Douglas RM . An integrated electronic health record and information system for Australia? Med J Austr 2000 ; 172 : 25 – 27 .

Purves IN Sugden B Booth N Sowerby M . The PRODIGY project—the iterative development of the release one model . Proceedings / AMIA Annual Symposium 1999 ; 359 – 363 .

Thakurdas P Coster G Gurr E Arroll B . New Zealand general practice computerisation; attitudes and reported behaviour . N Z Med J 1996 ; 109 : 419 – 422 .

NHS Information Authority . Building the information Core: Implementing the NHS Plan . Available at: http://www.nhsia.nhs.uk . Accessed August 23, 2002.

Hunt DL Haynes RB Hanna SE Smith K . Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review . JAMA 1998 ; 280 : 1339 – 1346 .

The Computer-Based Patient Record: An Essential Technology for Health Care . Washington, DC , National Academy Press , 1991 .

Detmer DE Steen EB . The computer-based record: patient moving from concept toward reality . Int J Biomed Comput 1996 ; 42 : 9 – 19 .

Department of Health and Human Services . The National Committee on Vital and Health Statistics . Available at: http://ncvhs.hhs.gov . Accessed August 23, 2002.

Overhage JM Tierney WM McDonald CJ Pickett KE . Computer-assisted order entry: impact on intern time use . Clin Res 1991 ; 39 ( 3 ): 729A .

Tierney WM Miller ME Overhage JM McDonald CJ . Physician inpatient order writing on microcomputer workstations. Effects on resource utilization . JAMA 1993 ; 269 : 379 – 383 .

Overhage JM Perkins S McDonald CJ . The impact of direct physician order entry on physician time utilization in a university affiliated, ambulatory primary care internal medicine practice . J Am Med Inform Assoc [in press].

American Academy of Pediatrics . Privacy Protection of Health Information: Patient Rights and Pediatrician Responsibilities (RE9927) . Pediatrics 104 (4 Pt 1):973–7, 1999 Oct. Available at: http://www.aap.org/policy/re9927.html . Accessed August 23, 2002.

Olson LA Peters SG Stewart JB . Security and confidentiality in an electronic medical record . Healthcare Inform Manage 1998 ; 12 : 27 – 37 .

FirstGov . Quality Interagency Coordination(QuIC) Task Force Available at: http://www.quic.gov . Accessed August 23, 2002.

Ebell MH Frame P . What can technology do to, and for, family medicine? Fam Med 2001 ; 33 : 311 – 319 .

Karson A Kuperman G Horsky J Fairchild DG Fiskio J Bates DW . Patient-specific computerized outpatient reminders to improve physician compliance with clinical guidelines . J Gen Intern Med 2000 .

Rehm S Kraft S . Electronic Medical Records: The FPM Vendor Survey . Fam Pract Manage 2002 ; January 2001 : 45 – 54 .

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1527-974X
• Copyright © 2024 American Medical Informatics Association
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Elsevier - PMC COVID-19 Collection

Successfully implementing a national electronic health record: a rapid umbrella review

Orna fennelly.

a Insight Centre for Data Analytics, University College Dublin, Ireland

b School of Public Health, Physiotherapy and Sports Science, University College Dublin, Ireland

Caitriona Cunningham

Loretto grogan.

c Office of the Nursing and Midwifery Services Director, Health Service Executive (HSE), Ireland

Heather Cronin

d National Rehabilitation Hospital, Dublin, Ireland

Conor O’Shea

e Irish College of General Practitioners, Ireland.

Miriam Roche

f Maternal and Newborn Clinical Management System National Project Team, HSE, Ireland

Fiona Lawlor

g St. James’ Hospital, Dublin, Ireland

Neil O’Hare

h Ireland East Hospital Group, HSE, Ireland

To summarize the findings from literature reviews with a view to identifying and exploring the key factors which impact on the success of an EHR implementation across different healthcare contexts.

Introduction

Despite the widely recognised benefits of electronic health records (EHRs), their full potential has not always been achieved, often as a consequence of the implementation process. As more countries launch national EHR programmes, it is critical that the most up-to-date and relevant international learnings are shared with key stakeholders.

A rapid umbrella review was undertaken in collaboration with a multidisciplinary panel of knowledge-users and experts from Ireland. A comprehensive literature review was completed (2019) across several search engines (PubMed, CINAHL, Scopus, Embase, Web of Science, IEEE Xplore, ACM Digital Library, ProQuest, Cochrane) and Gray literature. Identified studies (n = 5,040) were subject to eligibility criterion and identified barriers and facilitators were analysed, reviewed, discussed and interpreted by the expert panel.

Twenty-seven literature reviews were identified which captured the key organizational, human and technological factors for a successful EHR implementation according to various stakeholders across different settings. Although the size, type and culture of the healthcare setting impacted on the organizational factors, each was deemed important for EHR success; Governance, leadership and culture, End-user involvement, Training, Support, Resourcing , and Workflows. As well as organizational differences, individual end-users have varying Skills and characteristics, Perceived benefits and incentives , and Perceived changes to the health ecosystem which were also critical to success. Finally, the success of the EHR technology depended on Usability, Interoperability, Adaptability, Infrastructure, Regulation, standards and policies , and Testing.

Fifteen inter-linked organizational, human and technological factors emerged as important for successful EHR implementations across primary, secondary and long-term care settings. In determining how to employ these factors, the local context, individual end-users and advancing technology must also be considered.

1. Introduction

Capturing and effectively using clinical information and knowledge to ensure a quality, safe and sustainable healthcare service is widely recognised as important [ 1 , 2 ] and data from electronic health records (EHRs) have been vital to decision-making on public health policies during the COVID-19 pandemic [ 3 ]. An EHR provides a longitudinal record of information regarding the health status of an individual in computer-processible form across practices and specialists, and enables authorised access to clinical records in real-time [ 4 , 5 ]. As well as expanding the capacity to utilise clinical data for monitoring of patient outcomes and conducting audits and research [ 6 , 7 ], the EHR provides access to patient information in a timely manner, enabling healthcare professionals (HCPs) to spend more time with patients 8 , reducing duplication of tests and work, and improving the safety and quality of care provided [ 4 , 7 , [9] , [10] , [11] , [12] , [13] , [14] ]. Additionally, integration of other functions and software, such as clinical decision support and bar code medication administration, further expand its potential benefits [ 15 , 16 ].

Electronic patient records (EPRs) or electronic medical records (EMRs) also offer many of these benefits but solely contain the records from an individual organization. Whilst shared or summary care records and patient portals respectively store and facilitate access to specific patient information required by HCPs [ 17 ] and patients [ 18 ]. Despite the number of benefits which can be derived from these systems, challenges have been met in implementing a fully interoperable EHR between primary and secondary care [ 13 , 19 ], often attributed to the implementation process as opposed to the product supplied by the EHR vendor [ 20 , 21 ]. Therefore, the implementation process is critical [ 22 ] and must be considered as an ongoing process beginning during procurement and continuing throughout each phase of design, development, testing, ‘Go Live’ and optimization.

Whilst hospital information systems (HIS) in the USA have been in existence since the 1960s [ 23 ], HIS are a more recent phenomenon in the Republic of Ireland where public healthcare is managed by the Health Service Executive (HSE) which co-exists with a private health system. The Office of the Chief Information Officer (CIO) has overall responsibility for embedding technology within the health infrastructure [ 24 ] and to date, EPRs have been implemented in some individual private and public hospitals and the majority of general practitioner (GP) offices (i.e., private primary care physicians often with HSE contracts), as well as for specific cohorts of patients (e.g., maternal and newborn, and epilepsy) [ 25 ]. However, many other hospitals and HSE primary care (i.e., community) centres remain largely paper-based. With an EHR in the pipeline [ 24 , 26 ], three national projects have been planned by eHealth Ireland which is led by the Office of the CIO; Acute EHR, Community EHR and the Shared and Integrated Care Record. Therefore, this is an opportune time for policy-makers and other key stakeholders to review the learnings from the implementations of health information technology (HIT) both in Ireland and internationally.

However, a vast amount of literature is published on topics such as EHRs which renders it difficult for policy-makers to remain up-to-date [ 27 , 28 ], perhaps amplifying the “know-do” gap. Additionally, healthcare is a complex and adaptive system which needs to be recognized and acknowledged when attempting to replicate successes in another context [ 29 ]. The EHR programme in Ireland is also already underway and therefore, it’s critical that knowledge is generated to provide actionable and relevant key considerations in a timely manner aligned with the policy and decision-making cycles [ 30 ]. Therefore, the aim of this review is to identify and explore the key factors which promote a successful EHR implementation across healthcare settings, with active collaboration from key stakeholders in the Irish context.

2.1. Design

A rapid umbrella review was conducted and guided by the World Health Organisation (WHO) practical guide for Rapid Reviews to Strengthen Health Policy and Systems [ 31 ]. Unlike a systematic review, an umbrella review also known as a review of reviews, compiles evidence from several research syntheses across different healthcare contexts and stakeholder groups [ 32 , 33 ]. Active collaboration with an expert panel of knowledge users facilitated the acceleration of the systematic review process [ 30 ] and to facilitate uptake and use of these findings by planners and decision-makers, the synthesized findings were also presented in a report format [ 34 ].

2.2. Expert panel of knowledge users

A multi-disciplinary panel of experts and knowledge users (n = 10) were engaged and involved throughout the review process to inform its methodology, validate the generalizability and relevance of the review findings [ 35 ], and ensure it reflects current thinking and is useful [ 27 ]. The panel was convened in January 2019 by the Office of Nursing and Midwifery Services Director (HSE) and comprised of those currently involved in large HIT implementation projects across primary and secondary care at local and national levels in Ireland, as well as clinicians, health service researchers and academic partners from healthcare and health informatic backgrounds ( Table 1 ). Five consultative in-person group meetings and several individual meetings and email exchanges within the group were conducted throughout the review process.

Positions held by the members of the Expert Panel (n = 10).

Note: Some members of the expert panel had more than one position. Health Service Executive (HSE), government-funded organisation responsible for the provision of health and personal social services; UCD, University College Dublin; Ireland East Hospital Group, one of seven hospital groups in Ireland comprising of 11 hospitals and four community healthcare organisations; ICT, Information Communication Technology; Maternal and Newborn Clinical Management System (MN-CMS), an EHR for all women and babies being cared for across maternity and new born services in Ireland; GPIT, General Practice Information Technology; EPR, Electronic Patient Record.

2.3. Research question and search strategy

An initial exploratory scope of the EHR literature in the PubMed database was reviewed by the expert panel and the final research question, methodology and search strategy were developed and agreed. A large number of search terms to describe “Electronic Health Record”, “Implementation” and “ Literature Review” were identified from previous systematic reviews [ 7 , [36] , [37] , [38] , [39] , [40] ], additional literature [ 17 ], medical subject heading and controlled vocabulary and via consultation with the expert panel and an experienced information technologist at the Health Sciences Library, UCD [Appendix]. The search string was tailored to the indexing language of each database and in March 2019, it was executed across PubMed, CINAHL, Scopus, Embase, Web of Science, IEEE Xplore, ACM Digital Library, ProQuest and Cochrane, with limitations of English language and published since 2010. Grey literature including reports and conference proceedings were also searched (international Health Informatics Societies, the World Health Organization (WHO), European e-health network, Kings Fund, Gartner and Lenus). Panellists also drew on their expertise to identify any additional relevant sources [ 35 ].

2.4. Identification of literature reviews

Identified articles were calibrated in the citation management software Endnote version x9.2 and titles and abstracts were screened by one researcher using the inclusion and exclusion criteria agreed with the expert panel ( Table 2 ). Full text articles were then accessed and screened by the same researcher, with any doubts regarding inclusion or exclusion discussed with the panel to overcome any risk of errors or inconsistencies associated with using one reviewer [ 31 ]. In line with our chosen rapid review methodology, a quality assessment of identified reviews was not conducted.

Criteria for inclusion and exclusion of identified literature reviews.

2.5. Data extraction and synthesis

A standardized data extraction form was developed and included authors, year of publication, study design, participants, healthcare setting, included studies and findings related to factors impacting on the implementation (i.e., themes and/or paragraphs as required). Following data extraction, a qualitative content analysis of the factors impacting on the EHR implementation was undertaken by the researcher [ 41 ]. Using an iterative process, a list of codes representing the identified factors from each of the literature reviews was formed [ 42 ]. The expert panel reviewed these codes via an adapted nominal group technique, which saw collated appraisals distributed amongst the panellists [ 43 ] to assess whether they were comprehensive of the literature and their own experiences, and to determine whether the findings could be transferred to Irish contexts and settings [ 42 ]. Having reached a final consensus regarding the factors for a successful EHR implementation, these factors were further categorized into a theoretical framework [ 10 ] and resulted in the generation of key considerations [ 42 ].

3.1. Characteristics of literature reviews

Of the 5,040 articles retrieved, 27 literature reviews were identified which captured factors deemed important for the successful implementation of EHRs, as well as other HIT implementations ( Fig. 1 ). Fifteen were classified as systematic reviews, whilst the others were umbrella reviews (n = 3), scoping reviews (n = 2), interpretive review (n = 1), literature review with a meta-narrative (n = 1) and other non-systematic literature reviews (n = 5). Overlap in included publications existed across the literature reviews with 974 unique studies, literature reviews, reports, books and guidelines identified. Perspectives of a variety of stakeholders were captured in these reviews including GPs (or primary care physicians), other doctors, nurses, health and social care professionals (HCPs), patients, policymakers, vendors and IT consultants ( Table 3 ). Although many literature reviews encompassed studies from a variety of healthcare settings, others were specific to primary care (i.e., community) [ 13 , 44 , 45 ], long term care [ 46 ] and mental health settings [ 47 ] or within specific countries or groups of countries [ 19 , [48] , [49] , [50] , [51] ].

PRISMA Flow Diagram.

Identified literature reviews which reviewed the key factors for a successful EHR implementation.

Note: EHR, Electronic health record; LTC, long-term care; HIT, Health Information Technology; OECD, Organisation for Economic Co-operation and Development; EFTA, European Free Trade Association; HCPs, Health and Social Care Professionals.

3.2. Synthesized findings

Fifteen common factors were identified and classified as organizational, human and technological. Each of these factors are discussed in detail below as well as how they interact within different contexts.

3.2.1. Organizational factors

Factors relating to the processes by which the EHR was introduced and incorporated into routine care were categorized as organizational [ 54 ]. Whilst each of the six factors were important across all contexts, the size and type of organization impacted on how each triggered success during the EHR implementation [ 46 , 53 , 61 ].

3.2.1.1. Governance, leadership and culture

The governance of the EHR implementation [ 13 , 19 , 37 ], as well as leaders [ 7 , 10 , 36 , 44 , 48 , [52] , [53] , [54] , 62 , 63 ] and organizational culture, were identified as paramount in ensuring a successful EHR system [ 7 , 10 , 13 , 36 , 45 , [50] , [51] , [52] , [53] , 56 , 59 , 62 ]. Whilst top-down, middle-out and bottom-up governance structures have been utilised, ongoing political willingness, national policies and some independence at an individual organizational level regarding EHR procurement, development and design, were recommended to promote engagement, usability and interoperability [ 13 , 48 , 51 , 62 ]. It was also important that executive leaders such as CIOs and project management teams establish good and trusting relationships with vendors and consulting firms [ 12 , 44 , 52 , 56 , 63 ], and designed the implementation strategy with clear measurable objectives [ 10 , 50 , 52 ], a fitting implementation process (e.g., big-bang or phased) [ 44 , 46 , 51 , 58 ], and clear roles and divisions of labour [ 10 , 60 ]. A shift away from the dominance of top and middle management has also been recommended [ 10 , 19 , 36 ], with the appointment of local leaders or champions , and supporting of internal and external communication and collaboration [ 10 , 11 , 19 , 52 , 59 ], innovation and continual improvement [ 52 ], and patient-centred care [ 19 ]. This also helps to create a favourable [ 10 , 36 , 44 , 63 ] and flexible [ 52 ] culture.

3.2.1.2. End-user involvement

During each stage of the EHR implementation process, end-user involvement was highlighted as important [ 7 , 10 , 37 , 47 , 48 , 52 , 54 , 56 , 57 , 60 , 62 , 63 ], as it helps to ensure that the EHR meets end-users’ needs and workflows, as well as promoting a sense of ownership [ 37 ] and acceptance amongst staff [ 10 , 37 , 63 ]. Engaging end-users from each stakeholder group was recommended [ 36 ], and this has often been done in the form of appointing champions . These leaders should be respected amongst their colleagues as well as having the relevant knowledge to act as a bridge between the end-users and IT staff [ 60 , 62 , 63 ]. However, champions may sometimes need to be shared between organizations [ 10 ].

3.2.1.3. Training

Basic computer and EHR-specific training were identified as key to a successful EHR implementation [ 7 , 10 , 12 , 13 , 19 , 36 , 37 , 45 , 46 , 48 , [50] , [51] , [52] , [53] , 56 , 57 , 60 , 61 , 63 ]. However, the effectiveness and resource-efficiency of training depended on the appropriateness of the appointed trainers, training content, timing of training (i.e., as close to Go Live as possible [ 36 ]) and methods of training e.g., classroom based versus eLearning [ 57 ]. EHR training was also recommended on an ongoing basis for new staff, as well as existing staff to optimize their use of the system [ 37 , 53 ].

3.2.1.4. Support

Expert, technical, executive and external support have been critical to successful EHR implementations [ 7 , [10] , [11] , [12] , [13] , 19 , 36 , 37 , 44 , [50] , [51] , [52] , [53] , [56] , [57] , [58] , [60] , [61] , [62] , [63] ]. Expert or peer support, often referred to as super-users , reportedly helped end-users to optimize their use of the EHR [ 7 , 11 , 12 , 36 , 53 ], whereas technical support staff helped solve IT issues [ 51 , 62 ]. During Go Live (often first 3-4 weeks [ 37 ]), technical and peer support should be available 24/7 seven days a week in hospitals [ 12 , 36 ]. However, this may not be feasible or required in primary care centres but channels to obtain support during working hours remain important. Other crucial support comes from an executive or policy level [ 19 , 50 , 52 , 53 , 56 , 57 , 60 , 63 ] and professional networks or external parties [ 19 , 53 ]. Although maintenance support for servers and networks was not as evidenced in the identified literature [ 50 ], the expert panel also deemed this as important.

3.2.1.5. Resourcing

The availability of resources in terms of finance, skilled workforce and time was also important [ 7 , 10 , 12 , 13 , 36 , 37 , [44] , [45] , [46] , 48 , 49 , [51] , [52] , [53] , [54] , 56 , [59] , [60] , [61] , [62] , [63] ]. Financial resourcing was often highlighted as a barrier especially by primary care doctors [ 12 , 13 ] and those in lower income countries [ 48 ], and scope creep of the budget was a common occurrence for larger hospitals [ 10 , 52 , 54 ]. Therefore, a cost analysis which encompasses infrastructure, personnel, maintenance and ongoing optimization was critical [ 36 , 62 ]. Having a skilled workforce in-house who understand the clinical workflows was also recommended [ 53 , 61 ] as it can reduce dependence on and cost of vendors [ 12 , 36 ]. However, this may not be feasible for smaller organizations, and larger organizations also reportedly had issues with IT staff retention [ 10 , 13 , 36 , 48 , 51 ]. Adequate time for end-user involvement and habituation to the EHR was also vital [ 7 , 10 , 12 ] to ensure organizational readiness [ 7 , 13 , 51 , 53 ].

3.2.1.6. Workflows

Inability of the EHR system to meet the workflows of end-users and organizations was commonly cited as negatively impacting on success [ 7 , [10] , [11] , [12] , 36 , 37 , 51 , 52 , 54 , 56 , 62 , 63 ], including end-user efficiency, productivity, satisfaction and acceptance of the EHR [ 7 , 11 , 63 ]. Although replicating existing paper-based practices may minimize disruptions for end-users [ 7 , 13 , 19 , 62 ], re-engineering of workflows during digitization to make them safer and more efficient was recommended [ 19 , 62 , 63 ].

3.2.2. Human factors

Ability of healthcare organizations to successfully adopt an EHR system was largely determined by the individual end-users [ 10 , 54 ], and three overarching human factors were identified.

3.2.2.1. Skills and characteristics

IT skills as well as personal characteristics of individuals impacted on the success of an EHR implementation [ 10 , 12 , 50 , 51 , 53 , 56 , 58 , 60 , 62 , 13 , 19 , 36 , 37 , 44 , [47] , [48] , [49] ]. Assessing computer literacy of end-users enabled provision of basic computer training to those requiring it, prior to effective EHR training [ 36 , 48 ]. Whilst the research assessing the impact of age, gender and clinical experience on acceptance of the EHR reported in the identified reviews was inconclusive, personal traits such as being open-to-change and a problem-solver appeared to contribute to success [ 56 , 62 ]. However, resistance to embracing the EHR could also be attributed to unusable technology [ 10 , 51 ].

3.2.2.2. Perceived benefits and incentives

Where individual end-users perceived the EHR to positively impact on patient care and workload, this reportedly facilitated a successful implementation [ 10 , 12 , 50 , 51 , 56 , 58 , 60 , 13 , 19 , 36 , 37 , 44 , [47] , [48] , [49] ]. However, realistic benefits and timeframes specific to the organization should be communicated with end-users [ 44 , 45 , 62 ]. Monetary incentives or penalties have also been shown to be important, especially for privately-governed organizations [ 13 , 45 , 59 ].

3.2.2.3. Perceived changes to the healthcare ecosystem

End-users’ concerns with changes to data privacy and security, patient-clinician relationships and their roles and responsibilities, appeared to negatively impact on EHR implementations [ 7 , 10 , 51 , 53 , 56 , 58 , [60] , [61] , [62] , 12 , 13 , 19 , 36 , 44 , [47] , [48] , [49] ]. These concerns may differ depending on the specific setting and type of sensitive personal information being collected (e.g., mental health) [ 47 ]. Therefore, specific concerns and their causes of concerns should be identified and addressed as soon as possible to mitigate their impact on EHR implementations [ 19 , 36 ].

3.2.3. Technological factors

Six factors relating to the technology aspect of the EHR implementation were identified as critical to its success and were intrinsically linked to the organizational and human factors.

3.2.3.1. Usability

EHR usability was deemed important across several reviews [ 7 , 10 , 11 , 13 , 36 , 37 , 44 , 46 , 47 , 49 , 51 , 52 , 54 , 58 , 60 , 62 ], as it impacted on end-user efficiency, patient-facing time [ 12 , 13 , 37 , 53 ], quality of care [ 12 ], patient-clinician relationships [ 52 ] and safety [ 37 ]. However, a simple and intuitive system in one setting may not be transferrable to another, and therefore, end-user involvement in development, design [ 10 , 37 , 62 ] and usability testing were recommended at each site [ 37 ]. Additionally, enabling personalization of the EHR interface [ 53 ] and access to legacy paper-based records [ 50 , 51 ] as well as consideration of data quality and accuracy [ 13 , 44 , 51 ] with use of health terminologies and classifications [ 56 ] was recommended. However, usability needs to be balanced with security [ 44 ].

3.2.3.2. Interoperability

To enable health information exchange both within and across healthcare organizations, interoperability was identified as critical [ 7 , [10] , [11] , [12] , [13] , 19 , 37 , 44 , 45 , [49] , [50] , [51] , [52] , 54 , 58 , 60 , 62 ]. Local contextual factors within countries such as two tier and fully private health systems, lack of employment of national standards [ 45 , 53 , 62 ], inconsistent data capture in incompatible formats [ 12 ], have rendered the creation of a fully interoperable EHR as difficult. Therefore, technical standards and communication between organizations were recommended to ensure interoperability was built in from the outset including for legacy and existing health IT systems [ 7 ].

3.2.3.3. Infrastructure

Procurement or enhancement of infrastructure, including software (e.g., EHR, anti-viral), hardware (e.g., data-entry devices, Wi-Fi, power outlets) and furniture, accounted for a large proportion of the financial resourcing and were deemed critical for the success of the overall EHR implementation [ 10 , 12 , 56 , 62 , 63 , 36 , [47] , [48] , [49] , [50] , [51] , [52] , [53] ]. The existing and new hardware and software must be compatible with the specific EHR product 45 , reliable and functional [ 13 , 36 , 44 , 53 , 56 ], and enable sufficient accessibility to the EHR for end-users [ 36 , 45 , 52 , 56 ]. According to the expert panel and additional literature reviewed, selection of mobile and stationary data-entry devices also require consideration of vendor certification, healthcare setting (e.g., outpatients versus isolation rooms), required functions and workflows (e.g., checklists versus long narrative notes), and end-user preferences for usability.

3.2.3.4. Regulation, standards and policies

As stated earlier, national and international standards as well as regulation and policies were critical for interoperability and addressing privacy and security concerns [ 7 , 13 , 19 , 45 , 46 , 51 , 52 , 56 , 58 , 60 , 62 , 63 ]. Therefore, messaging and language standards [ 45 , 52 , 56 ], as well as robust privacy laws and policies [ 13 , 44 , 52 , 56 , 62 ] were recommended. Where healthcare organizations were permitted to procure their own EHR product, these standards would likely be especially important.

3.2.3.5. Adaptability

Many of the literature reviews reported that adaptability of the software was important to facilitate customization of the EHR software to meet the needs of the end-users and organizations [ 10 , 36 , 37 , 50 , 51 , 53 , 54 , 62 ]. This reportedly required the software vendors to be open to sharing code development data and willing to adapt their product [ 36 , 37 , 53 ], and the organization to have access to a skilled workforce with the capabilities to adapt the EHR to clinical workflows [ 37 ]. Where interoperability standards exist, the need for adaptations to the software may be reduced [ 37 ].

3.2.3.6. Testing

Comprehensive testing of the system was critical to ensure usability and safety [ 7 , 10 , 37 , 54 ], and was more commonly cited as important by IT staff and management than by HCPs [ 7 ]. This rigorous, resource-intensive, multi-step testing process of each EHR function needed to be conducted within live environments with actual end-users [ 54 ] and should not be underestimated.

4. Discussion

This umbrella review distilled the large volume of evidence available regarding the successful implementation of a national EHR and these findings were corroborated by an expert panel as being relevant to the Irish healthcare context. Fifteen key organizational, human and technological factors were identified as critical and by synthesizing the findings from several stakeholder groups and clinical settings, such as doctors in primary or secondary care [ 11 , 13 , 45 , 53 , 58 , 61 ] and nurses in a mental health setting [ 47 ], this review of reviews identified that each of these factors were also relevant and important to EHR and other HIT implementations across different healthcare contexts.

However, between country differences including health service management, politics, economics, regulation and socio-culture impact on how the identified factors influence success. This was evident in the literature reviews which largely focused on studies conducted in the predominantly private health service in the USA where return on investment and productivity were perceived benefits and incentives of EHRs or EMRs [ 50 , 51 , 56 ]. Additionally whilst the governance approach was identified as important, a successful approach in one country cannot necessarily be replicated in another, as occurred in the UK where the top-down approach successfully employed in the Netherlands resulted in disengaged healthcare organizations across the UK [ 22 ]. Therefore, these factors need to be employed with consideration of the national context and in the Republic of Ireland this will also require close collaboration and communication across the co-existing public and private health sectors [ 64 , 65 ], as well as with those in Northern Ireland (UK). Additionally, European Union (EU) citizens may avail of healthcare from any member state under the Cross-Border Healthcare Directive (2011/24/EU) and thus, efficient exchange of health data across borders is a major priority [ 66 ] and is a pillar of EU4Health 2021-2027 [ 67 ]. Therefore, the EU interoperability policies and frameworks [ 14 ] as well as standards such as the International Patient Summary, the General Data Protection Regulation (GDPR) and standardised terminologies [ 4 ] to support these frameworks need to be employed.

Despite the expansion in internationally-recognised standards (e.g., HL7 FHIR) and significant regulatory and financial incentives created by the HITECH Act and “Meaningful Use” requirements in the USA, factors such as Usability and Regulations, standards and policies continue to be highlighted as important for success as opposed to being assumed components of EHR products. Whilst the inclusion of older studies by these reviews perhaps contributed to this, it is also likely that standards and requirements alone will not ensure an interoperable and usable EHR. In fact, it is the dynamic interaction between each of the identified factors which promotes a successful EHR [ 68 ]. However, placing more emphasis on an individual factor can reduce the resources required for others. For example, promoting Usability and Standards can respectively reduce the burden of training and support, as well as adaptability [ 37 ]. Additionally, this may be achieved by advances in evidence and technology such as artificial intelligence (AI) including automated testing [ 69 ], eLearning modules [ 70 , 71 ], and personalization of the EHR interface [ 72 ]. Therefore, it is recommended that those involved in each aspect of the implementation process communicate throughout it and review the latest evidence regarding technology including peer-reviewed publications and white papers.

At a more local or meso level, the size of the organization, infrastructure, organizational readiness and culture, capabilities and beliefs of the workforce, and available finance [ 36 , 37 ], were also identified as important when considering the application of the identified factors. Certain aspects of the internal context can also be enhanced to improve the likelihood of EHR success such as employing change management to create a clear and realistic vision of the EHR [ 73 ] and providing basic computer training [ 36 , 48 ]. However, the size of the organization and its workforce will likely remain more limited compared to their larger counterparts [ 10 , 37 ]. Therefore, sharing of resources such as champions, support staff and trainers between larger and smaller hospitals or primary care settings has been recommended, with some countries creating networks or encouraging collaboration between existing regional groups of healthcare organizations [ 73 , 74 ].

4.1. Strengths and Limitations

Undertaking a rapid qualitative evidence synthesis requires acceleration of many of the research processes, is dependent on the reporting in the original reviews [ 32 ] and could risk losing the context and complexity of the original research setting [ 32 , 42 , 75 ]. Additionally, five of the literature reviews were conducted by the same lead author which could lead to bias of individual study inclusion. However, the inclusion of literature reviews, consideration of the inclusion criteria of each literature review and ongoing collaboration with an expert panel [ 30 ], provided a degree of confidence regarding the coherence, relevance and adequacy of the findings and their generalisability across healthcare settings [ 76 ]. Additionally, actively involving knowledge-users who were undertaking HIT implementations led to the concurrent translation of this knowledge into practice [ 77 ].

5. Conclusion

The key organizational, human and technological factors identified in this review provide policy-makers and other key stakeholders with a foundation for making evidence-based decisions during the implementation of a fully interoperable EHR across primary, secondary and long-term care. However, consideration of the specific contextual influences is critical to the successful application of these factors. Additionally, the end-users, existing technological standards and policies, and advances in technology and research in the area, will impact on how these factors dynamically interact during the EHR implementation and will influence success.

Summary points

What was already known on the topic:

• • Despite recognition of the huge potential for EHRs to improve the delivery of healthcare, huge challenges have been met in implementing a fully interoperable EHR across acute and community care.
• • The implementation process of EHRs is critical to their success and needs to be carefully planned and considered across the complex and adapting healthcare landscape.
• • A vast amount of literature exists on EHRs which has been relevant to specific stakeholder groups and healthcare contexts.

What this study adds:

• • A comprehensive and clear overview of factors influencing the success of an EHR implementation across primary, secondary and long term care and different stakeholder groups is presented.
• • Validation of these factors for the Irish healthcare context via co-production and transfer of knowledge with key knowledge-users.
• • Generation of key considerations for each of these factors for policy-makers and other knowledge-users.

This work was supported by the Office of the Nursing and Midwifery Services Director, Health Service Executive (HSE), Ireland.

Declaration of Competing Interest

The authors report no declarations of interest.

Acknowledgements

The panel of experts and knowledge users who gave their time and expertise as well as other contributors from the HSE.

Appendix A. Search Strategy

• Open access
• Published: 19 July 2023

Health professionals’ readiness to implement electronic medical record system in Gamo zone public hospitals, southern Ethiopia: an institution based cross-sectional study

• Samuel Hailegebreal 1 , 2 ,
• Temesgen Dileba 2 ,
• Yosef Haile 2 &
• Sintayehu Abebe 2  

BMC Health Services Research volume  23 , Article number:  773 ( 2023 ) Cite this article

1718 Accesses

1 Altmetric

Metrics details

The adoption of Electronic Medical Records (EMR) by the healthcare sector can improve patient care and safety, facilitate structured research, and effectively plan, monitor, and assess disease. EMR adoptions in low-income countries like Ethiopia were delayed and failing more frequently, despite their critical necessity. The most popular way to solve the issue is to evaluate user preparedness prior to the adoption of EMR. However, little is known regarding the EMR readiness of healthcare professionals in this study setting. Therefore, the objective of this study was to assess the readiness and factors associated with health professional readiness toward EMR in Gamo Zone, Ethiopia.

An institution-based cross-sectional survey was conducted by using a pretested self-administered questionnaire on 416 study participants at public hospital hospitals in southern Ethiopia. STAT version 14 software was used to conduct the analysis after the data was entered using Epi-data version 3.2. A binary logistic regression model was fitted to identify factors associated with readiness. Finally, the results were interpreted using an adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p-value less than 0.05.

A total of 400 participants enrolled in the study, with a response rate of 97.1%. A total of 65.25% (n = 261) [95% CI: 0.60, 0.69] participants had overall readiness, 68.75% (n = 275) [95% CI: 0.64, 0.73] had engagement readiness, and (69.75%) (n = 279) [95% CI: 0.65, 0.74] had core EMR readiness. Computer skills (AOR: 3.06; 95% CI: 1.49–6.29), EMR training (AOR: 2.00; 95% CI: 1.06–3.67), good EMR knowledge (AOR: 2.021; 95% CI: 1.19–3.39), and favorable attitude (AOR: 3.00; 95% CI: 1.76–4.97) were factors significantly associated with EMR readiness.

Although it was deemed insufficient, more than half of the respondents indicated a satisfactory level of overall readiness for the adoption of EMR. Moreover, having computer skills, having EMR training, good EMR knowledge, and favorable EMR attitude were all significantly and positively related to EMR readiness.

Peer Review reports

Introduction

E-health is defined by the World Health Organization (WHO) as the cost-effective application of information and communication technologies (ICT) to assist health and health-related disciplines [ 1 , 2 ]. The primary issues facing healthcare systems can be greatly addressed by EMR. The delivery of healthcare services to patients is supported by the use of an electronic medical record (EMR), which is a computerized medical record used to collect, store, and share data among healthcare professionals in an organization.

Although EMRs are a vital tool for the health sector, their implementation, uptake, and utilization are still low in developing nations. Many healthcare facilities throughout the world have deployed EMR systems to enhance the process of capturing patient data, but only a select fraction of them have proven successful [ 3 , 4 ]. EMRs are computerized medical information systems that gather, store, and display patient data. While maintaining the patient’s privacy and security, it may include a variety of data such as socio-demographics, insurance, past and present medication information, allergies, laboratory and test results, histories of immunizations and medical procedures, hospitalizations, progress evaluations, and others [ 5 , 6 , 7 ].

E-Health Readiness is the term used to describe how ready healthcare organizations or communities are for the anticipated change brought about by ICT-related activities. E-readiness is the capacity of an organization to foster and support the development of ICTs, including infrastructure, pertinent systems, and technical competencies [ 8 ]. EMR enhances patient care by establishing connections among all caregivers, lowering the demand for file space and supplies, and eliminating the need for staff to physically access any records [ 9 ]. However, in many developing countries the EMR system is not widely scattered or implemented [ 10 , 11 , 12 , 13 ].

Failure to implement EMRs has a negative impact on patients’ and healthcare professionals’ ability to access medical history, treatment information, and past diagnoses, which slows down the workflow of healthcare organizations [ 14 ]. The development of a national electronic health record is now underway in Ethiopia, while other EMR pilot programmes have been implemented in our country [ 15 , 16 ]. Previous finding revealed that the proportion of EMR readiness varied across Ethiopia, with 36.5% [ 17 ] in the Sidama region, 52.8% [ 18 ] in the southwest Ethiopia, and 54.1% [ 2 ] in the northern Ethiopia.

Prior research suggested that readiness was related to age, gender, profession, level of education, and work experience [ 15 , 19 , 20 , 21 , 22 ]. Moreover, having knowledge, computer skills, prior EMR experience, and a personal computer are associated to professional readiness [ 13 , 23 , 24 , 25 , 26 , 27 ]. Previous research revealed that readiness for the adoption of EMR can be influenced by health professionals working in organizations with IT infrastructure and access to computers [ 15 , 28 ]. Our literature search revealed that more studies have focused on organizational readiness than on the health professionals’ readiness [ 29 ], and no research has been done in the setting of our study.

Before implementing such systems in Ethiopia, the researchers of this study felt that it was important to assess the user’s readiness and the essential metrics. In environments with limited resources, this study also made it possible for policymakers to comprehend user needs prior to system deployment. Therefore, the objective this study was to assess readiness of health professionals towards electronic medical record system and its associated factors in public hospitals in Gamo zone, Ethiopia.

Study design, setting, and period

From September to October 2022, a cross-sectional institution-based survey was undertaken among healthcare professionals working at public health institutions in the Gamo zone. Gamo zone shares a border with Wolayta and Gofa zones in the north, Lake Abaya to the northeast, Amaro and Dirashe special woredas to the southeast, and South Omo to the southwest. The administrative center of the Gamo zone is Arba Minch town. Arba Minch settlement lays 505 km (km) southwest of Addis Ababa, the Ethiopian capital, and 275 km (km) southwest of Hawassa, the regional headquarters of southern Ethiopia. It hosted seven hospitals (one general and six primary hospitals), 56 health centers, and 299 health posts, which serve the community by providing preventive and curative services [ 30 ].

Study population, sample size and sampling procedure

All healthcare workers who were employed full-time in the Gamo zones of southern Ethiopia were eligible for this study. Health professionals working in Gamo zone hospitals (Arba Minch, Geress, Selamber, Chencha, and Kemba) were study population. The sample size was calculated using data from a study carried out in Ethiopia, which showed that 52.8% of medical professionals had EHR readiness levels comparable to those used in the current study [ 18 ]. We also take into account the following assumptions: a non-response rate of 10%, a margin of error of 5%, and a confidence level of 95%. Finally, the study’s sample size was 416 health professionals. We were proportionally allocated the total sample size, 416, to those five public hospitals found in the zones. Then, in those five hospitals, random selections of healthcare professionals were executed (Fig.  1 ).

Sampling procedure on EMR readiness among health professionals in Gamo zone, Ethiopia, 2022

Data collection technique and data quality assurance

A pretested self-administered questionnaire was used to collect the data. The questionnaire had questions about socio-demographics, behavior, technology, and organizations. The survey was written in English because the study’s participants are well-educated and capable of understanding it. Cronbach’s alpha coefficient was also used to test reliability (the overall Cronbach alpha for healthcare professionals readiness was 0.87, knowledge was 0.81 and attitude was 0.89). For two days, the investigators trained the supervisors and data collectors on the purpose of the study, data gathering techniques, data collection tools, respondents’ approach, data confidentiality, and respondent’s rights. Five health information technicians with strong communication skills were used for data collection, and two master’s-educated health professionals served as supervisors. The supervisors checked the questionnaire’s accuracy every day. Before the analysis, data cleaning and cross-checking were also performed. Following any necessary questionnaire adjustments, the actual data collection process began.

Operational definition

Core readiness.

Core readiness was assessed in this study using a series of four questions, with participants scoring 50% or higher being assumed to have core readiness and participants scoring less than 50% being believed to not have core readiness [ 18 ].

Engagement readiness

Engagement readiness is the active willingness and participation of individuals in the deployment of the electronic medical record (EMR). In this study, participants’ levels of engagement readiness was assessed by a series of four questions. Participants with scores of 50% or more were considered to be engaged, while those with scores averaging less than 50% were considered not to be so [ 31 ].

Overall readiness

Health professionals were classified as having an overall readiness if they met both the core and engagement readiness criteria [ 24 ].

Eight questions were used to examine if a person possesses the fundamental understanding of EMR, and knowledge is measured as a variable. Professionals with knowledge scores of 50% or higher were considered to have good knowledge [ 23 ].

Attitude was measured as a latent variable of a set of fifteen questions that assesses the individual perception of EMR measured on a five point Likert scale. A score of mean or above was used to classify as having a favorable attitude [ 32 ].

Data processing and analysis

Data from the survey were entered using Epi-data version 3.1, coded by using alpha-numeric symbols, and analyzed using STATA version 14 software. To describe demographic characteristics, attitudes, and readiness for EMR, descriptive analyses were conducted. Moreover, the binary logistic regression method was used to identify the independent factors related to readiness. The variance inflation factor was used to test for multicollinearity (VIF). Hosmer-Lemeshow tests were used to assess the model’s goodness of fit at P-value > 0.05. In multi-variable logistic regression analysis, odds ratio was used to examine the strength of association between factor and outcome variables and 95% CI and P-value < 0.05 were computed to assess statistical significance.

Socio demographic characteristics of study participant

Of the total 416 participants, 400 returned the questionnaire with a response rate of 97.1%.In this study, majority of the participants (53.3%) were within the age category 20–30 and 62% of the participants were male professionals. About 131 (28.25%) respondents were nurses, 72(18%) were doctors, 66(16.50%) were public health, 51(12.75%) were midwives and 69(17.25%) were other heath professional. Of the respondents, 322 (80.50%) have a bachelor degree while 36(9%) health professionals had a Master’s degree and above. About 184 (46%) of the study participants had more than five years of professional experience, while 29 (7.25%) had less than two years. Moreover, 288(72%) have worked at the hospital where they are currently employed (Table 1 ).

Organizational and technical factors

Majority (65%) of respondents have a personal computer at home and 337 (84.25%) of the participants had computer related skills. Only 109 (27.25%) of the study’s participants had prior EMR training, and only 616 (41%) had prior EMR system experience. More than half of (52%) the health professionals had access to a computer in their workplace; of these, 63.37% participant used for data recording, 20.30% for report writing, 10.89% for reading, and 5.45% for other purposes like video accessing. Moreover, more than half of the respondents reported that their hospitals have an IT technician and have a functioning IT department (Table 2 ).

Knowledge and attitude of health professionals for EMR

In this study the majority of respondents 259 (64.75%) had good knowledge of EMR. Likewise, 229 (57.25%) respondents had a favorable opinion of the EMR system (Fig. 2 ).

EMR knowledge and attitude towards EMR among health professionals in Gamo zone, Ethiopia, 2022

Readiness of health professional to EMR

Out of all participants, 65.25% [95% CI: 0.60, 0.69] had overall readiness for EMR, while 68.75% [95% CI: 0.64, 0.73] had engagement readiness for EMR and 69.75% [95% CI: 0.65, 0.74] had core readiness (Fig.  3 ).

Health professional readiness to implement EMR in public hospitals in Gamo zone, Ethiopia, 2022

Factors associated with health professional readiness to EMR

In this study, four factors were shown to be significantly associated with EMR readiness in the multivariable logistic regression model: computer skills, EMR training, EMR knowledge, and EMR attitude.

Health care professionals with computer skills were 3 times (AOR: 3.06; 95% CI: 1.49–6.29) more likely to be ready to use an EMR system than their counterparts. Health professionals who had taken EMR training were about 2 times (AOR: 2.00; 95% CI: 1.06–3.67) more likely ready to use EMR system than those who had not. Health professionals with good EMR knowledge had 2times (AOR: 2.021; 95% CI: 1.19–3.39) higher odds of being ready than those with poor knowledge. Moreover, it was revealed that professionals with a favorable attitude had 3 times (AOR: 3.00; 95% CI: 1.76–4.97) more likely ready than those with unfavorable attitude (Table 3 ).

This study examined the EMR readiness of healthcare professionals and related variables in Southern Ethiopia. Despite the fact that there are several levels of EMR readiness, the authors of this study emphasized on healthcare providers’ levels of readiness. In this study, the overall readiness of health professionals for an EMR system was 65.25%.This finding was similar with study done in Ethiopia (62.3%) [ 23 ]. This finding was higher than study conducted in Southwest Ethiopia (52.8%) [ 18 ], Sidama region (36.5%) [ 32 ], Ghana (54.9%) [ 24 ], Myanmar (54.2%) [ 33 ]. The discrepancy may be attributable to the development and extension of Technological infrastructure, as well as the Ethiopian government’s priority placement of the digitization of the health information system in its ambition to modernize the health sector [ 34 , 35 ]. The regular interaction of health workers with the global digital world may also be a factor. Moreover, the method used to categorize professional readiness, variations in sample size, or variations in socio-demographic factors could all contribute to this disparity [ 36 ].

In a multivariable logistic regression model, it was revealed that computer skills, EMR training, EMR knowledge, and EMR attitude were all significantly and positively related to EMR readiness. In this study, Health care professionals with computer skills were more likely to be ready to use an EMR system than their counterparts. This finding is supported by previous report in Ethiopia [ 2 , 18 , 37 ], Greek [ 38 ], Saudi Arabia [ 39 ]. This could be due to the fact that, if used for everyday healthcare management, people with computer capabilities won’t have as much trouble using the EMR system. Additionally, the availability of computers and computer skills may have had a direct impact on health professionals’ perceptions on the usage of computer-based systems. Regarding health professionals who had taken EMR training were about more likely ready for an EMR system as compared to those health professionals who had not taken any EMR training before. This finding was in line with previous studies in Ethiopia [ 23 , 40 ]. The findings indicating that education and training typically alter people’s perspectives and thoughts may help to explain this.

In this study, health professionals with good knowledge were more likely to be ready for an EMR system than those with poor knowledge. This result was consistent with findings from various studies in Greek [ 38 ], Lebanon [ 41 ], Australia [ 42 ]. This may be explained by the fact that professionals who recognize the benefits of electronic medical record systems could be more encouraged to employ them as a result of their awareness. Because of their tendency to do so, they may also be more prepared to accept technology advantages and be ready to adopt EMR systems.

Moreover, health professionals who had favorable attitude about electronic medical record systems were more likely ready to EMR system than unfavorable attitude towards the system. This may be explained by the fact that experts may be more motivated to use the system if they have a favorable opinion of it and show a keen interest in it. According to earlier research, health professionals’ attitudes influence both their readiness and how well they use the system in Ethiopia [ 23 ], Saudi Arabia [ 39 ], USA [ 43 ]. Furthermore, this could be due to the fact that healthcare providers have a positive opinion of those technologies, which motivates them to be more enthusiastic and dedicated towards using EMR [ 18 , 44 ].

Strength and limitation

The strength of this study was the first in its field to analyze the precise measurements that must be made to raise healthcare providers’ readiness levels prior to the deployment of EMR. In low-income nation settings, it also highlighted several key measurements that must be made before ERM implementation. The study was cross-sectional, thus causality cannot be concluded. The study’s primary shortcoming was the lack of triangulation with qualitative results. Additionally, it didn’t include other forms of ready, such as organizational and technological readiness, because there isn’t a tool that can compress all forms of ERM health preparedness.

Although it was deemed insufficient, more than half of the respondents indicated an acceptable level of overall readiness for the adoption of EMR. According to this study, having computer skills, having EMR training, good EMR knowledge, and favorable EMR attitude were all significantly and positively related to EMR readiness.

Prior to implementation, health personnel should receive training to raise their level of EMR understanding. Capacity building and awareness creation activities should also be made in this regard. Given that it would improve professional abilities and make them feel more competent and ready to use the system, it stands to reason that this could also alter health professionals’ attitudes.

Implications of the study

Future deployments of digital health systems could be affected by this study. Increasing computer accessibility, providing an EMR training course, and promoting a positive attitude towards EMR are potential strategies to boost the success rate of EMR implementations in Ethiopia. The electronic medical record will be implemented and customized in the study environment using the findings of the study as a foundation. The main goal was to increase the success of EMR implementation by determining the readiness of healthcare professionals.

Data Availability

Full data set and materials pertaining to this study can be obtained from corresponding author on reasonable request.

Abbreviations

Adjusted Odds Ratio

Confidence Interval

Electronic Health Record

Electronic Medical Record

Information Communication Technology

Information Technology

Qureshi Q, Ahmad I, Nawaz A. Readiness for e-health in the developing countries like Pakistan. Gomal J Med Sci. 2012;10(1):160–3.

Google Scholar  

Biruk S, Yilma T, Andualem M, Tilahun B. Health Professionals’ readiness to implement electronic medical record system at three hospitals in Ethiopia: a cross sectional study. BMC Med Inform Decis Mak [Internet]. 2014;14(1):115. Available from: https://doi.org/10.1186/s12911-014-0115-5 .

World Health Organization. Atlas of eHealth country profiles. The use of eHealth in support of universal health coverage. WHO, Geneva [Internet]. 2016;392. Available from: http://scholar.google.com/scholar?hl=en& btnG=Search&q=intitle:Atlas+of+eHealth+Country+Profiles#0.

Tubaishat A. Perceived usefulness and perceived ease of use of electronic health records among nurses: Application of Technology Acceptance Model. Informatics Heal Soc Care [Internet]. 2018 Oct 2;43(4):379–89. Available from: https://doi.org/10.1080/17538157.2017.1363761 .

Ariffin NAbt, Ismail N, bt A, Kadir IKA, Kamal JIA. Implementation of Electronic Medical Records in developing countries: Challenges & Barriers. Int J Acad Res Progress Educ Dev. 2018;7(3):187–99.

Janett RS, Yeracaris PP. Electronic medical records in the american health system: Challenges and lessons learned. Cienc e Saude Coletiva. 2020;25(4):1293–304.

Article   Google Scholar  

Lakbala P, Dindarloo K. Physicians’ perception and attitude toward electronic medical record. Springerplus. 2014;3(1):1–8.

Dugar D. https://www.selecthub.com/medical-software/how-to-conduct-ehr-readiness-assessment/ .

Gesulga JM, Berjame A, Moquiala KS, Galido A. Barriers to Electronic Health Record System Implementation and Information Systems Resources: A Structured Review. Procedia Comput Sci [Internet]. 2017;124:544–51. Available from: https://doi.org/10.1016/j.procs.2017.12.188 .

Albagmi S. The effectiveness of EMR implementation regarding reducing documentation errors and waiting time for patients in outpatient clinics: a systematic review. F1000Research. 2021;10:514.

Article   PubMed   PubMed Central   Google Scholar  

Noraziani K, Nurul’Ain A, Azhim MZ, Ekhab S, Drak B, Sharifa Ezat WP, et al. An overview of electronic medical record implementation in healthcare system: lesson to learn. World Appl Sci J. 2013;25(2):323–32.

Graves T. A manual for developing countries. Community Eye Health. 2002;15(44):64–4.

PubMed   PubMed Central   Google Scholar  

Berihun B, Atnafu DD, Sitotaw G. Willingness to Use Electronic Medical Record (EMR) System in Healthcare Facilities of Bahir Dar City, Northwest Ethiopia. 2020;2020.

Ahmed MH, Bogale AD, Tilahun B, Kalayou MH. Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019 : using unified theory of acceptance and use technology 2 (UTAUT2) model. 2020;1–11.

Yehualashet G, Andualem M, Tilahun B. The attitude towards and use of Electronic Medical Record System by Health Professionals at a Referral Hospital in Northern Ethiopia: cross-sectional study. J Heal Inf Afr. 2015;3(1):19–29.

Taye G, Ayele W, Biruk E, Tassew B. The Ethiopian Health Information System: where are we? And where are we going? Ethiop J Heal Dev. 2021;35(1):1–4.

Abore KW, Debiso AT, Birhanu BE, Bua BZ, Negeri KG. Health professionals’ readiness to implement electronic medical recording system and associated factors in public general hospitals of Sidama region, Ethiopia. PLoS One [Internet]. 2022;17(10 October):1–12. Available from: https://doi.org/10.1371/journal.pone.0276371 .

Ngusie HS, Kassie SY, Chereka AA, Enyew EB. Healthcare providers ’ readiness for electronic health record adoption: a cross – sectional study during pre – implementation phase. BMC Health Serv Res. 2022;1–12.

Muthee V, Bochner AF, Kang’a S, Owiso G, Akhwale W, Wanyee S et al. Site readiness assessment preceding the implementation of a HIV care and treatment electronic medical record system in Kenya. Int J Med Inform [Internet]. 2018;109(February 2017):23–9. Available from: https://doi.org/10.1016/j.ijmedinf.2017.10.019 .

Gesulga JM, Berjame A, Moquiala KS, Galido A. Barriers to Electronic Health Record System Implementation and Information Systems Resources: A Structured Review. Procedia Comput Sci [Internet]. 2017;124:544–51. Available from: https://www.sciencedirect.com/science/article/pii/S1877050917329563 .

Faris AAzmiSAl, Saleh KA, Al Ojayan OA. https://vlibrary.emro.who.int/imemr/physicians-perceptions-about-the-newly-implemented-electronic-medical-records-systems-at-the-primary-health-care-centers-in-kuwait-2/ .

Dimitrovski T, Bath PA, Ketikidis P, Lazuras L. Factors affecting general practitioners’ readiness to accept and use an electronic health record system in the republic of north macedonia: a national survey of general practitioners. JMIR Med Informatics. 2021;9(4).

Awol SM, Birhanu AY, Mekonnen ZA, Gashu KD, Shiferaw AM, Endehabtu BF, et al. Health professionals’ readiness and its associated factors to implement electronic medical record system in four selected primary hospitals in Ethiopia. Adv Med Educ Pract. 2020;11:147–54.

Abdulai AF, Adam F. Health providers’ readiness for electronic health records adoption: A cross-sectional study of two hospitals in northern Ghana. PLoS One [Internet]. 2020;15(6):1–11. Available from: https://doi.org/10.1371/journal.pone.0231569 .

Ghazisaeedi M, Mohammadzadeh N, Safdari R, Sharifian R, Rahimi A. Evaluation of Hospital Information System (HIS) in General Hospitals: user perspectives PhD, faculty member of Health Information Management Tehran University of Medical Sciences, Iran PhD student of. Health Inform Manage Tehran Univ of. 2013;10:660–3.

Ghazisaeidi M, Ahmadi M, Sadoughi F, Safdari R. A Roadmap to pre-implementation of electronic health record: the key step to success. Acta Inf Med. 2014;22(2):133–8.

Cucciniello M, Lapsley I, Nasi G, Pagliari C. Understanding key factors affecting electronic medical record implementation: a sociotechnical approach. BMC Health Serv Res [Internet]. 2015;15(1):268. Available from: https://doi.org/10.1186/s12913-015-0928-7 .

Ghazisaeidi M, Ahmadi M, Sadoughi F, Safdari R. An assessment of readiness for pre-implementation of electronic health record in Iran: a practical approach to implementation in general and teaching hospitals. Acta Med Iran. 2014;52(7):532–44.

PubMed   Google Scholar  

Yilma TM, Tilahun B, Mamuye A, Kerie H, Nurhussien F, Zemen E et al. Organizational and health professional readiness for the implementation of electronic medical record system: an implication for the current EMR implementation in northwest Ethiopia. 2023;1–7.

Sector H. Ethiopia Emergency type: multiple events reporting period : 1–29 February 2020 • Ethiopia ’ s Minister of Health declared a confirmed COVID-19 outbreak on 13 March 2020. Since then a total of 11 cases with 261 contacts have been • A yellow fever outbrea. 2020;(February).

Id JKK, Keizer N, De, Id RC. Assessment of organizational readiness to implement an electronic health record system in a low-resource settings cancer hospital: A. 2020;1–17.

Weldeab K, Id A, Tamiso A, Id D, Birhanu BE. Health professionals ’ readiness to implement electronic medical recording system and associated factors in public general hospitals of Sidama region. Ethiopia. 2022;201:1–12.

Min H, Id O, Minn Y, Id H, Win TT, Han ZM et al. Information and communication technology literacy, knowledge and readiness for electronic medical record system adoption among health professionals in a tertiary hospital, Myanmar : a cross-sectional study. 2021;1–15.

Tegegne MD, Wubante SM. Identifying barriers to the adoption of Information Communication Technology in Ethiopian Healthcare Systems. A systematic review. Adv Med Educ Pract. 2022;13:821–8.

Mondejar ME, Avtar R, Diaz HLB, Dubey RK, Esteban J, Gómez-Morales A et al. Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet. Sci Total Environ [Internet]. 2021;794:148539. Available from: https://www.sciencedirect.com/science/article/pii/S0048969721036111 .

Nölke L, Mensing M, Krämer A, Hornberg C. Sociodemographic and health- (care-) related characteristics of online health information seekers: a cross-sectional german study. 2015;1–12.

Walle AD, Shibabaw AA, Tilahun kefyalew N, Atinafu WT, Adem JB, Demsash AW et al. Readiness to use electronic medical record systems and its associated factors among health care professionals in Ethiopia: A systematic review and meta-analysis. Informatics Med Unlocked [Internet]. 2023;36:101140. Available from: https://www.sciencedirect.com/science/article/pii/S2352914822002775 .

Melas CD, Zampetakis LA, Dimopoulou A, Moustakis VS. An empirical investigation of Technology readiness among medical staff based in greek hospitals. Eur J Inf Syst. 2014 Nov;23(6):672–90.

Aldebasi B, Alhassan AI, Al-nasser S, Abolfotouh MA. Level of awareness of saudi medical students of the internet-based health- related information seeking and developing to support health services. 2020;8:1–8.

Kalayou MH, Endehabtu BF, Guadie HA, Abebaw Z, Dessie K, Awol SM et al. Physicians ’ Attitude towards Electronic Medical Record Systems: An Input for Future Implementers. 2021;2021.

Saleh S, Khodor R, Alameddine M, Baroud M. Readiness of healthcare providers for eHealth: the case from primary healthcare centers in Lebanon. BMC Health Serv Res. 2016 Nov;16(1):644.

Yusif S, Hafeez-Baig A, Soar J. e-Health readiness assessment factors and measuring tools: a systematic review. Int J Med Inform. 2017;107(June):56–64.

Article   PubMed   Google Scholar  

Mason P, Monestime JP. Overcoming barriers to Implementing Electronic Health Records in Rural Primary. Care Clin. 2017;22(11):2955.

Ghazisaeidi M, Ahmadi M, Sadoughi F. An Assessment of Readiness for Pre-Implementation of Electronic Health Record in Iran: a Practical Approach to Implementation in general and Teaching Hospitals. 2013.

Download references

Acknowledgements

The authors are indebted to the Arba Minch University College of health science ethical review board for the approval of ethical clearance and each hospital for giving a supporting letter. The authors would like to extend their heartfelt thanks to healthcare professional, data collectors, and supervisors who participated in this study.

No funding was obtained for this study.

Author information

Authors and affiliations.

College of Medicine and Health Sciences, School of Public Health, Department of Health Informatics, Wachemo University, Hosaena, Ethiopia

Samuel Hailegebreal

School of Public Health, College of Medicine and Health Sciences, Arba Minch University, Arba Minch, Ethiopia

Samuel Hailegebreal, Temesgen Dileba, Yosef Haile & Sintayehu Abebe

You can also search for this author in PubMed   Google Scholar

Contributions

Proposal preparation, acquisition of data, analysis, and interpretation of data was done by SH, TD, YH, and SA instruct the study design data cleaning and analysis. SH drafted the manuscript and all authors have a substantial contribution in revising and finalizing the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Samuel Hailegebreal .

Ethics declarations

Ethics approval and consent to participate.

Ethics approval (approval number IRB/1163/2020) was obtained from Arba Minch University, College of medicine and health science institution review board. A formal letter from the university addressed to the participating hospitals was taken and submitted. Informed written consent was obtained from the participants after thoroughly discussing the idea behind the study, and study participant rights. Participants were also assured that the confidentiality of the information they provided would be maintained. All methods were carried out in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Hailegebreal, S., Dileba, T., Haile, Y. et al. Health professionals’ readiness to implement electronic medical record system in Gamo zone public hospitals, southern Ethiopia: an institution based cross-sectional study. BMC Health Serv Res 23 , 773 (2023). https://doi.org/10.1186/s12913-023-09745-5

Download citation

Received : 01 March 2023

Accepted : 24 June 2023

Published : 19 July 2023

DOI : https://doi.org/10.1186/s12913-023-09745-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Electronic health record
• Electronic medical record
• Health professional
• Southern Ethiopia

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

The Role of Electronic Health Records to Identify Risk Factors for Developing Long COVID: A Scoping Review

• Conference paper
• First Online: 13 May 2024
• Cite this conference paper

• Ema Santos 14 ,
• Afonso Fernandes 14 ,
• Manuel Graça 14 &
• Nelson Pacheco Rocha   ORCID: orcid.org/0000-0003-3801-7249 15  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 986))

Included in the following conference series:

• World Conference on Information Systems and Technologies

Coronavirus disease 2019 (COVID-19) was considered a global pandemic from December 2019 to May 2023. A subset of COVID-19 patients develops long-lasting sequelae, commonly referred to as long COVID. This scoping review aimed to identify risk factors for long COVID reported in multiple studies and to determine the role of the secondary use of Electronic Health Records to identify these risk factors. An electronic search was conducted on Scopus, PubMed, and Web of Science, and 46 studies were included in this review after the selection process. Thirty-one risk factors were identified, with the most referred ones being female sex, age, severity of infection and obesity. In terms of data collection, Electronic Health Records were used by 63.0% of the studies, although only 21.7% were retrospective studies exclusively based on the secondary use of Electronic Health Records data. These results show that the potential of clinical research based on the secondary use of data collected from Electronic Health Records is not yet fully achieved, despite the respective advantages when compared with other data collection methods such as remote surveys.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Shah, W., Hillman, T., Playford, E.D., Hishmeh, L.: Managing the long term effects of covid-19: summary of NICE, SIGN, and RCGP rapid guideline. BMJ 372 (2021)

Google Scholar  

Davis, H.E., McCorkell, L., Vogel, J.M., Topol, E.J.: Long COVID: major findings, mechanisms and recommendations. Nat. Rev. Microbiol. 21 , 133–146 (2023)

Article   Google Scholar  

Koc, H.C., Xiao, J., Liu, W., Li, Y., Chen, G.: Long COVID and its Management. Int. J. Biol. Sci. 18 (12), 4768–4780 (2022)

Williams, J.G.: The use of clinical information to help develop new services in a district general hospital. Int. J. Med. Informatics 56 (1–3), 151–159 (1999)

Näher, A.F., et al.: Secondary data for global health digitalisation. Lancet Digital Health, 5 (2), e93-e101 (2023)

Fernández-de-Las-Peñas, C., et al.: Diabetes and the risk of long-term post-COVID symptoms. Diabetes 70 (12), 2917–2921 (2021)

Mady, A.F., Abdelfattah, R.A., Kamel, F.M., Abdel Naiem, A.S.M., AbdelGhany, W.M., Abdelaziz, A.O.: Predictors of long covid 19 syndrome. Egyptian J. Hospital Med. 85 (2), 3604–3608 (2021)

Osikomaiya, B., et al.: Long COVID’: persistent COVID-19 symptoms in survivors managed in Lagos State, Nigeria. BMC Infect. Diseases 21(1), 1–7 (2021)

Peghin, M., et al.: Post-COVID-19 symptoms 6 months after acute infection among hospitalized and non-hospitalized patients. Clinical Microbiol. Infect. 27 (10), 1507–1513 (2021)

Sudre, C.H., et al.: Attributes and predictors of long COVID. Nat. Med. 27 (4), 626–631 (2021)

Vimercati, L., et al.: Association between long COVID and overweight/obesity. J.  Clin. Med. 10 (18), 4143 (2021)

Arjun, M.C., et al.: Characteristics and predictors of Long COVID among diagnosed cases of COVID-19. Plos one 17 (12), e0278825 (2022)

Banić, M.,et al.: Risk factors and severity of functional impairment in long COVID: a single-center experience in Croatia. Croatian Med. J. 63 (1), 27–35 (2022)

Baris, S.A., et alk.: The predictors of long–COVID in the cohort of Turkish Thoracic Society–TURCOVID multicenter registry: one year follow–up results. Asian Pacific J. Tropical Med. 15 (9), 400–409 (2022)

Baruch, J., Zahra, C., Cardona, T., Melillo, T.: National long COVID impact and risk factors. Public Health 213 , 177–180 (2022)

Buonsenso, D., et al.: The prevalence, characteristics and risk factors of persistent symptoms in non-hospitalized and hospitalized children with sars-cov-2 infection followed-up for up to 12 months: a prospective, cohort study in Rome, Italy. J. Clin. Med. 11 (22), 6772 (2022)

Chudzik, M., Babicki, M., Kapusta, J., Kałuzinska-Kołat, Z., Kołat, D., Jankowski, P.: Long-COVID Clinical Features and Risk Factors: a Retrospective Analysis of Patients from the STOP-COVID Registry of the PoLoCOV Study. Viruses 14 (8), 1755 (2022)

Chudzik, M., Lewek, J., Kapusta, J., Banach, M., Jankowski, P., Bielecka-Dabrowa, A.: Predictors of long COVID in patients without comorbidities: data from the polish long-covid cardiovascular (polocov-cvd) study. J. Clin. Med. 11 (17), 4980 (2022)

Cristillo, V., Pilotto, A., Cotti Piccinelli, S., Bonzi, G., et al.: Premorbid vulnerability and disease severity impact on Long-COVID cognitive impairment. Aging Clin. Experim. Res. 34 , 257–260 (2022)

Daitch, V., Yet al.: Characteristics of long-COVID among older adults: A cross-sectional study. Inter. J. Infect. Diseases 125 , 287–293 (2022)

El Otmani, H., Nabili, S., Berrada, M., Bellakhdar, S., El Moutawakil, B., Abdoh Rafai, M.: Prevalence, characteristics and risk factors in a Moroccan cohort of Long-Covid-19. Neurol. Sci. 43 (9), 5175–5180

Fernández-de-Las-Peñas, C., et al.: Female sex is a risk factor associated with long-term post-COVID related-symptoms but not with COVID-19 symptoms: the LONG-COVID-EXP-CM multicenter study. J. Clin. Med. 11 (2), 413 (2022)

Fernández-de-Las-Peñas, C., et al.: Symptoms experienced at the acute phase of SARS-CoV-2 infection as risk factor of long-term post-COVID symptoms: the LONG-COVID-EXP-CM multicenter study. Int. J. Infect. Dis. 116 , 241–244 (2022)

Frontera, J.A., et al.: Life stressors significantly impact long-term outcomes and post-acute symptoms 12-months after COVID-19 hospitalization. J. Neurol. Sci. 443 , 120487 (2022)

Heubner, L., et al.: Extreme obesity is a strong predictor for in-hospital mortality and the prevalence of long-COVID in severe COVID-19 patients with acute respiratory distress syndrome. Sci. Rep. 12 (1), 18418

Hultström, M., et al.: Dehydration is associated with production of organic osmolytes and predicts physical long-term symptoms after COVID-19: a multicenter cohort study. Critical Care 26 (1), 1–9 (2022)

Izquierdo-Condoy, J.S., et al.: Long COVID at different altitudes: a countrywide epidemiological analysis. Inter. J. Environ. Res. Public Health 19 (22), 14673 (2022)

Knight, D.R., Munipalli, B., Logvinov, I.I., Halkar, M.G., Mitri, G., Hines, S.L.: Perception, prevalence, and prediction of severe infection and post-acute sequelae of COVID-19. Am. J. Med. Sci. 363 (4), 295–304 (2022)

Ko, A.C.S.,et al.: Number of initial symptoms is more related to long COVID-19 than acute severity of infection: A prospective cohort of hospitalized patients. Inter. J. Infect. Diseases 118 , 220–223 (2022)

Liao, X., et al.: Long-term sequelae of different COVID-19 variants: the original strain versus the Omicron variant. Global Health Med. 4 (6), 322–326 (2022)

Margalit, I., Yelin, D., Sagi, M., Rahat, M. M., Sheena, L., Mizrahi, N., ... Yahav, D.: Risk factors and multidimensional assessment of long coronavirus disease fatigue: a nested case-control study. Clinical Infectious Diseases, 75(10), 1688–1697 (2022)

Mazza, M.G., Palladini, M., Villa, G., De Lorenzo, R., Querini, P.R., Benedetti, F.: Prevalence, trajectory over time, and risk factor of post-COVID-19 fatigue. J. Psychiatr. Res.Psychiatr. Res. 155 , 112–119 (2022)

Osmanov, I.M., et al.: Risk factors for post-COVID-19 condition in previously hospitalised children using the ISARIC Global follow-up protocol: a prospective cohort study. European Resp. J. 59 (2) (2022)

Pazukhina, E., et al.: Prevalence and risk factors of post-COVID-19 condition in adults and children at 6 and 12 months after hospital discharge: a prospective, cohort study in Moscow (StopCOVID). BMC Med. 20 (1), 244 (2022)

Perlis, R.H., et al.: Prevalence and correlates of long COVID symptoms among US adults. JAMA Netw. Open 5 (10), e2238804-e2238804 (2022)

Pływaczewska-Jakubowska, M., Chudzik, M., Babicki, M., Kapusta, J., Jankowski, P.: Lifestyle, course of COVID-19, and risk of Long-COVID in non-hospitalized patients. Front. Med. 9 , 1036556 (2022)

Righi, E., et al.: Determinants of persistence of symptoms and impact on physical and mental wellbeing in Long COVID: a prospective cohort study. J. Infect. 84 (4), 566–572 (2022)

Rinaldi, R., Basile, M., Salzillo, C., Grieco, D. L., Caffè, A., Masciocchi, C.: Gemelli against COVID Group.: Myocardial injury portends a higher risk of mortality and long-term cardiovascular sequelae after hospital discharge in COVID-19 survivors. J. Clin. Med. 11 (19), 5964 (2022)

Sansone, D., et al.: Persistence of symptoms 15 months since COVID-19 diagnosis: prevalence, risk factors and residual work ability. Life 13 (1), 97 (2022)

Spinicci, M., et al.: Infection with SARS-CoV-2 variants is associated with different long COVID phenotypes. Viruses 14 (11), 2367 (2022)

Su, Y., et al.: Multiple early factors anticipate post-acute COVID-19 sequelae. Cell 185 (5), 881–895 (2022)

Subramanian, A., et al.: Symptoms and risk factors for long COVID in non-hospitalized adults. Nat. Med. 28 (8), 1706–1714 (2022)

Thompson, E.J., Williams, D.M., Walker, A.J., Mitchell, R.E., Niedzwiedz, C.L., Yang, T.C.: Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records. Nat. Commun. 13 (1), 3528 (2022)

Wang, S., et al.: Associations of depression, anxiety, worry, perceived stress, and loneliness prior to infection with risk of post–COVID-19 conditions. JAMA Psych. 79 (11), 1081–1091 (2022)

Adler, L., et al.: Long COVID symptoms in Israeli children with and without a history of SARS-CoV-2 infection: a cross-sectional study. BMJ Open 13 (2), e064155 (2023)

Bovil, T., Wester, C.T., Scheel-Hincke, L.L., Andersen-Ranberg, K.: Risk factors of post-COVID-19 conditions attributed to COVID-19 disease in people aged≥ 50 years in Europe and Israel. Public Health 214 , 69–72 (2023)

Daines, L., et al.: Characteristics and risk factors for post-COVID-19 breathlessness after hospitalisation for COVID-19. ERJ Open Res. 9 (1) (2023)

Lapa, J., Rosa, D., Mendes, J.P.L., Deusdará, R., Romero, G.A.S.: Prevalence and associated factors of post-COVID-19 syndrome in a Brazilian cohort after 3 and 6 months of hospital discharge. Int. J. Environ. Res. Public Health 20 (1), 848 (2023)

Nørgård, B.M., Zegers, F.D., Juhl, C.B., Kjeldsen, J., Nielsen, J.: Diabetes mellitus and the risk of post-acute COVID-19 hospitalizations—a nationwide cohort study. Diabet. Med.  40 (2), e14986 (2023)

Tene, L., Bergroth, T., Eisenberg, A., David, S.S.B., Chodick, G.: Risk factors, health outcomes, healthcare services utilization, and direct medical costs of patients with long COVID. Int. J. Infect. Dis. 128 , 3–10 (2023)

Zhang, Y., et al.: Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program. Environ. Adv. 11 , 100352 (2023)

Download references

Acknowledgments

This study was carried out within the scope of the course unit Clinical Information Management of the Master’s in Clinical Bioinformatics at the University of Aveiro.

Author information

Authors and affiliations.

Department of Medical Sciences, University of Aveiro, Aveiro, Portugal

Ema Santos, Afonso Fernandes & Manuel Graça

IEETA, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal

Nelson Pacheco Rocha

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Nelson Pacheco Rocha .

Editor information

Editors and affiliations.

ISEG, Universidade de Lisboa, Lisbon, Portugal

Álvaro Rocha

College of Engineering, The Ohio State University, Columbus, OH, USA

Hojjat Adeli

Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

Gintautas Dzemyda

DCT, Universidade Portucalense, Porto, Portugal

Fernando Moreira

Institute of Information Technology, Lodz University of Technology, Łódz, Poland

Aneta Poniszewska-Marańda

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Cite this paper.

Santos, E., Fernandes, A., Graça, M., Rocha, N.P. (2024). The Role of Electronic Health Records to Identify Risk Factors for Developing Long COVID: A Scoping Review. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-031-60218-4_12

Download citation

DOI : https://doi.org/10.1007/978-3-031-60218-4_12

Published : 13 May 2024

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-60217-7

Online ISBN : 978-3-031-60218-4

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

An official website of the Department of Health & Human Services

• Search All AHRQ Sites
• Email Updates

Informing Improvement in Care Quality, Safety, and Efficiency

• Contact DHR

A Proposal for Electronic Medical Records in U.S. Primary Care

• Director's Corner
• Current Priorities
• Executive Summary
• Research Spotlight
• Research Themes and Findings
• Research Dissemination
• Research Overview
• 2020 Year in Review
• 2019 Year in Review
• Engaging and Empowering Patients
• Optimizing Care Delivery for Clinicians
• Supporting Health Systems in Advancing Care Delivery
• Our Experts
• Search AHRQ-Funded Projects
• AHRQ-Funded Projects Map
• AHRQ Digital Healthcare Research Publications Database
• A Practical Guide for Implementing the Digital Healthcare Equity Framework
• ePROs in Clinical Care
• Guide to Integrate Patient-Generated Digital Health Data into Electronic Health Records in Ambulatory Care Settings
• Health IT Survey Compendium
• Time and Motion Studies Database
• Health Information Security and Privacy Collaboration Toolkit
• Implementation in Independent Pharmacies
• Implementation in Physician Offices
• Children's Electronic Health Record (EHR) Format
• Project Resources Archives
• Archived Tools & Resources
• National Webinars
• Funding Opportunities
• Digital Healthcare Research Home
• 2018 Year in Review Home
• Research Summary
• Research Spotlights
• 2019 Year in Review Home
• Annual Report Home

An official website of the Department of Health & Human Services

• Search All AHRQ Sites
• Email Updates

1. Use quotes to search for an exact match of a phrase.

2. Put a minus sign just before words you don't want.

3. Enter any important keywords in any order to find entries where all these terms appear.

• The PSNet Collection
• All Content
• Perspectives
• Current Weekly Issue
• Past Weekly Issues
• Curated Libraries
• Clinical Areas
• Patient Safety 101
• The Fundamentals
• Training and Education
• Continuing Education
• WebM&M: Case Studies
• Training Catalog
• Submit a Case
• Improvement Resources
• Innovations
• Submit an Innovation
• About PSNet
• Editorial Team
• Technical Expert Panel

The safety of outpatient health care: review of electronic health records.

Levine DM, Syrowatka A, Salmasian H, et al. The safety of outpatient health care: review of electronic health records. Ann Intern Med. 2024;Epub May 7. doi:10.7326/m23-2063.

Although most care occurs in outpatient settings, research into adverse events (AE) in this setting remains sparse in comparison to acute care. In this study, the medical records of patients who received outpatient care (e.g., primary, radiology , emergency, ambulatory surgery) in Massachusetts in 2018, were reviewed for any occurrence of adverse events. Seven percent had at least one AE, most commonly adverse drug events , and 23% were potentially preventable. Most AE originated from care in the physician's office.

The safety of inpatient health care. January 25, 2023

Expert consensus on currently accepted measures of harm. September 9, 2020

An analysis of the structure and content of dashboards used to monitor patient safety in the inpatient setting. December 8, 2021

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. March 9, 2022

Implementation of the I-PASS handoff program in diverse clinical environments: a multicenter prospective effectiveness implementation study. November 16, 2022

Families as partners in hospital error and adverse event surveillance. March 8, 2017

Optimizing Pediatric Patient Safety in the Emergency Care Setting. October 19, 2022

Seroprevalence of SARS-CoV-2 among frontline health care personnel in a multistate hospital network--13 academic medical centers, April-June 2020. September 23, 2020

The frequency of intravenous medication administration errors related to smart infusion pumps: a multihospital observational study. March 16, 2016

Comparison of a prototype for indications-based prescribing with 2 commercial prescribing systems. May 1, 2019

Incorporating medication indications into the prescribing process. May 9, 2018

Medication-related clinical decision support alert overrides in inpatients. November 15, 2017

Computerized prescriber order entry–related patient safety reports: analysis of 2522 medication errors. October 19, 2016

Evaluation of medication-related clinical decision support alert overrides in the intensive care unit. May 10, 2017

An examination of medical malpractice claims involving physician trainees. August 19, 2020

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions. August 4, 2021

High-priority drug-drug interaction clinical decision support overrides in a newly implemented commercial computerized provider order-entry system: override appropriateness and adverse drug events. May 20, 2020

Ten principles for more conservative, care-full diagnosis. October 10, 2018

Long-term sustainability and adaptation of I-PASS handovers. September 13, 2023

Analysis of variations in the display of drug names in computerized prescriber-order-entry systems. May 24, 2017

The frequency of inappropriate nonformulary medication alert overrides in the inpatient setting. April 6, 2016

Changes in medical errors after implementation of a handoff program. November 12, 2014

A multi-hospital before–after observational study using a point-prevalence approach with an infusion safety intervention bundle to reduce intravenous medication administration errors. June 27, 2018

Relationship between medication event rates and the Leapfrog computerized physician order entry evaluation tool. May 15, 2013

Surviving Sepsis Campaign: international guidelines for management of sepsis and septic shock: 2016 March 3, 2017

A cross-sectional observational study of high override rates of drug allergy alerts in inpatient and outpatient settings, and opportunities for improvement. May 25, 2016

Patient safety after implementation of a coproduced family centered communication programme: multicenter before and after intervention study. December 19, 2018

Outpatient CPOE orders discontinued due to 'erroneous entry': prospective survey of prescribers' explanations for errors. August 16, 2017

Electronic health records and malpractice claims in office practice. December 3, 2008

Cost of inpatient falls and cost-benefit analysis of implementation of an evidence-based fall prevention program. February 1, 2023

How often do prescribers include indications in drug orders? Analysis of 4 million outpatient prescriptions. July 10, 2019

Patient activation related to fall prevention: a multisite study February 19, 2020

The potential of artificial intelligence to improve patient safety: a scoping review. March 31, 2021

Using drug knowledgebase information to distinguish between look-alike-sound-alike drugs. June 27, 2018

Patient safety indicators during the initial COVID-19 pandemic surge in the United States. March 27, 2024

Evaluation of harm associated with high dose-range clinical decision support overrides in the intensive care unit. January 23, 2019

Prospective evaluation of medication-related clinical decision support over-rides in the intensive care unit. February 28, 2018

Effect of restriction of the number of concurrently open records in an electronic health record on wrong-patient order errors: a randomized clinical trial. May 29, 2019

Screening for adverse drug events: a randomized trial of automated calls coupled with phone-based pharmacist counseling. March 6, 2019

Readiness of US general surgery residents for independent practice. October 4, 2017

Preventing home medication administration errors. March 14, 2022

Determination of unnecessary blood transfusion by comprehensive 15-hospital record review. January 25, 2023

Are physicians' perceptions of healthcare quality and practice satisfaction affected by errors associated with electronic health record use? January 11, 2012

Risk of wrong-patient orders among multiple vs singleton births in the neonatal intensive care units of 2 integrated health care systems. September 4, 2019

Overrides of medication-related clinical decision support alerts in outpatients. January 8, 2014

Using a machine learning system to identify and prevent medication prescribing errors: a clinical and cost analysis evaluation. December 18, 2019

Screening for medication errors using an outlier detection system. February 8, 2017

Changes in safety attitude and relationship to decreased postoperative morbidity and mortality following implementation of a checklist-based surgical safety intervention. February 9, 2011

Effects of an online personal health record on medication accuracy and safety: a cluster-randomized trial. May 23, 2012

Association of display of patient photographs in the electronic health record with wrong-patient order entry errors. December 2, 2020

Evaluation of clinical practice guidelines on fall prevention and management for older adults: a systematic review. January 12, 2022

Evaluation of a patient-centered fall-prevention tool kit to reduce falls and injuries: a nonrandomized controlled trial. December 2, 2020

Patient safety in the cardiac operating room: human factors and teamwork: a scientific statement from the American Heart Association. August 21, 2013

Role of pharmacist counseling in preventing adverse drug events after hospitalization. March 22, 2006

Effect of genetic diagnosis on patients with previously undiagnosed disease. November 7, 2018

The vulnerabilities of computerized physician order entry systems: a qualitative study. December 9, 2015

Association of simulation training with rates of medical malpractice claims among obstetrician-gynecologists. October 13, 2021

Extended work shifts and neurobehavioral performance in resident-physicians. March 10, 2021

Comparison of a voluntary safety reporting system to a global trigger tool for identifying adverse events in an oncology population. August 3, 2022

Prospective evaluation of a multifaceted intervention to improve outcomes in intensive care: the Promoting Respect and Ongoing Safety through Patient Engagement Communication and Technology study. May 24, 2017

The impact of racism on child and adolescent health. July 1, 2019

Engaging patients in the use of real-time electronic clinical data to improve the safety and reliability of their own care. May 4, 2022

Four-year impact of an alert notification system on closed-loop communication of critical test results. November 26, 2014

Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: a cluster-randomized trial. April 29, 2009

Conducting safety research safely: a policy-based approach for conducting research with peer review protected material. January 13, 2021

Impact of vendor computerized physician order entry in community hospitals. February 22, 2012

Safety culture and mortality after acute myocardial infarction: a study of Medicare beneficiaries at 171 hospitals. October 25, 2017

Mixed-methods evaluation of real-time safety reporting by hospitalized patients and their care partners: the MySafeCare application. June 13, 2018

Operational failures detected by frontline acute care nurses. March 29, 2017

Computerised physician order entry-related medication errors: analysis of reported errors and vulnerability testing of current systems. January 28, 2015

Effect of bar-code technology on the safety of medication administration. May 12, 2010

Ambulatory prescribing errors among community-based providers in two states. December 21, 2011

Rates of medical errors and preventable adverse events among hospitalized children following implementation of a resident handoff bundle. December 11, 2013

Physician and nurse well-being and preferred interventions to address burnout in hospital practice: factors associated with turnover, outcomes, and patient safety. July 19, 2023

A randomized trial of a multifactorial strategy to prevent serious fall injuries. July 29, 2020

Crowding in the Emergency Department: Challenges for the Care of Children. March 15, 2023

Effect of number of open charts on intercepted wrong-patient medication orders in an emergency department. May 30, 2018

An effort to improve electronic health record medication list accuracy between visits: patients' and physicians' response. March 19, 2008

Crisis checklists for the operating room: development and pilot testing. January 30, 2005

A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts. December 7, 2022

Using incident reporting to improve patient safety: a conceptual model. March 28, 2007

The Veterans Affairs National Quality Scholars Program: a model for interprofessional education in quality and safety. August 22, 2012

Prospective study of the multisite spread of a medication safety intervention: factors common to hospitals with improved outcomes. February 7, 2024

Toward improving patient safety through voluntary peer-to-peer assessment. January 25, 2012

Cumulative effect of flexible duty-hour policies on resident outcomes: long-term follow-up results from the FIRST trial. July 15, 2020

Effect on patient safety of a resident physician schedule without 24-hour shifts. July 15, 2020

Factors determining safety culture in hospitals: a scoping review. October 25, 2023

Sustaining reductions in catheter related bloodstream infections in Michigan intensive care units: observational study. March 10, 2010

Diagnostic concordance among pathologists interpreting breast biopsy specimens. March 25, 2015

Physicians and electronic health records: a statewide survey. March 21, 2007

Adverse events in hospitalized pediatric patients. July 25, 2018

Effects on resident work hours, sleep duration and work experience in a Randomized Order Safety Trial Evaluating Resident-physician Schedules (ROSTERS). June 26, 2019

Using evidence, rigorous measurement, and collaboration to eliminate central catheter-associated bloodstream infections. August 25, 2010

Large-scale implementation of the I-PASS handover system at an academic medical centre. March 22, 2017

The impact of automated notification on follow-up of actionable tests pending at discharge: a cluster-randomized controlled trial. April 11, 2018

Clinical communities at Johns Hopkins Medicine: an emerging approach to quality improvement. September 2, 2015

Impact of sleep deficiency on surgical performance: a prospective assessment. April 19, 2023

Improving patient safety in intensive care units in Michigan. June 25, 2008

Renal medication-related clinical decision support (CDS) alerts and overrides in the inpatient setting following implementation of a commercial electronic health record: implications for designing more effective alerts. February 17, 2021

Education outcomes from a duty-hour flexibility trial in internal medicine. March 28, 2018

Stranded in the ER, seniors await hospital care and suffer avoidable harm. May 15, 2024

WebM&M Cases

The association between nurse staffing and quality of care in emergency departments: a systematic review. March 20, 2024

Patient reasoning: patients' and care partners' perceptions of diagnostic accuracy in emergency care. January 10, 2024

Nurses' experience with presenteeism and the potential consequences on patient safety: a qualitative study among nurses at out-of-hours emergency primary care facilities. January 10, 2024

Overnight stay in the emergency department and mortality in older patients. November 29, 2023

Network of Patient Safety Databases Chartbook. November 8, 2023

Cognitive bias during clinical decision-making and its influence on patient outcomes in the emergency department: a scoping review. October 25, 2023

"You just want to feel safe when you go to a healthcare professional:" intimate partner violence and patient safety. August 2, 2023

Patient Safety Innovations

Remote Response Team and Customized Alert Settings Help Improve Management of Sepsis

Annual Perspective

Reducing diagnostic errors in the emergency department at the time of patient treatment. March 29, 2023

Near-miss events detected using the emergency department trigger tool. March 15, 2023

Perspective

Journal Article

Catching those who fall through the cracks: integrating a follow-up process for emergency department patients with incidental radiologic findings.

Emergency department adverse events detected using the emergency department trigger tool. August 24, 2022

The frequency and nature of prescribing problems by general practitioners in training (REVISiT). June 15, 2022

Diagnostic trajectories in primary care at 12 months: an observational cohort study. June 15, 2022

Assessing quality of older persons' emergency transitions between long-term and acute care settings: a proof-of-concept study. May 18, 2022

Clinician distress and inappropriate antibiotic prescribing for acute respiratory tract infections: a retrospective cohort study. May 11, 2022

Impact of teamwork and communication training interventions on safety culture and patient safety in emergency departments: a systematic review. January 26, 2022

SECUre: a multicentre survey of the safety of emergency care in UK emergency departments. November 24, 2021

Incidence of prescription errors in patients discharged from the emergency department. September 22, 2021

Adverse events in emergency department boarding: a systematic review. August 18, 2021

Disparate perspectives: exploring healthcare professionals' misaligned mental models of older adults' transitions of care between the emergency department and skilled nursing facility. July 21, 2021

Experiences of transgender and gender nonbinary patients in the emergency department and recommendations for health care policy, education, and practice. July 21, 2021

Moving beyond the weekend effect: how can we best target interventions to improve patient care? June 30, 2021

Treatment patterns and clinical outcomes after the introduction of the Medicare Sepsis Performance Measure (SEP-1). May 5, 2021

Connect With Us

Sign up for Email Updates

To sign up for updates or to access your subscriber preferences, please enter your email address below.

Agency for Healthcare Research and Quality

5600 Fishers Lane Rockville, MD 20857 Telephone: (301) 427-1364

• Accessibility
• Disclaimers
• Electronic Policies
• HHS Digital Strategy
• HHS Nondiscrimination Notice
• Inspector General
• Plain Writing Act
• Privacy Policy
• Viewers & Players
• U.S. Department of Health & Human Services
• The White House
• Don't have an account? Sign up to PSNet

Submit Your Innovations

Please select your preferred way to submit an innovation.

Continue as a Guest

Track and save your innovation

in My Innovations

Edit your innovation as a draft

Continue Logged In

Please select your preferred way to submit an innovation. Note that even if you have an account, you can still choose to submit an innovation as a guest.

Continue logged in

New users to the psnet site.

Access to quizzes and start earning

CME, CEU, or Trainee Certification.

Get email alerts when new content

matching your topics of interest

in My Innovations.

• Skip to main content
• Skip to FDA Search
• Skip to in this section menu
• Skip to footer links

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

U.S. Food and Drug Administration

•   Search
•   Menu

Apologies; the page you are requesting is currently unavailable. The request resembles an abusive automated request. If you believe this an error, please contact us and we will assist in resolving the issue.

Thank you for visiting!

Santa Clarita Valley's #1 Local News Source

Making sense of electronic health records (ehrs) with nlp, sponsored post.

• May 16, 2024

The healthcare industry has been transformed by the digitization of patient details throughout the day and so the manner of storage and handling is also changed. Electronic Health Records (EHRs) are the contemporary digital versions of the original paper records, which integrate all the patient’s medical history, treatment choices, diagnoses, and more in a secure electronic form. At present, we are enjoying the most modern technology in the shape of the Natural Language Processing (NLP).

Challenges in EHR Interpretation

The health environment has become highly progressive and EHRs are the biggest leap in the organization and accessibility of patient data in the last few decades. Nevertheless, despite, the improvement of the efficiency and the better patient care is always patented, the healthcare professionals still have a lot of difficulties in the interpretation and the use of the information that is stored in the digital archives.

Complexity of EHR Data

The primary issue of the EHRs is that they are too complicated. On the contrary, the EHRs have both structured and unstructured data that consist of clinical notes, laboratory results, imaging studies, and medication histories, which are very data-rich. The lot of EHR data creates a very big problem for the healthcare workers who are in charge of extracting the meaningful data from it and making the good clinical decisions.

Lack of Standardization in EHR Formats

Besides that, the complexity of EHR data is increased by the fact that there is no EHR format that is standardized among the healthcare systems and institutions. Although the implementation of a number of measures was done to establish the standards of the compatible exchange of electronic health information, the data presentation, terminologies and coding systems are still different.

Role of NLP

The task of NLP in the analysis of EHRs is to interpret the digital documents, extract the right facts and help the decision makers to make the right decisions. Natural Language Processing (NLP) is the technology that is brought to the healthcare innovation the forefront, and it is a game-changer, from the advanced techniques to the tools that can interpret the Electronic Health Records (EHRs). By means of NLP, the healthcare professionals can identify the hidden secrets in the EHRs and therefore, they will be able to have the most efficient, the most precise and the data-driven clinical practices. Ways NLP Facilitates Efficient Data Extraction The main purpose of the project is to employ the AI system to extract the necessary data by means of NLP. The NLP in EHR analysis has the ability to quickly and precisely find the exact information that is important in the huge amount of unstructured text in the EHRs. Unstructured text is quite different from structured data which is organized and quantifiable. Thus, the unstructured text is the major obstacle for the normal analysis methods. Nevertheless, NLP techniques assist computers to comprehend and interpret human language in a way that is similar to what humans do. Thus, this ability lets the automatic removal of the vital clinical concepts, relationships, and events from the free-text clinical notes, reports, and other narrative documentation which can be found in the EHRs. By setting up data extraction, NLP enables the EHR analysis to be automatic, thus, leaving the healthcare professionals with nothing to do but to check themselves for the tedious manual review. Thus, they can attend to more important things like patient care and the making of the right decisions.

The significance of Text Annotation Tools

Text annotation tools are the key elements of the performance of NLP in EHR analysis. These tools are very important in the training and refinement of NLP models so that they can correctly and precisely deduce and extract information from clinical text. Text Annotation Tool enables the human annotators to label and annotate EHR data with the relevant clinical concepts, entities and relationships, thus the annotated data which is the labeled data needed for the NLP algorithms to be trained. 

The NLP models that are improved by the multiple training and validation cycles are able to recognize the patterns and the associations in the clinical text, hence, they can extract and interpret the information from the EHRs more accurately. Text Annotation Tools Text annotation tools are the primary instruments in the growth of Natural Language Processing (NLP), they are the main reason for the creation and improvement of algorithms for textual data, including Electronic Health Records (EHRs), the correct reading and extraction of insights. In this section, we interpret the significance, the objectives, the types, and the advantages of text annotation tools in NLP and demonstrate why they are vital in the creation of healthcare analytics and decision-making.

Types of Text Annotation Tools

Different kinds of text annotation tools can be found, each of them is designed for a certain NLP task and workflow. Some common types of text annotation tools include: 1. Entity Recognition Tools:  

These tools empower the annotators to detect and label the named entities like illnesses, drugs, and medical operations in the text documents. 2. Relationship Extraction Tools:  

These instruments help the annotators to mark the relations and the associations between the entities mentioned in the text, for example, the connection between a diagnosis and a treatment. 3. Sentiment Analysis Tools:  

These tools make it easier for us to annotate the sentiment and opinion expressed in the text, and thus, we can put the text into positive, negative, or neutral categories. 4. Document Classification Tools:  

Such tools are the tools for the annotation of text documents with predefined categories or labels which, in turn, are the means for the classification of the documents according to their contents.

Advantages of Text Annotation Tools in NLP

The utilization of Text Annotation Tools in NLP bears various benefits such as: 1. Streamlining Data Labeling Process:  

Text annotation tools are the ones that make the process of labeling and annotating textual data, automatic and time saving, which in turn, lowers the time and effort that is used to create the annotated datasets for the NLP model training. 2. Improving NLP Model Training:  

The annotated datasets of high-quality origin that is prepared with text annotation tools act as the background for the training and fine-tuning of NLP models, thus, the outcome is the improvement of model performance and accuracy. 3. Enabling Better Insights and Decision-Making in Healthcare: 

Text annotation tools make it possible for the analysis of the clinical text which in turn is meant for the extraction of the meaningful information from it thus helping the healthcare organizations to make the actionable insights and finally the patient care and outcomes are improved.

Conclusion:

In summary, Natural Language Processing (NLP) along with text annotation tools changes the way of interpretation of Electronic Health Records (EHRs), providing highly beneficial results for healthcare organizations. NLP of EHRs is the key to the speed and accuracy of the data that is extracted, that is, the EHRs are very complex. It is the tool that enables the healthcare professionals to get the specific data, which in turn, they use for their decision making and thus the patient’s care is improved. 

The employment of NLP and text annotation tools by healthcare organizations is a necessity, as it allows them to tap the complete information from their EHR data. The coming years will be amazing for the future of EHR interpretation with NLP, as the progress being made is turning the healthcare delivery and patient outcomes into something that will will not be possible to be anything other than phenomenal. 

Related To This Story

The rising trend of dbs checks: why companies are opting for them more than ever, encouraging healthy debates around the watercooler, ways to maximize your atlanta personal injury settlement, navigating the golden state: essential riverside moving tips for a smooth transition, future outlook: innovations in venture capital portfolio management, five benefits of hiring a moving company, latest news.

Photos: Hit the Trail community bike ride

Sexual battery suspect arrested at gunpoint in Newhall  

Senses: ‘Alice in Wonderland’

West Ranch junior starts nonprofit to help athletes 

Photos: Newhall School District hosts Parent Resource Fair

Sign up for the, morning rundown.

Filled with the top stories to start your day, and emergency news alerts. 

25060 Avenue Stanford, St. 141

Valencia, CA, 91355

Main Desk: 661-259-1234

Newsroom: 661-255-1234

Advertising: 661-287-5564

Have a news tip? Let us know!

News Sections

• Coronavirus
• Environment
• Politics & Government

More Sections

• Video + Podcasts
• Sunday Signal
• Subscribe to Print
• Classified Ads
• Event Calendar

• Introduction
• Article Information

Results are from logistic regression models controlling for age, Hispanic or Latina/x ethnicity, marital status, parity, tobacco use, prenatal visit utilization, stillbirth, and placental abruption. Other race includes Alaska Native, American Indian, Chinese, Filipino, Guam/Chamorro Hawaiian, Indian, Japanese, Korean, Other Asian/Pacific Islander, Samoan, and Vietnamese. In the sample, 4100 patients had a history of substance use, and 33 760 had no history of substance use; 4636 had a urine toxicology test, and 2199 had any positive test result at labor and delivery. Error bars indicate 95% CIs.

Data Sharing Statement

See More About

Select your interests.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing

Others Also Liked

• Download PDF
• X Facebook More LinkedIn

Jarlenski M , Shroff J , Terplan M , Roberts SCM , Brown-Podgorski B , Krans EE. Association of Race With Urine Toxicology Testing Among Pregnant Patients During Labor and Delivery. JAMA Health Forum. 2023;4(4):e230441. doi:10.1001/jamahealthforum.2023.0441

Manage citations:

© 2024

• Permissions

Association of Race With Urine Toxicology Testing Among Pregnant Patients During Labor and Delivery

• 1 Department of Health Policy and Management, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania
• 2 Friends Research Institute, Baltimore, Maryland
• 3 Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco
• 4 Department of Obstetrics, Gynecology & Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
• 5 Magee-Womens Research Institute, Pittsburgh, Pennsylvania

An estimated 16% of pregnant persons in the US use alcohol (10%) or an illicit substance (6%, including cannabis). 1 Urine toxicology testing (UTT) is often performed at the time of labor and delivery for pregnant patients to evaluate substance use. 2 , 3 We sought to elucidate associations between race and receipt of UTT and a positive test result among pregnant patients admitted to the hospital for delivery.

This cohort study followed the STROBE reporting guideline. Data were extracted from electronic medical records (EMRs) of patients with a live or stillbirth delivery between March 2018 and June 2021 in a large health care system in Pennsylvania. The study was approved by the University of Pittsburgh institutional review board. Informed consent was waived because the research constituted minimal risk. All patients presenting for delivery were verbally screened for substance use using questions adapted from the National Institute on Drug Abuse Quick Screen. 4 Policy specified UTT would be performed for those with a positive screen result, history of substance use in the year prior to delivery, few prenatal visits, or abruption or stillbirth without a clear medical explanation.

We studied 2 binary outcomes: the receipt of UTT (point of care presumptive testing) and a positive test result at delivery. The primary variable of interest, patient race, was conceptualized as a social construct that could manifest in biased or discriminatory delivery of health care. Self-reported race was categorized as Black, White, and other (Alaska Native, American Indian, Chinese, Filipino, Guam/Chamorro Hawaiian, Indian, Japanese, Korean, Other Asian/Pacific Islander, Samoan, and Vietnamese). Substance use history was defined as having a diagnosis of an alcohol, cannabis, opioid, or stimulant use or disorder during pregnancy in the EMR within 1 year prior through delivery. A positive UTT result was defined as at least 1 positive result of a test component, including amphetamines, barbiturates, benzodiazepines, buprenorphine, cocaine, cannabis, methadone, opiates, or phencyclidine. We used multivariable logistic regression models including race and substance use history, adjusting for age, Hispanic or Latina/x ethnicity, marital status, parity, tobacco use, prenatal visit utilization, stillbirth, and placental abruption. We derived mean predicted probabilities of outcomes by race and substance use history. 5 Analyses were conducted using Stata, version 17.

Among 37 860 patients (100% female; mean [SD] age, 29.8 [5.5] years), 16% Black, 76% were White, and 8% were other race ( Table ). Overall, 11% had a history of substance use; opioid use was more common among White patients (40% of all substance use), whereas cannabis use was most common among Black patients (86% of all substance use). The mean predicted probability of having a UTT at delivery was highest among Black patients compared with White patients and other racial groups regardless of history of substance use ( Figure ). For Black patients without a history of substance use, the mean predicted probability of receiving a UTT at delivery was 6.9% (95% CI, 6.4%-7.4%) vs 4.7% (95% CI, 4.4%-4.9%) among White patients. Among Black patients with a history of substance use, the mean predicted probability of receiving a UTT at delivery was 76.4% (95% CI, 74.8%-78.0%) vs 68.7% (95% CI, 67.3%-70.1%) among White patients. In contrast, among those with a history of substance use, the mean predicted probability of having a positive test result was 66.7% (95% CI, 64.8%-68.7%) among White patients and 58.3% (95% CI, 55.5%-61.1%) among Black patients.

In this cohort study, Black patients, regardless of history of substance use, had a greater probability of receiving a UTT at delivery compared with White patients and other racial groups. However, Black patients did not have a higher probability of a positive test result than other racial groups. Limitations of the study include a lack of a sufficient sample size to investigate other racial and ethnic minoritized groups, such as Alaska Native and American Indian patients, and that data were from a single geographical area and may not generalize nationally. To address racial biases, health care systems should examine drug testing practices and adhere to evidence-based practices.

Accepted for Publication: February 4, 2023.

Published: April 14, 2023. doi:10.1001/jamahealthforum.2023.0441

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Jarlenski M et al. JAMA Health Forum .

Corresponding Author: Marian Jarlenski, PhD, MPH, University of Pittsburgh School of Public Health, 130 DeSoto St, A619, Pittsburgh, PA 15261 ( [email protected] ).

Author Contributions: Dr Jarlenski and Mr Shroff had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Jarlenski, Terplan, Krans.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Jarlenski, Krans.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Shroff, Terplan, Brown-Podgorski, Krans.

Obtained funding: Jarlenski, Krans.

Administrative, technical, or material support: Krans.

Supervision: Jarlenski, Krans.

Conflict of Interest Disclosures: Dr Roberts reported receiving grants from the Foundation for Opioid Response Efforts and the University of California, San Francisco CSF Bixby Center for Global Reproductive Health and National Center of Excellence in Women's Health outside the submitted work. Dr Krans reported receiving grants from the National Institutes of Health, Merck, and Gilead outside the submitted work. No other disclosures were reported.

Funding/Support: This work was supported by grant R01DA049759 from the National Institute on Drug Abuse (Dr Jarlenski and Krans).

Role of the Funder/Sponsor: The National Institute on Drug Abuse had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement .

• Academics & Research
• Release Notes

Release 5.38

Also in eram.

• Getting Started
• eRAM Access
• Reference Materials
• System Email Notifications

Electronic Medical Record (EMR)

• For Non-USDA ONLY : An update was added that automatically closes and reopens Clinicals based on the existence of Open ATRs. For example, if a new ATR is created for the same CLN#, the Clinical will automatically re-open. The intent is to easily keep CLNs and ATRs in sync with less action by staff.
• Added a Generate DAR button to the DAR Workspace. This enables drug administration records  to be entered more quickly.

V2 Protocol

• Updated Help text links and wording on some Procedure and other pages.