The Value of Electronic Health Records Since the Health Information Technology for Economic and Clinical Health Act: Systematic Review

Shikha Modi; Sue S Feldman

doi:10.2196/37283

The Value of Electronic Health Records Since the Health Information Technology for Economic and Clinical Health Act: Systematic Review

Modi, Shikha; Feldman, Sue S 2022-09-27 00:00:00 Background: Electronic health records (EHRs) are the electronic records of patient health information created during ≥1 encounter in any health care setting. The Health Information Technology Act of 2009 has been a major driver of the adoption and implementation of EHRs in the United States. Given that the adoption of EHRs is a complex and expensive investment, a return on this investment is expected. Objective: This literature review aims to focus on how the value of EHRs as an intervention is defined in relation to the elaboration of value into 2 different value outcome categories, financial and clinical outcomes, and to understand how EHRs contribute to these 2 value outcome categories. Methods: This literature review was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The initial search of key terms, EHRs, values, financial outcomes, and clinical outcomes in 3 different databases yielded 971 articles, of which, after removing 410 (42.2%) duplicates, 561 (57.8%) were incorporated in the title and abstract screening. During the title and abstract screening phase, articles were excluded from further review phases if they met any of the following criteria: not relevant to the outcomes of interest, not relevant to EHRs, nonempirical, and non–peer reviewed. After the application of the exclusion criteria, 80 studies remained for a full-text review. After evaluating the full text of the residual 80 studies, 26 (33%) studies were excluded as they did not address the impact of EHR adoption on the outcomes of interest. Furthermore, 4 additional studies were discovered through manual reference searches and were added to the total, resulting in 58 studies for analysis. A qualitative analysis tool, ATLAS.ti. (version 8.2), was used to categorize and code the final 58 studies. Results: The findings from the literature review indicated a combination of positive and negative impacts of EHRs on financial and clinical outcomes. Of the 58 studies surveyed for this review of the literature, 5 (9%) reported on the intersection of financial and clinical outcomes. To investigate this intersection further, the category “Value–Intersection of Financial and Clinical Outcomes” was generated. Approximately 80% (4/5) of these studies specified a positive association between EHR adoption and financial and clinical outcomes. Conclusions: This review of the literature reports on the individual and collective value of EHRs from a financial and clinical outcomes perspective. The collective perspective examined the intersection of financial and clinical outcomes, suggesting a reversal of the current understanding of how IT investments could generate improvements in productivity, and prompted a new question to be asked about whether an increase in productivity could potentially lead to more IT investments. (JMIR Med Inform 2022;10(9):e37283) doi: 10.2196/37283 KEYWORDS electronic health records; EHRs; value; financial outcomes; clinical outcomes; health informatics; clinical informatics https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman to an individual is considered to be valuable to that individual, Introduction regardless of it being an action or intervention. Value is defined in multiple ways within the health care industry. Payne et al Electronic health records (EHRs) are described as electronic [13] describe value as dollars (financial), productivity (clinical), records of patient health information created by ≥1 encounter or effectiveness (clinical). Payne et al [13] also suggest that in any health care setting and include patient demographics, health IT (HIT) literature is primarily focused on productivity issues, medication information, laboratory data, radiology (process) and effectiveness (outcome), followed by dollars reports, and history [1]. EHRs enable health information (outcome). Feldman et al [14] explain value as a combination exchange, clinical decision support, diagnostic support, patient of tangible (dollars, financial) and intangible (doing the right health portals, and more [2]. EHR use has the potential to thing; trust relationships, social) components. In terms of improve the quality of care and patient safety [3] and has examining the EHR value component, another study analyzed become an important part of the modern health system because the value of EHRs in terms of efficiency (clinical) and cost of government policies, technology developments, health care savings (financial). This study further used efficiency to derive challenges, and market situations [4]. The Health Information value by looking at the quality of care and cost savings from Technology for Economic and Clinical Health (HITECH) Act better claims management and reduced payments [11]. Riskin has been a major driver of the increase in the adoption and et al [15] highlighted the national focus on health reform and implementation of EHRs [5]. defined its value in terms of improved outcomes (clinical) and The HITECH Act of 2009 was passed to decrease health care reduced costs (financial). Yeung [16] discussed EHR in terms costs, improve quality, and increase patient safety through of value as it is connected to improving services (clinical) incentives for providers (physicians) and organizations that delivered at local health departments. Hepp et al [17] evaluated provided proof of their meaningful use (MU) of certified EHR the value of EHRs by looking at EHRs as a cost-effective systems [5]. Approximately US $27 billion in incentives was strategy to improve medication safety (clinical). Adler-Milstein given to physicians and hospitals that adopted and used EHRs et al [18] analyzed different scopes of the value of EHRs by according to federally defined “meaningful use” criteria [6]. gauging process adherence (clinical), patient satisfaction Out of US $27 billion, US $406 million was allotted to Medicare (clinical), and efficiency outcomes (clinical). Advantage Organizations for eligible providers. The Center for The environment in which HIT is used may have an impact on Medicare and Medicaid Services (CMS) provided subsidy the value that is derived from HIT [19]. For example, Peterson payments of US $63,750 over 6 years for Medicaid or US et al [11] suggested that current users of EHR systems focus on $44,000 over 5 years for Medicare to individual physicians if value in terms of improving workflows and, as a result, better they used certified EHRs beginning in 2011 and exhibited MU clinical outcomes, whereas local health departments or criteria [7]. It is worth noting that in 2018, the CMS refocused community clinics may focus on value in terms of capturing MU on increasing health information exchange and patient patient information to improve the services that are provided access to data, renaming MU as Promoting Interoperability [16] or for ambulatory settings on increasing medication safety Programs. [17]. Thinking about EHRs’ value more holistically, the value Given that it has been over a decade since the HITECH Act was could equate with increased revenue and reduced cost passed, sufficient data are available to understand how EHR (financial). For patients, it could mean improved health and adoption investment adds value to the hospitals that have EHR prevention of illness (outcomes); for providers, it could signal systems in place. It is important to first define “value” to reduced errors and an increase in the efficiency of care (process); understand the value of EHR adoption from a comprehensive and for the government, it could correspond with improvements perspective. in population health through timely public health reporting and population well-being (process and outcomes) [13] When reviewing the cost and resources associated with EHR adoption, it is generally considered to be an expensive The World Health Organization defines an outcome measure investment [8,9], with an expectation of a return or value on as “a change in the health of an individual, group of people, or the investment. Typically, return on investment (ROI) is population that is attributable to an intervention or series of measured by dividing the net profit by the net investment [10]. interventions” [20]. Outcomes, in the conventional health ROI-related concerns about EHR adoption were considered to services sense, are usually regarded as clinical outcomes [21]; be a major barrier to the adoption of EHRs, primarily as the however, to represent the scope of the Triple Aim of health care, value was unknown [11]. Jang et al [9] calculated the ROI for the authors built upon the literature to broaden the definition of EHR adoption by looking at the breakeven point of EHR outcomes to include financial and social outcomes, in addition adoption investment. This study focused on 17 community to traditional clinical outcomes. primary care practices targeting the financial aspect of EHR This review of the literature aimed to describe how the value adoption but did not consider the financial aspect of multilayered of EHRs, as an intervention, is defined in relation to the decisions such as system selection, employee training, updating elaboration of value into 2 different value outcome categories, or maintaining systems, and training employees for updated financial and clinical outcomes, and by understanding the systems [11]. contributions that EHRs make to these 2 value outcome Moving beyond ROI, value can be defined as “considering categories. (someone or something) to be important or beneficial” [12]. To simplify this definition, anything that benefits or is important https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman discovered through manual reference searches and were added Methods to the total, resulting in 58 studies for analysis. Figure 1 displays this process in a flow diagram. Both authors were involved in This review was conducted using the PRISMA (Preferred the article search, selection, and review process. Reporting Items for Systematic Reviews and Meta-Analyses) [22]. This method has been used for other qualitative analyses The 58 studies selected for inclusion are exhibited in the Results of literature and is therefore regarded as a suitable method for section and are organized by outcome category. ATLAS.ti this qualitative systematic review of the literature [23,24]. To (version 8.2), a qualitative data analysis tool, was used to capture the multidisciplinary evidence in this field, the following categorize and code the final 58 studies. All studies were databases were used to conduct the initial search: PubMed, uploaded into ATLAS.ti as full-text documents with names that Scopus, and Embase. To capture the decade that followed the included the first author, year of publication, and article title. enactment of the HITECH Act, the literature published in Qualitative data analysis software was deemed fitting for this English between January 2009 and December 2019 was used type of analysis as it allows for the possibility of applying a as a filter to refine the results. The initial keywords used were recurring and reiterative approach to data analysis that is “electronic health records,” “EHR,” “value,” “financial efficient and would have been difficult to replicate using a outcomes,” and “clinical outcomes.” To ensure the spreadsheet application [25]. comprehensiveness of the literature search, all the outcome The coding process began by analyzing each article to categories were searched separately and in conjunction with understand the context in relation to how each outcome category one another. The search strings and gathered results were is defined in the literature and learn about the evaluation process extensive and lengthy and are recorded in Table 1. To optimize of the impact of EHRs on these outcome categories. For this the chance of finding relevant studies on the value of EHR from study, overarching a priori categories (financial outcomes and the financial and clinical outcomes perspective after the clinical outcomes) were used, and the studies were further enactment of the HITECH Act, the following filters were applied categorized under these 2 overarching categories. Additional to the searches: (1) keywords in the title or abstract, (2) categories that were developed included the following: published in English, (3) published in the United States only, and (4) published between 2009 and 2019, when applicable. • Financial outcomes: cost, revenue, profit margins, reimbursement, and return on assets A total of 971 articles was included in the initial literature • Clinical outcomes: productivity, workflow efficiency, screening, of which, after removing 410 (42.2%) duplicates, medical errors, patient safety, patient satisfaction, clinical 561 (57.8) were incorporated in the title and abstract screening. volume, readmission rates, length of stay (LOS), and quality During the title and abstract screening phase, articles were indicators at individual patient levels excluded from further review phase if they met any of the following criteria: (1) not relevant to the outcomes of interest, Additional categories were added as necessitated throughout (2) not relevant to EHRs, (3) nonempirical, and (4) non–peer the coding and category generation process, which was part of reviewed. After the application of the exclusion criteria, 80 the larger data analysis process. For example, introduction and studies remained for a full-text review. After evaluating the full gap categories were generated as they assisted in the writing of text of the residual 80 studies, 26 (33%) studies were excluded the introduction and gap and supplied context for this review as they did not address the impact of EHR adoption on the of literature; however, quotations included in these categories outcomes of interest. Following this, 4 additional studies were did not necessarily factor into the results presented. https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Table 1. Search strings from the literature search for the impact of electronic health records on financial and clinical outcomes (N=971). Database and keywords Results, n (%) Filters Results after apply- ing filters, n (%) PubMed ([([([(((Finance*[Title] OR monetary[Title] OR economic*[Title] OR fiscal[Ti- 193 (19.9) Years: 2009-2019; lan- 179 (18.4) tle] OR commercial[Title] OR cost[Title])) OR (Finance*[Other Term] OR guage: English monetary[Other Term] OR economic*[Other Term] OR fiscal[Other Term] OR cost[Other Term])) OR “Economics” [Mesh]]) OR ([(Clinical[Title] OR quality[Title] OR)] OR [Clinical[Other Term] OR quality[Other Term]]) AND ((((((Adopt*[Title] OR (Adopt*[Other Term]) OR implement*(Title)] OR implement*[Other Term])] AND [([(Follow-up-stud*[Title] OR prognos*[Title] OR predict*[Title] OR course[Title] OR followup-stud*[Title] OR efficacy[Ti- tle] OR complication[Title] OR chang*[Title] OR effective*[Title] OR evalu- at*[Title] OR improve*[Title] OR indicat*[Title] OR impact*[Title] OR con- sequence*[Title] OR development*[Title] OR Result*[Title] OR outcome*[Ti- tle])] OR [Follow-up-stud*(Other Term) OR prognos*[Other Term] OR pre- dict*(Other Term) OR course(Other Term) OR followup-stud*(Other Term) OR efficacy(Other Term) OR complication(Other Term) OR chang*(Other Term) OR effective*(Other Term) OR evaluat*(Other Term) OR improve*(Oth- er Term) OR indicat*(Other Term) OR impact*(Other Term) OR conse- quence*(Other Term) OR development*(Other Term) OR Result*(Other Term) OR outcome*(Other Term)]) OR “follow-up studies” (mesh)]) AND ([([Elec- tronic-health-record*(Title) OR electronic-medical-record*(Title) OR comput- erized-health-record*(Title) OR computerized-medical-record*(Title) OR EHR(Title) OR electronic-patient-record*(Title)]) OR (Electronic-health- record*[Other Term] OR electronic-medical-record*[Other Term] OR comput- erized-health-record*[Other Term] OR computerized-medical-record*[Other Term] OR EHR[Other Term] OR electronic-patient-record*[Other Term])] OR “electronic health records” [mesh]) ([“electronic health records adoption”(Title/Abstract)] OR “EHR adoption”[Ti- 0 (0) 0 (0) N/A tle/Abstract]) AND “financial outcomes”(Title/Abstract) ([“electronic health records adoption”(Title/Abstract)] OR “EHR adoption”[Ti- 39 (4) Years: 2009-2019; lan- 33 (3.4) tle/Abstract]) AND “financial”(Title/Abstract) guage: English ([“electronic health records adoption”(Title/Abstract)] OR “EHR adoption”[Ti- 1 (0.1) Years: 2009-2019; lan- 1 (0.1) tle/Abstract]) AND “clinical outcomes”(Title/Abstract) guage: English ([“electronic health records adoption”(Title/Abstract)] OR “EHR adoption”[Ti- 99 (10.2) Years: 2009-2019; lan- 89 (9.2) tle/Abstract]) AND “clinical”(Title/Abstract) guage: English Scopus (TITLE-ABS-KEY [electronic-health-record* OR electronic-medical-record* 70 (7.2) Years: 2009-2019; lan- 35 (3.6) OR computerized-health-record* OR computerized-medical-record* OR ehr guage: English; coun- OR electronic-patient-record* OR “electronic health record”] AND TITLE- try: United States ABS-KEY [finance* OR monetary OR economic* OR fiscal OR “economic”] AND TITLE-ABS-KEY [clinical OR quality] AND TITLE-ABS-KEY [“fol- low-cup studies” OR follow-up-stud* OR prognos* OR predict* chang* OR effective* OR evaluat* OR improve* OR indicat* OR impact* OR conse- quence* OR outcome*] AND TITLE-ABS-KEY [Adopt* OR implement*]) TITLE-ABS-KEY (“EHR adoption” OR “electronic health records adoption” 0 (0) N/A 0 (0) AND “financial outcomes”) TITLE-ABS-KEY (“EHR adoption” OR “electronic health records adoption” 61 (6.3) Years: 2009-2019; lan- 41 (4.2) AND “financial”) guage: English; coun- try: United States TITLE-ABS-KEY (“ehr adoption” OR “electronic health records adoption” 2 (0.2) Years: 2009-2019; lan- 2 (0.2) AND “clinical outcomes”) guage: English; coun- try: United States TITLE-ABS-KEY (“EHR adoption” OR “electronic health records adoption” 173 (17.8) Years: 2009-2019; lan- 155 (16) AND “clinical”) guage: English; coun- try: United States Embase https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Database and keywords Results, n (%) Filters Results after apply- ing filters, n (%) (“electronic health record*”:ti,ab,kw OR “electronic medical record*”:ti,ab,kw 350 (36) Years: 2009-2019 303 (31.2) OR “computerized health record*”:ti,ab,kw OR “computerized medical record*”:ti,ab,kw OR ehr:ti,ab,kw OR “electronic patient record*”:ti,ab,kw OR “electronic health record”:ti,ab,kw) AND (finance*:ti,ab,kw OR mone- tary:ti,ab,kw OR economic*:ti,ab,kw OR fiscal:ti,ab,kw OR “econom- ic”:ti,ab,kw) AND (clinical:ti,ab,kw OR quality:ti,ab,kw) AND (“follow-up studies”:ti,ab,kw OR “follow up stud*”:ti,ab,kw OR prognos*:ti,ab,kw OR predict*:ti,ab,kw OR course:ti,ab,kw OR “followup stud*”:ti,ab,kw OR effica- cy:ti,ab,kw OR complication:ti,ab,kw OR chang*:ti,ab,kw OR effec- tive*:ti,ab,kw OR evaluat*:ti,ab,kw OR imptove*:ti,ab,kw OR indicat*:ti,ab,kw OR impact*:ti,ab,kw OR consequence*:ti,ab,kw OR development*:ti,ab,kw OR result*:ti,ab,kw OR outcome*:ti,ab,kw) AND (adopt*:ti,ab,kw OR imple- ment*:ti,ab,kw) (“electronic health records adoption”:ti,ab,kw OR “ehr adoption”:ti,ab,kw) 0 (0) N/A 0 (0) AND “financial outcomes”:ti,ab,kw (“electronic health records adoption”:ti,ab,kw OR “ehr adoption”:ti,ab,kw) 42 (4.3) Years: 2009-2019 35 (3.6) AND “financial”:ti,ab,kw (“electronic health records adoption”:ti,ab,kw OR “ehr adoption”:ti,ab,kw) 3 (0.3) Years: 2009-2019 3 (0.3) AND “clinical outcomes”:ti,ab,kw (“electronic health records adoption”:ti,ab,kw OR “ehr adoption”:ti,ab,kw) 104 (10.7) Years: 2009-2019 95 (9.8) AND “clinical”:ti,ab,kw N/A: not applicable. https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram [22]. EHR: electronic health record. Outcomes” category. Different measures of financial outcomes Results were used in these studies, such as cost [26-29], revenue [28,29], profit margins [8,27], reimbursement [30], and return on assets Information from the reviewed articles (n=58) was analyzed to [8]. These different financial outcome measures are described ascertain how the value of EHRs is determined regarding and detailed in Table 2. The included studies contained positive financial and clinical outcomes relative to how they are defined (17/58, 81%), negative (4/58, 19%), and no (3/58, 14%) earlier in this paper. In addition, findings from this review of association relationships between EHR adoption and financial the literature describe how EHR adoption affects each outcome outcomes. There were overlapping positive and negative impacts category. of EHR adoption on financial outcomes in some of the reviewed Financial Outcomes studies. Of the 58 studies reviewed, 21 (36%) studies incorporated segments that were coded under the “Value-Financial https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Table 2. Reviewed studies on the impact of EHR adoption and financial and clinical outcomes. Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Adler-Milstein et Health Services Re- AHA IT Supplement To examine the relation- Efficiency (measured by ✓ al [18] search Survey (2008-2011), ship between EHR adop- the ratio of a hospital’s AHA Annual Survey tion and hospital out- total expenditures to ad- comes justed patient days), pro- (2009-2012), CMS cess adherence, and pa- Hospital Compare data tient satisfaction set (2009-2012), and CMS EHR Incentive Program Reports Appari et al [31] The American Journal Cross-sectional retrospec- To determine whether Adverse event indicators ✓ d e of Managed Care tive study, data on hospi- HIT systems are associ- developed by AHRQ tal patient safety perfor- ated with better patient (death among surgical mance (2008-2010) com- safety in acute care set- patients with serious, bined with IT systems tings treatable complications; data (2007; n=3002 non- collapsed lung that re- federal acute care hospi- sults from medical treat- tals) ment [iatrogenic pneu- mothorax]; breathing failure after surgery [postoperative respiratory failure]; blood clots in the lung or a large vein after surgery [postopera- tive pulmonary embolism or deep venous thrombo- sis]; wounds that split open after surgery [post- operative wound dehis- cence]; accidental cuts and tears [accidental puncture or laceration]; death after surgery to re- pair a weakness in the abdominal aorta [abdom- inal aortic aneurysm mortality rate]; and death among patients with hip fractures [hip fracture mortality rate]) Bae et al [32] BioMed Central National Ambulatory To analyze the impact of Duration measured in ✓ Health Services Re- Medical Care Survey EHRs on primary care minutes of the face-to- search (37,962 patient visits to physicians’ workloads face encounter between 1470 primary care physi- physicians and patients cians from 2006 to 2009) (patient face time) for di- rect patient care during the office visit and num- ber of total patient office visits per physician per week (patient volume) Behkami et al [33] Studies in Health Simulation of clinic-type To describe a framework Revenue ✓ Technology and Infor- scenarios to capture the that allows decision- matics dynamic nature of policy makers to efficiently interventions that affect evaluate factors that af- the adoption of EHR fect EHR adoption and test financial incentives https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Bishop et al [34] Health Affairs Interviews of medical To understand how prima- The convenience of ac- ✓ group leaders (n=21) ry care practices can use cess, patient satisfaction, who use electronic com- electronic communica- efficiency, safety and munication with patients tion to manage clinical quality of care, and extensively and staff issues that are usually workload from 6 of the groups managed during clinic visits; determine per- ceived advantages and disadvantages of the electronic communica- tion programs for pa- tients, physicians, and practices; and determine the barriers to and facili- tators of the implementa- tion of the electronic communication programs Brown Jr et al [29] Journal of Addiction Data collected from pa- To evaluate the impact of Financial performance ✓ ✓ Medicine per patient charts (for (revenue), quality (timeli- an EMR system on the preimplementation data) ness of medical assess- Opioid Agonist Treat- and electronic patient ments), productivity ment Program charts (for postimplemen- (clinic visits), patient sat- tation data); patients, isfaction, and risk man- clinicians, and manage- agement (incident re- ment stakeholders partic- ports) ipated in surveys Bucher et al [35] Journal of the Ameri- To analyze the impact of Hospital compliance with ✓ CMS SCIP measuring can College of Sur- EHR adoption on hospi- SCIP core measures compliance rates; geons tal compliance with qual- HIMSS hospital EHR ity and process measures adoption survey from 2006 to 2012 Burke et al [36] Journal of Innovation Notes of outpatients with To analyze the impact of HbA values ✓ 1c in Health Informatics type 2 diabetes analyzed EHR use on clinical (n=537) for 5.5 years quality measures and HbA 1c Cheriff et al [37] International Journal The practice management To describe the changes Average monthly charge, ✓ ✓ of Medical Informat- system used to extract in physician productivity visit volume, and work- ics physician productivity in an academic multispe- relative value units data (n=203) cialty group because of ambulatory EHR adop- tion Chiang et al [38] Journal of American Academic pediatric oph- To analyze the impact of Clinical volume ✓ Association for Pedi- thalmology practice data EHR implementation on atric Ophthalmology for the year 2006 (n=4 the volume and time for and Strabismus faculty providers) pediatric ophthalmology Chiang et al [39] Transactions of the Outpatient clinical exam- To evaluate clinical vol- Clinical volume, time re- ✓ American Ophthalmo- inations (n=120,490) ume, time requirements, quirements, and nature of logical Society from faculty providers and nature of clinical clinical documentation (n=23) at an academic documentation related to ophthalmology depart- EHR implementation ment analyzed for 3 years Choi et al [40] Journal of Medical Retrospective chart re- To analyze the organiza- Documentation of medi- ✓ Systems view study—a conve- tional performance and cation and patient status nience sample of 60 to 80 regulatory compliance charts reviewed every before and after imple- month from (January 1, mentation of the Anesthe- 2006, to October 4, 2009, sia Information Manage- n=3997; October 5, 2009, ment System to December 31, 2010, n=984) https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Collum et al [8] Healthcare Manage- To examine how EHR Profit margins and return ✓ AHA Annual survey ment Review adoption affects hospital on assets (2007-2010), AHA IT financial performance Supplement (2007-2010), and CMS Medicare Cost Reports (2007-2011) Dandu et al [41] Clinical Orthopedics Data were collected from To evaluate the impact of Billing, outpatient vol- ✓ ✓ and Related Research a combination of the EHRs on provider produc- ume, and surgical volume Physician Compare data tivity, billing, and ortho- set (2016), Meaningful pedic surgery Use Eligible Professional public use files (2011- 2016), and Medicare Uti- lization and Payment da- ta sets (2012-2016) Daniel et al [42] Academic Emergency Health plan and electron- To evaluate the use of Plan payment for ED en- ✓ ✓ Medicine ic hospital data from a paper-based EHR in an counters and ED LOS k l large urban ED ED on LOS and plan (November 1, 2004, to payments March 31, 2005, n=1509 ED encounters compared with September 1, 2005, to February 17, 2006, n=779 ED encounters) Deily et al [43] Health Research and Administrative claims To examine whether HIT Incidence of obstetric ✓ Educational Trust data in Pennsylvania at nonhospital facilities trauma and preventable from 1998 to 2004 improves health out- complications; LOS (n=491,832) comes and decreases re- source use at hospitals within the same network and whether the effect of HIT differs as providers obtain more experience with it Edwardson et al Medical Care Re- Financial panel data from To examine the effect of Average per-patient ✓ [44] search and Review the pediatric primary care EHR adoption on charge charge, average per-pa- network comprising 260 capture tient collections, and providers across 42 prac- charge-to-collection ra- tices (2008-2013) tios Ehrlich et al [45] Applied Clinical Infor- Survey responses from To comprehend and de- Documentation quality, ✓ matics 32 ophthalmologists after scribe the perceptions of workflow, and efficiency implementation, 28 at 3 ophthalmologists during months, 35 at 7 months, EHR implementation in 40 at 13 months, and 39 an academic department at 24 months after imple- of ophthalmology mentation (implementa- tion in 2012) Flatow et al [46] Applied Clinical Infor- Retrospective chart re- To evaluate key quality LOS, mortality, central ✓ matics view for all patients ad- measures of a surgical line–associated blood- mitted to the surgical in- intensive care unit follow- stream infection rates, tensive care unit ing EHR implementation clostridium difficile coli- (n=3742; January 1, in a tertiary hospital tis rates, readmission 2009, to December 31, rates, and number of 2010) coded diagnoses Furukawa et al Journal of the Ameri- Data collected from To evaluate the impact of Rate of adverse drug ✓ [47] can Medical Informat- Medicare Patient Safety meaningful use capabili- events ics Association Monitoring System ties on in-hospital ad- (2010-2013) and HIMSS verse drug events Analytics database (2008-2013) https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Han et al [48] American Journal of A prospective observa- To determine the effect MICU mortality, hospital ✓ the Medical Sciences tional study (n=797 pa- of EHR on MICU mortal- LOS, and medication er- tients) at an urban teach- ity, hospital LOS, and rors ing hospital from July medication errors 2010 to June 2011 in the MICU Hepp et al [17] Value in Health The decision-analytic To assess the cost-effec- Costs (CPOE system ✓ ✓ model was used to esti- tiveness of CPOE in the costs, personnel costs, mate the cost-effective- reduction of medication administrative costs, and errors and adverse drug prescribing costs), finan- ness of CPOE in a multi- events in an ambulatory cial incentives (Health disciplinary medical setting Information Technology group for the years 2010 for Economic and Clini- to 2014 (n=400 cal Health meaningful providers) use incentives and pay- for-performance incen- tives), medication error probability, and adverse drug event probability Herasevich et al Critical Care A prospective study at To design and test an Prevalence of acute lung ✓ [49] Medicine Mayo Clinic, Rochester, electronic algorithm that injury Minnesota (n=1159 pa- includes patient character- tients) from February 16, istics and ventilator set- 2008, to February 16, tings, allowing notifica- 2009 tion to bedside providers about potentially injuri- ous ventilator settings to improve the safety of ventilator care and de- crease the risk of ventila- tor-related lung injury Hessels et al [50] Online Journal of Data on 854,258 adult To examine the relation- Prolonged LOS and pa- ✓ Nursing Informatics patients discharged from ship between the EHR tient satisfaction 70 New Jersey hospitals adoption stage, missed and 7679 nurses working nursing care, nursing in those same hospitals practice environment, for the year 2006 and adverse outcomes and satisfaction of pa- tients who are hospital- ized Howley et al [51] Journal of the Ameri- Compared practice pro- To evaluate how EHR Reimbursement and ✓ ✓ can Medical Informat- ductivity and reimburse- implementation affects practice productivity ics Association ment of ambulatory prac- the financial performance (number of patient visits) tices (n=30) for 2 years of ambulatory practices after EHR implementa- tion to their per-EHR im- plementation baseline Jones et al [52] American Journal of Database with 2021 hos- To analyze longitudinal Composite measures of ✓ Managed Care pitals collected by link- data on EHR adoption to hospital process quality ing the AHA Annual evaluate the impact of for acute myocardial in- Survey database, Hospi- new EHR adoption on farction, health failure, tal Compare database, quality improvement and pneumonia and HIMSS database for the years 2004 and 2007 Katzer et al [53] Applied Clinical Infor- Prehospital patient care To describe whether im- Mean physical exam ✓ matics reports (n=154) at plementing an electronic documentation Georgetown University’s patient care report system student-run Emergency influenced improvement Medical Services organi- in physical exam docu- zation mentation https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Kritz et al [54] Journal of Evaluation Opioid treatment pro- Prospective, comparative Revenue, quality, produc- ✓ ✓ in Clinical Practice gram clinics (7 clinics) in study using a pre- and tivity, risk management, New York State—paper postimplementation de- and satisfaction patient charts and elec- sign to establish whether tronic patient charts (to EHR implementation analyze pre- and postim- yielded any improve- plementation data), as- ments sessment meetings and surveys with patients, di- rect care providers, and supervisors or managers Lam et al [55] BioMed Central Data from physicians To analyze the impact of Patient volume per ✓ Health Services Re- with practices at the Uni- EHR adoption on patient provider search versity of Washington visit volume at an aca- Department of Ophthal- demic ophthalmology mology for the years department 2008 to 2012 (n=8 physi- cians) Lim et al [28] Journal of American Population-based, cross- To evaluate the adoption Net revenues and produc- ✓ ✓ Medical Association sectional study (n=348) rate and perceptions of tivity Ophthalmology financial and clinical outcomes of EHRs among ophthalmologists in the United States Love et al [3] Journal of American 2007 state-wide survey To characterize and de- Medical errors, quality of ✓ Medical Informatics of Massachusetts physi- scribe physicians’ atti- care, and physician satis- Association cians (n=541) tudes toward EHR’s po- faction tential to cause new er- rors, improve health care quality, and change physician satisfaction Lowe et al [56] Journal of Wound Os- Data were collected from To evaluate the impact of Documentation of wound ✓ tomy Continence a regional Veterans Af- a 1-year intervention of care and documentation Nurses Society fairs database and com- an EMR wound care of coding for diagnoses puterized patient medical template on the complete- and procedures records for a year after ness of wound care docu- implementation of the mentation and medical EMR wound care tem- coding and compare re- plate (October 1, 2006, sults with the preinterven- to September 30, 2007) tion period and 2 years before the in- tervention https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 11 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) McCullough et al Health Affairs AHA Annual Survey, To analyze the impact of Quality indicators: per- ✓ [57] HIMSS Analytics, and HIT on the quality of centage of patients with CMS Hospital Compare care in US hospitals heart failure given an- database for the years giotensin-converting en- 2004 to 2007 (n=3401 zyme inhibitor or an- nonfederal acute care US giotensin II receptor hospitals) blocker for left ventricu- lar systolic dysfunction; the percentage of smok- ers with heart failure and pneumonia who were given smoking cessation advice; the percentage of patients with pneumonia assessed and given pneu- mococcal vaccination if indicated; the percentage of patients with pneumo- nia whose initial blood culture in the ED preced- ed their first dose of the hospital-administered an- tibiotics; and the percent- age of patients with pneumonia given the most appropriate initial antibiotic McCullough et al Generating Evidence Manual review of the pa- To analyze the quality Clinical quality mea- ✓ [58] and Methods to im- per and electronic charts measure performance in sures: antithrombotic prove patient out- for 6007 patients across small practices before therapy, BMI recorded, comes) 35 small primary care and after EHR adoption smoking status recorded, practices smoking cessation inter- vention offered, HbA 1c testing and control, cholesterol testing and control, and BP control Mirani and ACM Transactions on “Data and Reports” and To analyze the impact of The average cost of ancil- ✓ ✓ Harpalani [27] Management Informa- “Hospital Cost Report” the Medicare EHR incen- lary services per patient, tion Systems from the CMS website tive program on acute profit margins, inpatient for 2008 to 2010 care hospitals bed debts, outpatient bed debts, and patient stay durations Mitchell et al [59] The Journal of Rural AHA EHR adoption sur- To investigate whether Percentage of hospitals ✓ Health vey and CMS Hospital there is an association meeting quality require- Compare data set for the between clinical decision ments and pneumonia year 2009 support system use and process composite scores quality disparities in pneumonia process indi- cators between rural and urban hospitals Patterson et al [60] Applied Clinical Infor- Data used from the AHA To compare 30 days all- 30-day all-cause readmis- ✓ matics Health IT survey and cause readmission rates sion rates Medicare Part A claims for Medicare patients (n=52,048 Medicare ben- with health failure dis- eficiaries discharged for charged from hospitals heart failure anytime with fully implemented during the calendar year comprehensive EHR vs 2008) without it https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Persell et al [61] Medical Care Time series analysis at a To implement and ana- Quality measures pertain- ✓ large internal medicine lyze a multifaceted quali- ing to coronary heart dis- practice from February 1, ty improvement interven- ease, health failure, dia- 2007, to February 1, tion using EHRs as tools betes mellitus, and pre- 2009 (n=12,299 patients for improving perfor- vention eligible at the beginning mance of the intervention) Radley et al [62] Journal of American Systematic literature re- To analyze the adoption Likelihood of medication ✓ Medical Informatics view and random-effects of CPOE systems on the errors Association meta-analytic techniques, reduction in medication American Society of errors in hospitals Health System Pharma- cists Annual survey (2007), AHA Annual Survey (2007), and AHA EHR Adoption Database supplement (2008) Rao et al [63] Journal of American Mailed surveys to a na- To analyze variation in Physician perceptions of ✓ Medical Informatics tionally representative the adoption of EHR quality of clinical deci- Association random sample of practic- functionalities and their sion, quality of communi- ing physicians from the use patterns, barriers to cation with patients and Physician Masterfile of adoption, and perceived other providers, delivery the American Medical benefits by physician of preventive or chronic Association (n=2769) practice size care that met the guide- lines, avoiding medica- tion errors and prescrip- tion refills Risko et al [64] Healthcare Patient processing met- To analyze the impact of Patient workup times and ✓ rics (n=374 observations) EHR implementation on LOS were collected for ED ED physician efficiency physicians (34 physi- and patient throughput cians) at 2 hospitals for 7 months before and 10 months after EHR imple- mentation Ryan et al [65] Medical Care Data collected from 143 To analyze whether EHR Quality of care was ana- ✓ practices with EHR im- implementation and lyzed from 8 separate in- plementation (2009- complementary interven- dicators; 4 cardiovascular 2011) tions, such as clinical de- measures (smoking cessa- cision support, technical tion intervention, BP assistance, and financial control, cholesterol con- incentives improved, the trol, and aspirin or an- quality of care provided tithrombotic treatment) and 4 additional clinical- ly important measures (BMI measurement, HbA control, pneumo- 1c coccal vaccine, and asth- ma control) Schreiber and Sha- Journal of Innovation Data collected from a To evaluate whether an LOS and cost measured ✓ ✓ ha [66] in Health Informatics community hospital for increase in adoption of by LOS 5 years after CPOE CPOE leads to a decrease adoption in LOS Scott et al [67] The Journal of Bone Data collected from an To evaluate the impact of Labor cost, documenta- ✓ ✓ and Joint Surgery outpatient adult recon- EMR implementation us- tion time for providers, struction clinic (n=143 ing advanced cost-ac- and time spent interact- patients) before imple- counting methods on or- ing with patients menting the hospital sys- thopedic surgeons in an tem–wide EMR system outpatient setting and 2 months, 6 months, and 2 years after imple- mentation https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 13 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Shen et al [68] International Journal National Inpatient Sam- To examine how EHR Cost of care for the 8 ✓ ✓ of Healthcare Technol- ple and AHA EHR imple- adoption affected the cost quality indicators (cardio- ogy and Management mentation survey for the of care and quality out- vascular and cerebrovas- year 2009 comes in an acute care cular) and quality indica- hospital setting tors for 5 cardiovascular and 3 cerebrovascular conditions and proce- dures Silow-Carroll et al Issue Brief (Common- Interviews with individu- To analyze the experi- Communication among ✓ [69] wealth Fund) als in the 9 hospitals that ence of 9 hospitals in us- providers, care coordina- implemented a compre- ing EHR to improve tion, patient engagement, hensive EHR system quality and efficiency and medical errors Singh et al [70] Journal of American Retrospective case-con- To evaluate the impact of Net revenue, revenue to ✓ ✓ Medical Association trol study comparing the EHR system implementa- volume ratio, capital and Ophthalmology pre- (n=13,969 patient tion from clinical and implementation costs, encounters) and post- economic perspectives at EHR incentive payments EHR (n=14,191 patient a large multidisciplinary received, patient volume, encounters) implementa- ophthalmic practice diagnostic and procedure tion periods at an eye in- volume, and coding vol- stitute umes Sockolow et al Applied Clinical Infor- Pre- and postobservation- To compare workflows, Number of days required ✓ ✓ [30] matics al mixed methods study, financial billing, and pa- to create a financial reim- Philadelphia-based tient outcomes before bursement bill, productiv- homecare agency with and after implementation ity, behavioral outcomes, 137 clinicians—data in- to analyze the effect of a and clinicians’ percep- cluded clinician EHR homecare point of care tions of patient safety documentation comple- EHR tion, EHR use data, Medicare billing data, an EHR Nurse Satisfaction survey, clinician observa- tions, clinician inter- views, and patient out- comes Thirukumaran et al Health Services Re- Data collected from the To evaluate the effect of SCIP scores ✓ [71] search SCIP Core Measure data EHR placement on SCIP set from the CMS Hospi- measures in a tertiary tal Inpatient Quality Re- care teaching hospital porting (n=1816) pro- gram (March 2010 to February 2012) Tidwell et al [72] Obstetrics and Gyne- Data collected from an To evaluate whether a Net profit, days in ac- ✓ ✓ cology obstetrics and gynecolo- low-cost electronic prac- counts receivables, pa- gy practice comprising 6 tice management system tient visits, no-show rate, physicians and 6 mid- (EHR) can improve care and quality data gather- wives with 150 daily vis- coordination and finan- ing its cial measures Varpio et al [73] Medical Education A 2-phase longitudinal To evaluate the impact of Clinician experience was ✓ study; data collected adopting EHR on clini- measured in terms of through field observa- cian experience cognitive workload, clin- tions (146 hours with 300 ical reasoning support providers, 22 patients, mechanisms, and knowl- and 32 patient family edge about the patient members), think-aloud (n=13) and think-after (n=11) sessions, inter- views (n=39) and docu- ment retrieval (n=392) https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 14 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Walker-Czyz et al Journal of Nursing Data for a quantitative, To evaluate how an inte- Cost (nurse hours per pa- ✓ ✓ [74] Administration retrospective analysis grated EHR innovation tient day, nurse turnover, collected from urban adoption affects cost, and nurse overtime), hospitals (431 beds) with nurse satisfaction, and quality nursing care out- 10 medical-surgical units nursing care delivered in comes (hospital-acquired and 2 critical care units terms of quality falls and pressure ulcers, ventilator-associated pneumonia, central line–associated blood- stream infections, and catheter-associated uri- nary tract infections) Wang et al [75] Preventing Chronic Clinical quality measure To analyze how clinical 4 key quality measures: ✓ Disease performance data collect- quality measures for inde- antithrombotic therapy, ed from 151 primary care pendent primary care BP control, HbA test- 1c practices that implement- practices improve as a ing, and smoking cessa- ed EHR (October 2009 result of EHR use and tion intervention to October 2011) technical support from a local public health agen- cy Wang et al [26] International Journal Definitive health care da- To evaluate how HIT ex- Return on assets, produc- ✓ ✓ of Accounting Informa- ta set for hospital-level penses and intermediate tivity ([net revenue, 1 tion Systems data for the years 2011 to business processes affect million]), and number of 2016 (n=3266 observa- hospital financial perfor- staff beds) tions) mance and productivity Xiao et al [76] Perspectives in Health Charts were reviewed to To describe how electron- Note completion and ✓ Information Manage- collect data from a large ic charting implementa- documentation of medica- ment tertiary public medical tion in a large public out- tion center (3 years before patient clinic improves and 3 years after EHR clinical documentation implementation in July 2009) Yeung [16] International Journal 433 local health depart- To determine the impact The health of a popula- ✓ of Medical Informat- ments’ population-based of the adoption of EHR tion at the county level, ics data for 433 counties and health information as measured by health exchange changes by lo- outcomes such as prema- cal health departments on ture death and health-re- population health lated quality of life Wani and Malhotra Journal of Operations Acute care hospitals in To analyze the impact of LOS and readmission ✓ [77] Management California EHR adoption in terms rates of full adoption vs mean- ingful assimilation on clinical outcomes https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 15 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Zhou et al [78] Journal of the Ameri- To evaluate the extent of Quality measures are ag- Quality measures aggre- ✓ can Medical Informat- EHR use and how the gregated into 6 clinical gated into 6 clinical cate- ics Association quality of care delivered categories (asthma care, gories (asthma care, be- in ambulatory care prac- behavioral and mental havioral and mental tices varied according to health, cancer screening, health, cancer screening, the duration of EHR diabetes care, well-child diabetes care, well child availability and adolescent visits, and and adolescent visit, women’s health screen- women’s health screen- ings) ings) EHR: electronic health record. CMS: Centers for Medicare and Medicaid Services. ✓: indicates that the outcome was discussed in the study. HIT: health IT. AHRQ: Agency for Healthcare Research and Quality. EMR: electronic medical record. SCIP: Surgical Care Improvement Project. HIMSS: Healthcare Information and Management Systems Society. HbA : hemoglobin A . 1c 1c AHA: American Hospital Association. ED: emergency department. LOS: length of stay. MICU: medical intensive care unit. CPOE: certified provider order entry. BP: blood pressure. Most of the studies included in this review of the literature had EHR adoption [29,51]. A few studies reported improved financial outcome measures that demonstrated some form of financial performance concerning savings [42], net profit, and improvement. One of the studies reported that costs that days in account receivables [72] as a result of EHR adoption. increased during the implementation period were equivalent to One of the studies examined the association among HIT the preimplementation level after 6 months [67]. Hepp et al [17] expenses, hospital financial performance, and productivity, with found that the certified physician order entry (CPOE) system EHR adoption being an intermediate variable. This study (part of the EHR system) generated lower costs in addition to indicated a direct and positive association between HIT improving medication safety. A few other studies also confirmed investment and positive financial performance regarding return that patients in facilities with EHR systems incurred lower costs on assets [26]. than those in facilities without an EHR system [54,68,69]. By contrast, a set of results from a survey of ophthalmologists In terms of mixed financial outcomes, the analysis of indicated increasing costs and decreasing revenue and Adler-Milstein et al [18] exhibited that greater EHR adoption productivity with the adoption of EHRs [28]. Other studies have did not improve financial efficiency (measured by the ratio of similarly reported findings in terms of a decrease in revenue a hospital’s total expenditures to adjusted patient days) for [54,70] and an increase in cost [29] as a result of EHR adoption. nonfederal acute care hospitals immediately after the adoption Dandu et al [41] did not provide any statistically significant of EHR; however, the results from this study reported evidence to report a direct association between EHR adoption improvements in financial efficiency for the years 2010 and and higher-level billing [41]. Similarly, Mirani and Harpalani 2011 compared with the years 2008 and 2009 [18]. [27] did not provide any statistically significant evidence to report a direct association between EHR adoption and revenue. Regarding the reimbursement measure, EHR systems were Findings from Collum et al [8] suggested that alterations in the thought to be responsible for significant improvements in the level of EHR adoption were not related to increases in revenue timeliness of clinical documentation and billing for and the reduction of operating margins. reimbursement [30,41,76]. The analysis of Cheriff et al [37] documented that physicians who adopted EHRs in a large Clinical Outcomes academic multispecialty physician group captured higher Of the 58 reviewed studies, 55 (95%) contained segments that average monthly charges than before the use of EHRs. Similarly, were coded under the category of “Value-Clinical Outcomes.” another study reported that the introduction of EHRs was The differing measures for clinical outcomes in these studies associated with an increase in average per-patient charge and were productivity [26,28,30], workflow inefficiency, medical an increase in average per-patient collection [44]. errors, patient safety [3], patient satisfaction, clinical volume, readmission rates, patient LOS [27], and quality indicators at In terms of revenues, profit margins, and return on assets, the individual patient level. The different measures of clinical revenues were reported to have increased in conjunction with https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 16 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman outcomes are listed and described in depth in Table 2. The [28,34,45]. EHR implementation was reportedly associated with studies detailed both positive (33/58, 57%), negative (16/58, increased documentation effort and time, with little to no 28%), and no (7/58, 12%) association relationships between increase in clinical volume and little to no or perhaps a negative EHR adoption and clinical outcomes. Similar to financial impact on clinical and surgical volume [38,39,41]. Increased outcomes, an overlap of both positive and negative impacts documentation time because of EHR adoption resulted in a pertaining to EHR adoption on clinical outcomes was observed decrease in the time spent reviewing patient records and in some of the studies. performing physical examinations [67]. The results from one of the studies did not identify any differences in productivity Most of the clinical outcome measures involved in this review (total visit volume) resulting from EHR adoption [70]; however, exhibited some form of improvement. The Hessels et al [50] 3% (2/58) of other studies detailed a decrease in productivity study reported a statistically significant association between immediately following the adoption of EHR [51,64]. Another EHR adoption and LOS. A significant reduction of LOS in example includes significant and consistent decreases in patient emergency departments [42] and medical errors in emergency volume spanning 4 years after EHR adoption in an academic and critical care departments [48,49], as well as inpatient acute outpatient ophthalmology practice [55]. EHR systems were said care settings [62], were indicated as a result of EHR adoption. to increase the number of missed assessments, decrease the The rising and falling CPOE rates were also determined to be timely completion rate of assessments, and negatively affect in correlation with the increase and decrease in LOS [66]. the productivity of clinicians [54]. A study reported that In connection with workflow efficiency and productivity, EHR physicians were mostly checking boxes to complete the EHR use was reportedly helpful in improving the promptness of data process instead of developing or using investigative clinical documentation [30], enhancing productivity and strategies, which are common among diagnosticians [73]. efficiency in the workloads of primary care physicians [32], Considering the patient satisfaction, quality, safety, LOS, and and increasing productivity [37]. Furthermore, EHR was found readmission rate perspectives, EHR use resulted in lower patient to be responsible for an increase in patient visits (which results satisfaction [79] and quality of care [71] for a few years in increased revenue), a decrease in no-show rates (also following the adoption of EHRs. In addition, EHR use was increasing revenue), and improved care coordination [72]. There associated with an increase in hospital-acquired conditions was statistically significant progress in terms of completion during EHR implementation [74]. No relationship was found rates of assessments [29,54], better documentation of to exist between practice size and the impact of EHR on the medication, patients’ vital signs and pain scores [40], and quality of patient care from the perspectives of physicians [63]. improved clinical documentation [53,56,76] as a result of EHR Some studies reported no association between EHR adoption adoption. and improvement in the quality of care provided [36,52,68,78], For the category of patient satisfaction, physicians recognized readmission rates [60], and LOS [48]. Findings from another electronic communication permitted through EHR as a secure study that examined physician perceptions of EHRs indicated and efficient way of communicating with patients, resulting in that physicians believed that EHRs could create new improvements in patient satisfaction [34]. A study discovered opportunities for error [3]. evidence that higher levels of EHR adoption were positively The Intersection of Financial and Clinical Outcomes associated with performance and patient satisfaction. This study Having reported on studies that examined financial and clinical detected improvements in performance and patient satisfaction outcomes as individual factors, we now report on studies that for the years 2010 and 2011 compared with the years 2008 and examined both financial and clinical outcomes. 2009 [18]. Overall, 9% (5/58) of studies surveyed for this review of the With regard to patient safety and medical errors, surgical IT literature reported on the intersection of financial and clinical systems (as a subset of EHR systems) positively affected levels outcomes. To further investigate this intersection, the category of patient safety, compliance, and quality and process measures “Value–Intersection of Financial and Clinical Outcomes” was for patients undergoing surgical procedures in hospitals [31,35]. generated. Furthermore, 80% (4/5) of these studies specified a Outside of surgical IT systems, clinical decision support has positive association between EHR adoption and financial and also been shown to address other areas of patient safety [59]. clinical outcomes. For example, adverse drug events decreased by 20% [47], and CPOE was reported to provide exceptional value by improving In terms of the financial outcomes, hospitals that had adopted medication safety in a cost-effective manner [17]. EHR selectively increased the efficiency of their turnover rate of Medicare patients to receive higher MU incentives [27]. Indicators of quality at the individual patient level, such as rates These findings point toward the impact of EHR adoption on a of antithrombotic therapy and nicotine use documentation, patient’s stay duration on average (clinical outcome), which, in increased immediately following EHR adoption [58]. Similarly, turn, affects their compensation because of the loss of patient another study reported improvements in antibiotic therapy, days (financial outcome) from CMS. EHR adoption was blood pressure control, hemoglobin A testing, and smoking 1c associated with enabling the prioritization of improvements in cessation interventions because of EHR systems [75]. clinical documentation time to improve agency cash flow [30]. In contrast, for productivity and workload efficiency, the results EHR use was thought to contribute to shortened emergency of a survey indicated that physicians perceived that EHR department LOS, which led to a positive impact in terms of adoption harmed productivity and increased their workload CMS compensation [42]. Similarly, CPOE, a subset of EHR, https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 17 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman was said to be an independent factor in the impact of LOS; was also responsible for an increase in personnel costs in therefore, it indirectly contributed to lower costs [66]. By association with the new technology’s initial steep learning contrast, 20% (1/5) of the studies reported that EHR adoption curve [67]. Overall, these studies indicated interdependence required a learning period, where increased medical assistant between financial and clinical outcomes, in essence, how one time, patient time, and physician documentation time incurred was associated with the other in some form. additional costs [67]. This review of the literature discovered some studies with contradictory findings. For example, financial outcomes such Discussion as profit margins, return on assets, and costs were some of the measures that reported contradictory findings. A potential reason Principal Findings could be that the studies that reported an inverse relationship The primary goal of this literature review was to substantiate reviewed these measures right after the adoption, as opposed how EHR value is described concerning 2 different outcome to studies that reported it after a longer period. Organizational categories, financial and clinical outcomes, and to further the performance measures such as return on assets, ROI, and return exploration of the impact of EHR adoption on these 2 outcome on equity could be examined to explore the cyclical relationship categories. Subsequently, this review incorporated studies that between IT inputs and productivity [80]. Future research may described relationships between EHR adoption along with be required to investigate the trajectory and extent of the financial and clinical outcomes with a priori categories (financial relationship between IT investments and reinvestments, such outcomes and clinical outcomes) and with an additional category as EHR adoption or readoption, and clinical outcomes to further that included the intersection of financial and clinical outcomes. expand upon this question. This review of the literature included a total of 58 studies. Limitations Overall, 76% (16/21) of the studies that discussed the financial The comprehensive findings of this literature review should be outcomes of EHR adoption presented a positive relationship considered along with the limitations. Concerning the searched between EHR adoption and financial outcomes. These studies databases, PubMed, Scopus, and Embase—the primary health observed changes in financial outcomes in terms of profit ratios, services and HIT databases—were used. It is possible that costs, revenues, reimbursements, and return on assets. Consistent studies on the value of EHRs were published outside of with the literature, value realization, especially in terms of health-focused journals and if so, may not have been included financial outcomes, is lagging as it involves a large upfront cost in this literature review. Another limitation of this review [18]. involves the keywords used in the selection criteria of the article Regarding clinical outcomes, 76% (35/58) of the studies that search process. It is possible that the used keywords were not examined the clinical outcomes of EHR adoption indicated a exhaustive, and studies could have been overlooked. Finally, positive relationship between EHR adoption and clinical this review included English-only studies that were conducted outcomes in terms of LOS, readmission rates, patient in the United States. It is possible that other countries with EHRs satisfaction, medical errors, patient safety, user productivity, may have had an experiential understanding that could have and quality indicators at individual patient levels. Similar to contributed to this review. To mitigate bias, manual screening financial outcomes, value realization regarding clinical outcomes of all the references of included studies was conducted. also improved over time. For instance, clinical outcome Conclusions measures such as rates of hemoglobin A testing, recorded 1c This review of the literature reports on the individual and BMI, and cholesterol testing decreased before rebounding, collective value of EHRs from a financial and clinical outcomes following the adoption of EHR [57]. perspective. The collective perspective examined the intersection Of the 58 studies in this review of the literature, 5 (9%) studies of financial and clinical outcomes, suggesting a reversal of the highlighted the intersection of financial and clinical outcomes. current understanding of how IT investments could generate EHR adoption allowed for improvements in clinical productivity improvements, and prompted a new question to be documentation time and LOS and sequentially reduced overall asked about whether an increase in productivity could potentially costs and improved reimbursement [27,30,42,66]. EHR adoption lead to more IT investments. Conflicts of Interest None declared. References 1. Electronic Health Records. Healthcare Information and Management Systems Society. 2011. URL: https://www.himss.org/ library/ehr [accessed 2022-09-12] 2. Garrett P, Seidman J. EMR vs EHR – What is the Difference? Health IT Buzz. 2011 Jan 4. URL: https://www.healthit.gov/ buzz-blog/electronic-health-and-medical-records/emr-vs-ehr-difference [accessed 2022-09-12] 3. Love JS, Wright A, Simon SR, Jenter CA, Soran CS, Volk LA, et al. Are physicians' perceptions of healthcare quality and practice satisfaction affected by errors associated with electronic health record use? J Am Med Inform Assoc 2012;19(4):610-614 [FREE Full text] [doi: 10.1136/amiajnl-2011-000544] [Medline: 22199017] https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 18 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman 4. Redd TK, Read-Brown S, Choi D, Yackel TR, Tu DC, Chiang MF. Electronic health record impact on productivity and efficiency in an academic pediatric ophthalmology practice. J AAPOS 2014 Dec;18(6):584-589 [FREE Full text] [doi: 10.1016/j.jaapos.2014.08.002] [Medline: 25456030] 5. McAlearney AS, Sieck C, Hefner J, Robbins J, Huerta TR. Facilitating ambulatory electronic health record system implementation: evidence from a qualitative study. Biomed Res Int 2013;2013:629574 [FREE Full text] [doi: 10.1155/2013/629574] [Medline: 24228257] 6. Adler-Milstein J, Green CE, Bates DW. A survey analysis suggests that electronic health records will yield revenue gains for some practices and losses for many. Health Aff (Millwood) 2013 Mar;32(3):562-570. [doi: 10.1377/hlthaff.2012.0306] [Medline: 23459736] 7. Medicare and Medicaid Promoting Interoperability Program Basics. Centers for Medicare & Medicaid Services. 2018. URL: https://www.cms.gov [accessed 2019-01-16] 8. Collum TH, Menachemi N, Sen B. Does electronic health record use improve hospital financial performance? Evidence from panel data. Health Care Manage Rev 2016;41(3):267-274. [doi: 10.1097/HMR.0000000000000068] [Medline: 26052785] 9. Jang Y, Lortie MA, Sanche S. Return on investment in electronic health records in primary care practices: a mixed-methods study. JMIR Med Inform 2014 Sep 29;2(2):e25 [FREE Full text] [doi: 10.2196/medinform.3631] [Medline: 25600508] 10. Pine R, Tart K. Return on investment: benefits and challenges of baccalaureate nurse residency program. Nurs Econ 2007;25(1):13-19. [Medline: 17402673] 11. Peterson LT, Ford EW, Eberhardt J, Huerta TR, Menachemi N. Assessing differences between physicians' realized and anticipated gains from electronic health record adoption. J Med Syst 2011 Apr;35(2):151-161. [doi: 10.1007/s10916-009-9352-z] [Medline: 20703574] 12. value definition. Oxford Dictionary. 2019. URL: https://en.oxforddictionaries.com/definition/value [accessed 2019-01-16] 13. Payne TH, Bates DW, Berner ES, Bernstam EV, Covvey HD, Frisse ME, et al. Healthcare information technology and economics. J Am Med Inform Assoc 2013;20(2):212-217 [FREE Full text] [doi: 10.1136/amiajnl-2012-000821] [Medline: 22781191] 14. Feldman SS, Horan TA. Collaboration in electronic medical evidence development: a case study of the Social Security Administration's MEGAHIT System. Int J Med Inform 2011 Aug;80(8):e127-e140. [doi: 10.1016/j.ijmedinf.2011.01.012] [Medline: 21333588] 15. Riskin L, Koppel R, Riskin D. Re-examining health IT policy: what will it take to derive value from our investment? J Am Med Inform Assoc 2015 Mar;22(2):459-464. [doi: 10.1136/amiajnl-2014-003065] [Medline: 25326600] 16. Yeung T. Local health department adoption of electronic health records and health information exchanges and its impact on population health. Int J Med Inform 2019 Aug;128:1-6. [doi: 10.1016/j.ijmedinf.2019.04.011] [Medline: 31160006] 17. Hepp Z, Forrester SH, Roth J, Wirtz HS, Devine EB. Cost-effectiveness of a computerized provider order entry system in improving medication safety: a case study in ambulatory care. Value Health 2013 May 1;16(3):A205-A206. [doi: 10.1016/j.jval.2013.03.1038] 18. Adler-Milstein J, Everson J, Lee SD. EHR adoption and hospital performance: time-related effects. Health Serv Res 2015 Dec;50(6):1751-1771 [FREE Full text] [doi: 10.1111/1475-6773.12406] [Medline: 26473506] 19. Shah GH, Leider JP, Castrucci BC, Williams KS, Luo H. Characteristics of local health departments associated with implementation of electronic health records and other informatics systems. Public Health Rep 2016;131(2):272-282 [FREE Full text] [doi: 10.1177/003335491613100211] [Medline: 26957662] 20. World Health Organization. 2019. URL: https://www.who.int/en/ [accessed 2022-09-12] 21. Velentgas P, Dreyer NA, Nourjah P, Smith SR, Torchia MM. Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide. Rockville, MD, USA: Agency for Healthcare Research and Quality; 2013. 22. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med 2009 Jul 21;6(7):e1000100 [FREE Full text] [doi: 10.1371/journal.pmed.1000100] [Medline: 19621070] 23. Fond G, Hamdani N, Kapczinski F, Boukouaci W, Drancourt N, Dargel A, et al. Effectiveness and tolerance of anti-inflammatory drugs' add-on therapy in major mental disorders: a systematic qualitative review. Acta Psychiatr Scand 2014 Mar;129(3):163-179. [doi: 10.1111/acps.12211] [Medline: 24215721] 24. Pichler G, Cheung PY, Aziz K, Urlesberger B, Schmölzer GM. How to monitor the brain during immediate neonatal transition and resuscitation? A systematic qualitative review of the literature. Neonatology 2014;105(3):205-210 [FREE Full text] [doi: 10.1159/000357162] [Medline: 24481411] 25. Friese S. Qualitative Data Analysis with ATLAS.ti. 2nd edition. Thousand Oaks, CA, USA: Sage Publications; 2014. 26. Wang T, Wang Y, McLeod A. Do health information technology investments impact hospital financial performance and productivity? Int J Account Inf Syst 2018 Mar;28:1-13. [doi: 10.1016/j.accinf.2017.12.002] 27. Mirani R, Harpalani A. Business benefits or incentive maximization? Impacts of the medicare EHR incentive program at acute care hospitals. ACM Trans Manage Inf Syst 2013 Dec;4(4):1-19. [doi: 10.1145/2543900] https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 19 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman 28. Lim MC, Boland MV, McCannel CA, Saini A, Chiang MF, Epley KD, et al. Adoption of electronic health records and perceptions of financial and clinical outcomes among ophthalmologists in the United States. JAMA Ophthalmol 2018 Feb 01;136(2):164-170 [FREE Full text] [doi: 10.1001/jamaophthalmol.2017.5978] [Medline: 29285542] 29. Brown Jr LS, Kritz S, Lin M, Zavala R. Evaluation of an electronic medical record system at an opioid agonist treatment program. J Addict Med 2014;8(2):96-101 [FREE Full text] [doi: 10.1097/ADM.0000000000000018] [Medline: 24562402] 30. Sockolow PS, Bowles KH, Adelsberger MC, Chittams JL, Liao C. Impact of homecare electronic health record on timeliness of clinical documentation, reimbursement, and patient outcomes. Appl Clin Inform 2014 Apr 30;5(2):445-462 [FREE Full text] [doi: 10.4338/ACI-2013-12-RA-0106] [Medline: 25024760] 31. Appari A, Johnson EM, Anthony DL. Information technology and hospital patient safety: a cross-sectional study of US acute care hospitals. Am J Manag Care 2014 Nov;20(11 Spec No. 17):eSP39-eSP47 [FREE Full text] [Medline: 25811818] 32. Bae J, Encinosa WE. National estimates of the impact of electronic health records on the workload of primary care physicians. BMC Health Serv Res 2016 May 10;16:172 [FREE Full text] [doi: 10.1186/s12913-016-1422-6] [Medline: 27160147] 33. Behkami NA, Dorr DA, Morrice S. A business case for HIT adoption: effects of "meaningful use" EHR financial incentives on clinic revenue. Stud Health Technol Inform 2010;160(Pt 1):779-783 [FREE Full text] [Medline: 20841792] 34. Bishop TF, Press MJ, Mendelsohn JL, Casalino LP. Electronic communication improves access, but barriers to its widespread adoption remain. Health Aff (Millwood) 2013 Aug;32(8):1361-1367 [FREE Full text] [doi: 10.1377/hlthaff.2012.1151] [Medline: 23918479] 35. Bucher BT, Swords DS, Robinson J, Jackson GP, Finlayson SR. Advanced electronic health record adoption improves hospital compliance with surgical care improvement project core measures. J Am Coll Surgeons 2016 Oct;223(4):e33. [doi: 10.1016/j.jamcollsurg.2016.08.088] 36. Burke HB, Becher DA, Hoang A, Gimbel RW. The adoption of an electronic health record did not improve A1c values in type 2 diabetes. J Innov Health Inform 2016 Apr 15;23(1):144 [FREE Full text] [doi: 10.14236/jhi.v23i1.144] [Medline: 27348484] 37. Cheriff AD, Kapur AG, Qiu M, Cole CL. Physician productivity and the ambulatory EHR in a large academic multi-specialty physician group. Int J Med Inform 2010 Jul;79(7):492-500. [doi: 10.1016/j.ijmedinf.2010.04.006] [Medline: 20478738] 38. Chiang MF, Read-Brown S, Tu DC, Beaudet K, Yackel TR. Electronic health record implementation in pediatric ophthalmology: impact on volume and time. J AAPOS 2013 Feb 1;17(1):e3. [doi: 10.1016/j.jaapos.2012.12.011] 39. Chiang MF, Read-Brown S, Tu DC, Choi D, Sanders DS, Hwang TS, et al. Evaluation of electronic health record implementation in ophthalmology at an academic medical center (an American Ophthalmological Society thesis). Trans Am Ophthalmol Soc 2013 Sep;111:70-92 [FREE Full text] [Medline: 24167326] 40. Choi CK, Saberito D, Tyagaraj C, Tyagaraj K. Organizational performance and regulatory compliance as measured by clinical pertinence indicators before and after implementation of Anesthesia Information Management System (AIMS). J Med Syst 2014 Jan;38(1):5. [doi: 10.1007/s10916-013-0005-x] [Medline: 24424430] 41. Dandu N, Zmistowski B, Chen AF, Chapman T, Howley M. How are electronic health records associated with provider productivity and billing in orthopaedic surgery? Clin Orthop Relat Res 2019 Nov;477(11):2443-2451 [FREE Full text] [doi: 10.1097/CORR.0000000000000896] [Medline: 31389875] 42. Daniel GW, Ewen E, Willey VJ, Reese Iv CL, Shirazi F, Malone DC. Efficiency and economic benefits of a payer-based electronic health record in an emergency department. Acad Emerg Med 2010 Aug;17(8):824-833 [FREE Full text] [doi: 10.1111/j.1553-2712.2010.00816.x] [Medline: 20670319] 43. Deily ME, Hu T, Terrizzi S, Chou SY, Meyerhoefer CD. The impact of health information technology adoption by outpatient facilities on pregnancy outcomes. Health Serv Res 2013 Feb;48(1):70-94 [FREE Full text] [doi: 10.1111/j.1475-6773.2012.01441.x] [Medline: 22742682] 44. Edwardson N, Kash BA, Janakiraman R. Measuring the impact of electronic health record adoption on charge capture. Med Care Res Rev 2017 Oct;74(5):582-594. [doi: 10.1177/1077558716659408] [Medline: 27416948] 45. Ehrlich JR, Michelotti M, Blachley TS, Zheng K, Couper MP, Greenberg GM, et al. A two-year longitudinal assessment of ophthalmologists' perceptions after implementing an electronic health record system. Appl Clin Inform 2016 Oct 12;7(4):930-945 [FREE Full text] [doi: 10.4338/ACI-2016-05-RA-0075] [Medline: 27730248] 46. Flatow VH, Ibragimova N, Divino CM, Eshak DS, Twohig BC, Bassily-Marcus AM, et al. Quality outcomes in the surgical intensive care unit after electronic health record implementation. Appl Clin Inform 2015 Oct 7;6(4):611-618 [FREE Full text] [doi: 10.4338/ACI-2015-04-RA-0044] [Medline: 26767058] 47. Furukawa MF, Spector WD, Rhona Limcangco MR, Encinosa WE. Meaningful use of health information technology and declines in in-hospital adverse drug events. J Am Med Inform Assoc 2017 Jul 01;24(4):729-736 [FREE Full text] [doi: 10.1093/jamia/ocw183] [Medline: 28339642] 48. Han JE, Rabinovich M, Abraham P, Satyanarayana P, Liao TV, Udoji TN, et al. Effect of electronic health record implementation in critical care on survival and medication errors. Am J Med Sci 2016 Jun;351(6):576-581. [doi: 10.1016/j.amjms.2016.01.026] [Medline: 27238919] 49. Herasevich V, Tsapenko M, Kojicic M, Ahmed A, Kashyap R, Venkata C, et al. Limiting ventilator-induced lung injury through individual electronic medical record surveillance. Crit Care Med 2011 Jan;39(1):34-39. [doi: 10.1097/CCM.0b013e3181fa4184] [Medline: 20959788] https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 20 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman 50. Hessels A, Flynn L, Cimiotti JP, Bakken S, Gershon R. Impact of heath information technology on the quality of patient care. Online J Nurs Inform 2015;19:1 [FREE Full text] [Medline: 27570443] 51. Howley MJ, Chou EY, Hansen N, Dalrymple PW. The long-term financial impact of electronic health record implementation. J Am Med Inform Assoc 2015 Mar;22(2):443-452. [doi: 10.1136/amiajnl-2014-002686] [Medline: 25164255] 52. Jones SS, Adams JL, Schneider EC, Ringel JS, McGlynn EA. Electronic health record adoption and quality improvement in US hospitals. Am J Manag Care 2010 Dec;16(12 Suppl HIT):SP64-SP71 [FREE Full text] [Medline: 21314225] 53. Katzer R, Barton DJ, Adelman S, Clark S, Seaman EL, Hudson KB. Impact of implementing an EMR on physical exam documentation by ambulance personnel. Appl Clin Inform 2012 Jul 25;3(3):301-308 [FREE Full text] [doi: 10.4338/ACI-2012-03-RA-0008] [Medline: 23646077] 54. Kritz S, Brown LS, Chu M, John-Hull C, Madray C, Zavala R, et al. Electronic medical record system at an opioid agonist treatment programme: study design, pre-implementation results and post-implementation trends. J Eval Clin Pract 2012 Aug;18(4):739-745 [FREE Full text] [doi: 10.1111/j.1365-2753.2011.01664.x] [Medline: 21414112] 55. Lam JG, Lee BS, Chen PP. The effect of electronic health records adoption on patient visit volume at an academic ophthalmology department. BMC Health Serv Res 2016 Jan 13;16:7 [FREE Full text] [doi: 10.1186/s12913-015-1255-8] [Medline: 26762304] 56. Lowe JR, Raugi GJ, Reiber GE, Whitney JD. Does incorporation of a clinical support template in the electronic medical record improve capture of wound care data in a cohort of veterans with diabetic foot ulcers? J Wound Ostomy Continence Nurs 2013;40(2):157-162 [FREE Full text] [doi: 10.1097/WON.0b013e318283bcd8] [Medline: 23466720] 57. McCullough JS, Casey M, Moscovice I, Prasad S. The effect of health information technology on quality in U.S. hospitals. Health Aff (Millwood) 2010 Apr;29(4):647-654. [doi: 10.1377/hlthaff.2010.0155] [Medline: 20368594] 58. McCullough CM, Wang JJ, Parsons AS, Shih SC. Quality measure performance in small practices before and after electronic health record adoption. EGEMS (Wash DC) 2015 Jan 6;3(1):1131 [FREE Full text] [doi: 10.13063/2327-9214.1131] [Medline: 25848635] 59. Mitchell J, Probst J, Brock-Martin A, Bennett K, Glover S, Hardin J. Association between clinical decision support system use and rural quality disparities in the treatment of pneumonia. J Rural Health 2014;30(2):186-195. [doi: 10.1111/jrh.12043] [Medline: 24689543] 60. Patterson ME, Marken P, Zhong Y, Simon SD, Ketcherside W. Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure. Appl Clin Inform 2014 Jul 30;5(3):670-684 [FREE Full text] [doi: 10.4338/ACI-2014-01-RA-0008] [Medline: 25298808] 61. Persell SD, Kaiser D, Dolan NC, Andrews B, Levi S, Khandekar J, et al. Changes in performance after implementation of a multifaceted electronic-health-record-based quality improvement system. Med Care 2011 Feb;49(2):117-125. [doi: 10.1097/MLR.0b013e318202913d] [Medline: 21178789] 62. Radley DC, Wasserman MR, Olsho LE, Shoemaker SJ, Spranca MD, Bradshaw B. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc 2013 May 01;20(3):470-476 [FREE Full text] [doi: 10.1136/amiajnl-2012-001241] [Medline: 23425440] 63. Rao SR, Desroches CM, Donelan K, Campbell EG, Miralles PD, Jha AK. Electronic health records in small physician practices: availability, use, and perceived benefits. J Am Med Inform Assoc 2011 May 01;18(3):271-275 [FREE Full text] [doi: 10.1136/amiajnl-2010-000010] [Medline: 21486885] 64. Risko N, Anderson D, Golden B, Wasil E, Barrueto F, Pimentel L, et al. The impact of electronic health record implementation on emergency physician efficiency and patient throughput. Healthc (Amst) 2014 Sep;2(3):201-204. [doi: 10.1016/j.hjdsi.2014.06.003] [Medline: 26250507] 65. Ryan AM, McCullough CM, Shih SC, Wang JJ, Ryan MS, Casalino LP. The intended and unintended consequences of quality improvement interventions for small practices in a community-based electronic health record implementation project. Med Care 2014 Sep;52(9):826-832. [doi: 10.1097/MLR.0000000000000186] [Medline: 25100231] 66. Schreiber R, Shaha SH. Computerised provider order entry adoption rates favourably impact length of stay. J Innov Health Inform 2016 Apr 18;23(1):166 [FREE Full text] [doi: 10.14236/jhi.v23i1.166] [Medline: 27348485] 67. Scott DJ, Labro E, Penrose CT, Bolognesi MP, Wellman SS, Mather 3rd RC. The impact of electronic medical record implementation on labor cost and productivity at an outpatient orthopaedic clinic. J Bone Joint Surg Am 2018 Sep 19;100(18):1549-1556. [doi: 10.2106/JBJS.17.01339] [Medline: 30234619] 68. Shen JJ, Cochran CR, Neish S, Moseley CB, Mukalian R. Level of EHR adoption and quality and cost of care - evidence from vascular conditions and procedures. Int J Healthcare Technol Manag 2015 Jul 13;15(1):4-21. [doi: 10.1504/ijhtm.2015.070514] 69. Silow-Carroll S, Edwards JN, Rodin D. Using electronic health records to improve quality and efficiency: the experiences of leading hospitals. Issue Brief (Commonw Fund) 2012 Jul;17:1-40. [Medline: 22826903] 70. Singh RP, Bedi R, Li A, Kulkarni S, Rodstrom T, Altus G, et al. The practice impact of electronic health record system implementation within a large multispecialty ophthalmic practice. JAMA Ophthalmol 2015 Jun;133(6):668-674. [doi: 10.1001/jamaophthalmol.2015.0457] [Medline: 25880083] https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 21 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman 71. Thirukumaran CP, Dolan JG, Reagan Webster P, Panzer RJ, Friedman B. The impact of electronic health record implementation and use on performance of the Surgical Care Improvement Project measures. Health Serv Res 2015 Feb;50(1):273-289 [FREE Full text] [doi: 10.1111/1475-6773.12191] [Medline: 24965357] 72. Tidwell C, Wolfberg A, Corrigan H. Leveraging technology to improve finances and care coordination in a small rural practice. Obstetrics Gynecology 2016 May;127:54S-55S. [doi: 10.1097/01.aog.0000483870.50355.52] 73. Varpio L, Day K, Elliot-Miller P, King JW, Kuziemsky C, Parush A, et al. The impact of adopting EHRs: how losing connectivity affects clinical reasoning. Med Educ 2015 May;49(5):476-486. [doi: 10.1111/medu.12665] [Medline: 25924123] 74. Walker-Czyz A. The impact of an integrated electronic health record adoption on nursing care quality. J Nurs Adm 2016;46(7-8):366-372. [doi: 10.1097/NNA.0000000000000360] [Medline: 27379908] 75. Wang JJ, Sebek KM, McCullough CM, Amirfar SJ, Parsons AS, Singer J, et al. Sustained improvement in clinical preventive service delivery among independent primary care practices after implementing electronic health record systems. Prev Chronic Dis 2013 Aug 01;10:E130 [FREE Full text] [doi: 10.5888/pcd10.120341] [Medline: 23906330] 76. Xiao AQ, Acosta FX. Implementation and impact of psychiatric electronic medical records in a public medical center. Perspect Health Inf Manag 2016 Oct 1;13(Fall):1e [FREE Full text] [Medline: 27843422] 77. Wani D, Malhotra M. Does the meaningful use of electronic health records improve patient outcomes? J Oper Manag 2018 Jun 22;60(1):1-18. [doi: 10.1016/j.jom.2018.06.003] 78. Zhou L, Soran CS, Jenter CA, Volk LA, Orav EJ, Bates DW, et al. The relationship between electronic health record use and quality of care over time. J Am Med Inform Assoc 2009;16(4):457-464 [FREE Full text] [doi: 10.1197/jamia.M3128] [Medline: 19390094] 79. Marmor RA, Clay B, Millen M, Savides TJ, Longhurst CA. The impact of physician EHR usage on patient satisfaction. Appl Clin Inform 2018 Jan;9(1):11-14 [FREE Full text] [doi: 10.1055/s-0037-1620263] [Medline: 29298451] 80. Baker J, Song J, Jones DR. Closing the loop: empirical evidence for a positive feedback model of IT business value creation. J Strategic Inf Syst 2017 Jun;26(2):142-160. [doi: 10.1016/j.jsis.2016.12.001] Abbreviations CMS: Center for Medicare and Medicaid Services CPOE: certified physician order entry EHR: electronic health record HIT: health IT HITECH: Health Information Technology for Economic and Clinical Health LOS: length of stay MU: meaningful use PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses ROI: return on investment Edited by C Lovis; submitted 14.02.22; peer-reviewed by M Pinto da Costa, C Ta; comments to author 27.03.22; revised version received 10.05.22; accepted 31.07.22; published 27.09.22 Please cite as: Modi S, Feldman SS The Value of Electronic Health Records Since the Health Information Technology for Economic and Clinical Health Act: Systematic Review JMIR Med Inform 2022;10(9):e37283 URL: https://medinform.jmir.org/2022/9/e37283 doi: 10.2196/37283 PMID: ©Shikha Modi, Sue S Feldman. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 27.09.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included. https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 22 (page number not for citation purposes) XSL FO RenderX http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JMIR Medical Informatics JMIR Publications http://www.deepdyve.com/lp/jmir-publications/the-value-of-electronic-health-records-since-the-health-information-dD9odq1z0D

Loading next page...

References (80)

Steven Kritz, L. Brown, Melissa Chu, Carlota John-Hull, Charles Madray, R. Zavala, Ben Louie (2012)
Electronic medical record system at an opioid agonist treatment programme: study design, pre-implementation results and post-implementation trends.
Journal of evaluation in clinical practice, 18 4
V. Herasevich, M. Tsapenko, M. Kojičić, Adil Ahmed, R. Kashyap, C. Venkata, Khurram Shahjehan, S. Thakur, B. Pickering, Jiajie Zhang, R. Hubmayr, O. Gajic (2011)
Limiting ventilator-induced lung injury through individual electronic medical record surveillance*
Critical Care Medicine, 39
Michael Howley, Edgar Chou, N. Hansen, P. Dalrymple (2015)
The long-term financial impact of electronic health record implementation
Journal of the American Medical Informatics Association : JAMIA, 22 2
T. Payne, D. Bates, E. Berner, E. Bernstam, H. Covvey, M. Frisse, T. Graf, R. Greenes, E. Hoffer, G. Kuperman, H. Lehmann, Louise Liang, Blackford Middleton, G. Omenn, J. Ozbolt (2013)
Healthcare information technology and economics
Journal of the American Medical Informatics Association : JAMIA, 20 2
Robert Katzer, David Barton, S. Adelman, S. Clark, Elizabeth Seaman, Korin Hudson (2012)
Impact of implementing an EMR on physical exam documentation by ambulance personnel
Applied Clinical Informatics, 03
N. Behkami, D. Dorr, Stuart Morrice (2010)
A Business case for HIT Adoption: effects of "meaningful use" EHR financial incentives on clinic revenue
Studies in health technology and informatics, 160 Pt 1
L. Varpio, Kathy Day, Pat Elliot-Miller, James King, C. Kuziemsky, A. Parush, T. Roffey, J. Rashotte (2015)
The impact of adopting EHRs: how losing connectivity affects clinical reasoning
Medical Education, 49
L. Brown, Steven Kritz, Melissa Lin, R. Zavala (2014)
Evaluation of an Electronic Medical Record System at an Opioid Agonist Treatment Program
Journal of Addiction Medicine, 8
Z. Hepp, S. Forrester, J. Roth, Heidi Wirtz, E. Devine (2013)
Cost-Effectiveness Of A Computerized Provider Order Entry System In Improving Medication Safety: A Case Study In Ambulatory Care
Value in Health, 16
Tara Bishop, M. Press, Jayme Mendelsohn, L. Casalino (2013)
Electronic communication improves access, but barriers to its widespread adoption remain.
Health affairs, 32 8
Rishi Singh, Rumneek Bedi, Ang Li, Sharmila Kulkarni, Tiffany Rodstrom, G. Altus, Daniel Martin (2015)
The Practice Impact of Electronic Health Record System Implementation Within a Large Multispecialty Ophthalmic Practice.
JAMA ophthalmology, 133 6
G. Fond, N. Hamdani, F. Kapczinski, W. Boukouaci, N. Drancourt, A. Dargél, José Oliveira, E. Guen, E. Marlinge, R. Tamouza, M. Leboyer (2014)
Effectiveness and tolerance of anti‐inflammatory drugs' add‐on therapy in major mental disorders: a systematic qualitative review
Acta Psychiatrica Scandinavica, 129
M. Deily, Tianyan Hu, Sabrina Terrizzi, Shin-Yi Chou, C. Meyerhoefer (2013)
The impact of health information technology adoption by outpatient facilities on pregnancy outcomes.
Health services research, 48 1
Christina Zimmerman (2012)
The effect of health information technology on quality in U.S. hospitals.
Findings brief : health care financing & organization, 15 3
Jaeyong Bae, W. Encinosa (2016)
National estimates of the impact of electronic health records on the workload of primary care physicians
BMC Health Services Research, 16
G. Pichler, P. Cheung, K. Aziz, B. Urlesberger, G. Schmölzer (2014)
How to Monitor the Brain during Immediate Neonatal Transition and Resuscitation: A Systematic Qualitative Review of the Literature
Neonatology, 105
MD MicheleC.Lim, MDPhD MichaelV.Boland, MD ColinA.McCannel, MD ArvindSaini, MD MichaelF.Chiang, MD K.DavidEpley, MD FloraLum, Jama Ophthalmology, Original Investigation (2017)
Adoption of Electronic Health Records and Perceptions of Financial and Clinical Outcomes Among Ophthalmologists in the United States
JAMA Ophthalmology, 136
Jason Wang, K. Sebek, Colleen McCullough, Sam Amirfar, Amanda Parsons, Jesse Singer, S. Shih (2013)
Sustained Improvement in Clinical Preventive Service Delivery Among Independent Primary Care Practices After Implementing Electronic Health Record Systems
Preventing Chronic Disease, 10
Jenny Han, M. Rabinovich, P. Abraham, P. Satyanarayana, Vivan Liao, T. Udoji, G. Cotsonis, E. Honig, G. Martin (2016)
Effect of Electronic Health Record Implementation in Critical Care on Survival and Medication Errors
The American Journal of the Medical Sciences, 351
Jocelyn Lam, Bryan Lee, Philip Chen (2015)
The effect of electronic health records adoption on patient visit volume at an academic ophthalmology department
BMC Health Services Research, 16
(2011)
EMR vs EHR – What is the Difference
P. Sockolow, K. Bowles, M. Adelsberger, J. Chittams, C. Liao (2014)
Impact of Homecare Electronic Health Record on Timeliness of Clinical Documentation, Reimbursement, and Patient Outcomes
Applied Clinical Informatics, 05
Anna Xiao, F. Acosta (2016)
Implementation and Impact of Psychiatric Electronic Medical Records in a Public Medical Center.
Perspectives in health information management, 13 Fall
Loren Riskin, R. Koppel, D. Riskin (2015)
Re-examining health IT policy: what will it take to derive value from our investment?
Journal of the American Medical Informatics Association : JAMIA, 22 2
D. Scott, Eva Labro, Colin Penrose, M. Bolognesi, S. Wellman, R. Mather (2018)
The Impact of Electronic Medical Record Implementation on Labor Cost and Productivity at an Outpatient Orthopaedic Clinic
The Journal of Bone and Joint Surgery, 100
Colleen McCullough, Jason Wang, Amanda Parsons, S. Shih (2015)
Quality Measure Performance in Small Practices Before and After Electronic Health Record Adoption
eGEMs, 3
D. Radley, M. Wasserman, Lauren Olsho, Sarah Shoemaker, M. Spranca, B. Bradshaw (2013)
Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems
Journal of the American Medical Informatics Association : JAMIA, 20
Deepa Wani, M. Malhotra (2018)
Does the meaningful use of electronic health records improve patient outcomes
Journal of Operations Management, 60
Tiankai Wang, Yangmei Wang, Alexander McLeod (2018)
Do health information technology investments impact hospital financial performance and productivity?
Int. J. Account. Inf. Syst., 28
V. Flatow, N. Ibragimova, C. Divino, D. Eshak, Bridget Twohig, A. Bassily-Marcus, R. Kohli-Seth (2015)
Quality Outcomes in the Surgical Intensive Care Unit after Electronic Health Record Implementation
Applied Clinical Informatics, 06
A. Hessels, L. Flynn, Jeannie Cimiotti, S. Bakken, R. Gershon (2015)
Impact of Heath Information Technology on the Quality of Patient Care.
On-line journal of nursing informatics, 19
Jordan Mitchell, J. Probst, Amy Brock-Martin, K. Bennett, S. Glover, J. Hardin (2014)
Association between clinical decision support system use and rural quality disparities in the treatment of pneumonia.
The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association, 30 2
R. Schreiber, S. Shaha (2016)
Computerised provider order entry adoption rates favourably impact length of stay
BMJ Health & Care Informatics, 23
Tina Yeung (2019)
Local health department adoption of electronic health records and health information exchanges and its impact on population health
International journal of medical informatics, 128
Li Zhou, Christine Soran, Chelsea Jenter, L. Volk, E. Orav, D. Bates, S. Simon (2009)
The relationship between electronic health record use and quality of care over time.
Journal of the American Medical Informatics Association : JAMIA, 16 4
Ajit Appari, Eric Johnson, D. Anthony (2014)
Information technology and hospital patient safety: a cross-sectional study of US acute care hospitals.
The American journal of managed care, 20 11 Spec No. 17
Ralph Thomas (1907)
The Oxford Dictionary
Notes and Queries
M. Patterson, P. Marken, Y. Zhong, S. Simon, W. Ketcherside (2014)
Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure.
Applied Clinical Informatics, 05
A. Liberati, D. Altman, J. Tetzlaff, C. Mulrow, P. Gøtzsche, J. Ioannidis, M. Clarke, P. Devereaux, J. Kleijnen, D. Moher (2009)
The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration
The BMJ, 339
Sharon Silow-Carroll, J. Edwards, D. Rodin (2012)
Using electronic health records to improve quality and efficiency: the experiences of leading hospitals.
Issue brief, 17
Lori Peterson, E. Ford, John Eberhardt, Timothy Huerta, N. Menachemi (2011)
Assessing Differences Between Physicians’ Realized and Anticipated Gains from Electronic Health Record Adoption
Journal of Medical Systems, 35
S. Friese (2012)
Qualitative Data Analysis with ATLAS.ti
Caroline Thirukumaran, J. Dolan, Patricia Webster, R. Panzer, B. Friedman (2015)
The impact of electronic health record implementation and use on performance of the Surgical Care Improvement Project measures.
Health services research, 50 1
Julia Adler-Milstein, Carola Green, D. Bates (2013)
A survey analysis suggests that electronic health records will yield revenue gains for some practices and losses for many.
Health affairs, 32 3
AnneMarie Walker-Czyz (2016)
The Impact of an Integrated Electronic Health Record Adoption on Nursing Care Quality
JONA: The Journal of Nursing Administration, 46
A. Cheriff, Akshay Kapur, Maggie Qiu, Curtis Cole (2010)
Physician productivity and the ambulatory EHR in a large academic multi-specialty physician group
International journal of medical informatics, 79 7
Nicholas Risko, David Anderson, B. Golden, E. Wasil, F. Barrueto, L. Pimentel, J. Hirshon (2014)
The impact of electronic health record implementation on emergency physician efficiency and patient throughput.
Healthcare, 2 3
(2016)
Healthcare Information and Management Systems Society Announces the Alliance for Nursing Informatics Selection of 2016-2017 Emerging Leaders: Nurses and Mentors Linked Through Informatics Professional Development Program.
CIN: Computers, Informatics, Nursing
R. Marmor, B. Clay, Marlene Millen, T. Savides, C. Longhurst (2018)
The Impact of Physician EHR Usage on Patient Satisfaction
Applied Clinical Informatics, 09
M. Chiang, Sarah Read-Brown, Daniel Tu, Dongseok Choi, David Sanders, T. Hwang, Steven Bailey, D. Karr, Elizabeth Cottle, J. Morrison, David Wilson, T. Yackel (2013)
Evaluation of electronic health record implementation in ophthalmology at an academic medical center (an American Ophthalmological Society thesis).
Transactions of the American Ophthalmological Society, 111
(1954)
THE WORLD HEALTH ORGANIZATION
Medical Journal of Australia, 2
J. Lowe, G. Raugi, G. Reiber, J. Whitney (2013)
Does Incorporation of a Clinical Support Template in the Electronic Medical Record Improve Capture of Wound Care Data in a Cohort of Veterans With Diabetic Foot Ulcers?
Journal of Wound, Ostomy and Continence Nursing, 40
N. Edwardson, B. Kash, R. Janakiraman (2017)
Measuring the Impact of Electronic Health Record Adoption on Charge Capture
Medical Care Research and Review, 74
Sue Feldman, T. Horan (2011)
Collaboration in electronic medical evidence development: A case study of the Social Security Administration's MEGAHIT System
International journal of medical informatics, 80 8
G. Daniel, E. Ewen, V. Willey, Charles Iv, F. Shirazi, D. Malone (2010)
Efficiency and economic benefits of a payer-based electronic health record in an emergency department.
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, 17 8
Rajesh Mirani, Anju Harpalani (2013)
Business Benefits or Incentive Maximization? Impacts of the Medicare EHR Incentive Program at Acute Care Hospitals
ACM Trans. Manag. Inf. Syst., 4
Navya Dandu, Benjamin Zmistowski, Antonia Chen, Talia Chapman, Michael Howley (2019)
How are Electronic Health Records Associated with Provider Productivity and Billing in Orthopaedic Surgery?
Clinical Orthopaedics & Related Research
Julia Adler-Milstein, Jordan Everson, S. Lee (2015)
EHR Adoption and Hospital Performance: Time-Related Effects.
Health services research, 50 6
Joshua Ehrlich, Monica Michelotti, T. Blachley, K. Zheng, M. Couper, G. Greenberg, S. Kileny, Greta Branford, D. Hanauer, Jennifer Weizer (2016)
A Two-Year Longitudinal Assessment of Ophthalmologists’ Perceptions after Implementing an Electronic Health Record System
Applied Clinical Informatics, 07
Jay Shen, C. Cochran, Scott Neish, C. Moseley, Robin Mukalian (2015)
Level of EHR adoption and quality and cost of care - evidence from vascular conditions and procedures
International Journal of Healthcare Technology and Management, 15
C. Choi, Darlene Saberito, C. Tyagaraj, K. Tyagaraj (2013)
Organizational Performance and Regulatory Compliance as Measured by Clinical Pertinence Indicators Before and After Implementation of Anesthesia Information Management System (AIMS)
Journal of Medical Systems, 38
M. Furukawa, W. Spector, M. Limcangco, W. Encinosa (2017)
Meaningful use of health information technology and declines in in-hospital adverse drug events
Journal of the American Medical Informatics Association, 24
Stephen Persell, D. Kaiser, N. Dolan, B. Andrews, Sue Levi, J. Khandekar, T. Gavagan, Jason Thompson, Elisha Friesema, D. Baker (2011)
Changes in Performance After Implementation of a Multifaceted Electronic-Health-Record-Based Quality Improvement System
Medical Care, 49
G. Shah, J. Leider, B. Castrucci, Karmen Williams, Huabin Luo (2016)
Characteristics of Local Health Departments Associated with Implementation of Electronic Health Records and other Informatics Systems
Public Health Reports, 131
J. McCullough, M. Casey, I. Moscovice, S. Prasad (2010)
The effect of health information technology on quality in U.S. hospitals.
Health affairs, 29 4
R. Pine, K. Tart (2007)
Return on investment: benefits and challenges of baccalaureate nurse residency program.
Nursing economic$, 25 1
J. Love, A. Wright, S. Simon, Chelsea Jenter, Christine Soran, L. Volk, D. Bates, E. Poon (2012)
Are physicians' perceptions of healthcare quality and practice satisfaction affected by errors associated with electronic health record use?
Journal of the American Medical Informatics Association : JAMIA, 19 4
Spencer Jones, J. Adams, E. Schneider, J. Ringel, E. McGlynn (2010)
Electronic health record adoption and quality improvement in US hospitals.
The American journal of managed care, 16 12 Suppl HIT
Travis Redd, Sarah Read-Brown, Dongseok Choi, T. Yackel, Daniel Tu, M. Chiang (2014)
Electronic health record impact on productivity and efficiency in an academic pediatric ophthalmology practice.
Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus, 18 6
J. Baker, Jaeki Song, Donald Jones (2017)
Closing the loop: Empirical evidence for a positive feedback model of IT business value creation
J. Strateg. Inf. Syst., 26
A. McAlearney, Cynthia Sieck, Jennifer Hefner, J. Robbins, Timothy Huerta (2013)
Facilitating Ambulatory Electronic Health Record System Implementation: Evidence from a Qualitative Study
BioMed Research International, 2013
P. Velentgas, N. Dreyer, P. Nourjah, Scott Smith, M. Torchia (2013)
Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide
H. Burke, Dorothy Becher, A. Hoang, R. Gimbel (2016)
The adoption of an electronic health record did not improve A1c values in Type 2 diabetes
BMJ Health & Care Informatics, 23
Taleah Collum, N. Menachemi, B. Sen (2016)
Does electronic health record use improve hospital financial performance? Evidence from panel data.
Health care management review, 41 3
A. Ryan, Colleen McCullough, S. Shih, Jason Wang, M. Ryan, L. Casalino (2014)
The Intended and Unintended Consequences of Quality Improvement Interventions for Small Practices in a Community-based Electronic Health Record Implementation Project
Medical Care, 52
Yeona Jang, M. Lortie, S. Sanche (2014)
Return on Investment in Electronic Health Records in Primary Care Practices: A Mixed-Methods Study
JMIR Medical Informatics, 2
J. Halfon, Maria Small, Washington Hill, S. Rulisa (2016)
Successful Uterine Conservation in Cases of Post-Cesarean Section Peritonitis With Uterine Necrosis [13F]
Obstetrics & Gynecology, 127
M. Chiang, Sarah Read-Brown, Daniel Tu, Kimberly Beaudet, T. Yackel (2013)
Electronic health record implementation in pediatric ophthalmology: Impact on volume and time
Journal of Aapos, 17
Sowmya Rao, C. DesRoches, K. Donelan, E. Campbell, Paola Miralles, A. Jha (2011)
Electronic health records in small physician practices: availability, use, and perceived benefits
Journal of the American Medical Informatics Association : JAMIA, 18 3
B. Bucher, D. Swords, Jamie Robinson, G. Jackson, S. Finlayson (2016)
Advanced Electronic Health Record Adoption Improves Hospital Compliance with Surgical Care Improvement Project Core Measures
Journal of The American College of Surgeons, 223

Publisher: JMIR Publications
Copyright: Copyright © The Author(s). Licensed under Creative Commons Attribution cc-by 4.0
ISSN: 2291-9694
DOI: 10.2196/37283
Publisher site: See Article on Publisher Site

Abstract

Background: Electronic health records (EHRs) are the electronic records of patient health information created during ≥1 encounter in any health care setting. The Health Information Technology Act of 2009 has been a major driver of the adoption and implementation of EHRs in the United States. Given that the adoption of EHRs is a complex and expensive investment, a return on this investment is expected. Objective: This literature review aims to focus on how the value of EHRs as an intervention is defined in relation to the elaboration of value into 2 different value outcome categories, financial and clinical outcomes, and to understand how EHRs contribute to these 2 value outcome categories. Methods: This literature review was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The initial search of key terms, EHRs, values, financial outcomes, and clinical outcomes in 3 different databases yielded 971 articles, of which, after removing 410 (42.2%) duplicates, 561 (57.8%) were incorporated in the title and abstract screening. During the title and abstract screening phase, articles were excluded from further review phases if they met any of the following criteria: not relevant to the outcomes of interest, not relevant to EHRs, nonempirical, and non–peer reviewed. After the application of the exclusion criteria, 80 studies remained for a full-text review. After evaluating the full text of the residual 80 studies, 26 (33%) studies were excluded as they did not address the impact of EHR adoption on the outcomes of interest. Furthermore, 4 additional studies were discovered through manual reference searches and were added to the total, resulting in 58 studies for analysis. A qualitative analysis tool, ATLAS.ti. (version 8.2), was used to categorize and code the final 58 studies. Results: The findings from the literature review indicated a combination of positive and negative impacts of EHRs on financial and clinical outcomes. Of the 58 studies surveyed for this review of the literature, 5 (9%) reported on the intersection of financial and clinical outcomes. To investigate this intersection further, the category “Value–Intersection of Financial and Clinical Outcomes” was generated. Approximately 80% (4/5) of these studies specified a positive association between EHR adoption and financial and clinical outcomes. Conclusions: This review of the literature reports on the individual and collective value of EHRs from a financial and clinical outcomes perspective. The collective perspective examined the intersection of financial and clinical outcomes, suggesting a reversal of the current understanding of how IT investments could generate improvements in productivity, and prompted a new question to be asked about whether an increase in productivity could potentially lead to more IT investments. (JMIR Med Inform 2022;10(9):e37283) doi: 10.2196/37283 KEYWORDS electronic health records; EHRs; value; financial outcomes; clinical outcomes; health informatics; clinical informatics https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman to an individual is considered to be valuable to that individual, Introduction regardless of it being an action or intervention. Value is defined in multiple ways within the health care industry. Payne et al Electronic health records (EHRs) are described as electronic [13] describe value as dollars (financial), productivity (clinical), records of patient health information created by ≥1 encounter or effectiveness (clinical). Payne et al [13] also suggest that in any health care setting and include patient demographics, health IT (HIT) literature is primarily focused on productivity issues, medication information, laboratory data, radiology (process) and effectiveness (outcome), followed by dollars reports, and history [1]. EHRs enable health information (outcome). Feldman et al [14] explain value as a combination exchange, clinical decision support, diagnostic support, patient of tangible (dollars, financial) and intangible (doing the right health portals, and more [2]. EHR use has the potential to thing; trust relationships, social) components. In terms of improve the quality of care and patient safety [3] and has examining the EHR value component, another study analyzed become an important part of the modern health system because the value of EHRs in terms of efficiency (clinical) and cost of government policies, technology developments, health care savings (financial). This study further used efficiency to derive challenges, and market situations [4]. The Health Information value by looking at the quality of care and cost savings from Technology for Economic and Clinical Health (HITECH) Act better claims management and reduced payments [11]. Riskin has been a major driver of the increase in the adoption and et al [15] highlighted the national focus on health reform and implementation of EHRs [5]. defined its value in terms of improved outcomes (clinical) and The HITECH Act of 2009 was passed to decrease health care reduced costs (financial). Yeung [16] discussed EHR in terms costs, improve quality, and increase patient safety through of value as it is connected to improving services (clinical) incentives for providers (physicians) and organizations that delivered at local health departments. Hepp et al [17] evaluated provided proof of their meaningful use (MU) of certified EHR the value of EHRs by looking at EHRs as a cost-effective systems [5]. Approximately US $27 billion in incentives was strategy to improve medication safety (clinical). Adler-Milstein given to physicians and hospitals that adopted and used EHRs et al [18] analyzed different scopes of the value of EHRs by according to federally defined “meaningful use” criteria [6]. gauging process adherence (clinical), patient satisfaction Out of US $27 billion, US $406 million was allotted to Medicare (clinical), and efficiency outcomes (clinical). Advantage Organizations for eligible providers. The Center for The environment in which HIT is used may have an impact on Medicare and Medicaid Services (CMS) provided subsidy the value that is derived from HIT [19]. For example, Peterson payments of US $63,750 over 6 years for Medicaid or US et al [11] suggested that current users of EHR systems focus on $44,000 over 5 years for Medicare to individual physicians if value in terms of improving workflows and, as a result, better they used certified EHRs beginning in 2011 and exhibited MU clinical outcomes, whereas local health departments or criteria [7]. It is worth noting that in 2018, the CMS refocused community clinics may focus on value in terms of capturing MU on increasing health information exchange and patient patient information to improve the services that are provided access to data, renaming MU as Promoting Interoperability [16] or for ambulatory settings on increasing medication safety Programs. [17]. Thinking about EHRs’ value more holistically, the value Given that it has been over a decade since the HITECH Act was could equate with increased revenue and reduced cost passed, sufficient data are available to understand how EHR (financial). For patients, it could mean improved health and adoption investment adds value to the hospitals that have EHR prevention of illness (outcomes); for providers, it could signal systems in place. It is important to first define “value” to reduced errors and an increase in the efficiency of care (process); understand the value of EHR adoption from a comprehensive and for the government, it could correspond with improvements perspective. in population health through timely public health reporting and population well-being (process and outcomes) [13] When reviewing the cost and resources associated with EHR adoption, it is generally considered to be an expensive The World Health Organization defines an outcome measure investment [8,9], with an expectation of a return or value on as “a change in the health of an individual, group of people, or the investment. Typically, return on investment (ROI) is population that is attributable to an intervention or series of measured by dividing the net profit by the net investment [10]. interventions” [20]. Outcomes, in the conventional health ROI-related concerns about EHR adoption were considered to services sense, are usually regarded as clinical outcomes [21]; be a major barrier to the adoption of EHRs, primarily as the however, to represent the scope of the Triple Aim of health care, value was unknown [11]. Jang et al [9] calculated the ROI for the authors built upon the literature to broaden the definition of EHR adoption by looking at the breakeven point of EHR outcomes to include financial and social outcomes, in addition adoption investment. This study focused on 17 community to traditional clinical outcomes. primary care practices targeting the financial aspect of EHR This review of the literature aimed to describe how the value adoption but did not consider the financial aspect of multilayered of EHRs, as an intervention, is defined in relation to the decisions such as system selection, employee training, updating elaboration of value into 2 different value outcome categories, or maintaining systems, and training employees for updated financial and clinical outcomes, and by understanding the systems [11]. contributions that EHRs make to these 2 value outcome Moving beyond ROI, value can be defined as “considering categories. (someone or something) to be important or beneficial” [12]. To simplify this definition, anything that benefits or is important https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman discovered through manual reference searches and were added Methods to the total, resulting in 58 studies for analysis. Figure 1 displays this process in a flow diagram. Both authors were involved in This review was conducted using the PRISMA (Preferred the article search, selection, and review process. Reporting Items for Systematic Reviews and Meta-Analyses) [22]. This method has been used for other qualitative analyses The 58 studies selected for inclusion are exhibited in the Results of literature and is therefore regarded as a suitable method for section and are organized by outcome category. ATLAS.ti this qualitative systematic review of the literature [23,24]. To (version 8.2), a qualitative data analysis tool, was used to capture the multidisciplinary evidence in this field, the following categorize and code the final 58 studies. All studies were databases were used to conduct the initial search: PubMed, uploaded into ATLAS.ti as full-text documents with names that Scopus, and Embase. To capture the decade that followed the included the first author, year of publication, and article title. enactment of the HITECH Act, the literature published in Qualitative data analysis software was deemed fitting for this English between January 2009 and December 2019 was used type of analysis as it allows for the possibility of applying a as a filter to refine the results. The initial keywords used were recurring and reiterative approach to data analysis that is “electronic health records,” “EHR,” “value,” “financial efficient and would have been difficult to replicate using a outcomes,” and “clinical outcomes.” To ensure the spreadsheet application [25]. comprehensiveness of the literature search, all the outcome The coding process began by analyzing each article to categories were searched separately and in conjunction with understand the context in relation to how each outcome category one another. The search strings and gathered results were is defined in the literature and learn about the evaluation process extensive and lengthy and are recorded in Table 1. To optimize of the impact of EHRs on these outcome categories. For this the chance of finding relevant studies on the value of EHR from study, overarching a priori categories (financial outcomes and the financial and clinical outcomes perspective after the clinical outcomes) were used, and the studies were further enactment of the HITECH Act, the following filters were applied categorized under these 2 overarching categories. Additional to the searches: (1) keywords in the title or abstract, (2) categories that were developed included the following: published in English, (3) published in the United States only, and (4) published between 2009 and 2019, when applicable. • Financial outcomes: cost, revenue, profit margins, reimbursement, and return on assets A total of 971 articles was included in the initial literature • Clinical outcomes: productivity, workflow efficiency, screening, of which, after removing 410 (42.2%) duplicates, medical errors, patient safety, patient satisfaction, clinical 561 (57.8) were incorporated in the title and abstract screening. volume, readmission rates, length of stay (LOS), and quality During the title and abstract screening phase, articles were indicators at individual patient levels excluded from further review phase if they met any of the following criteria: (1) not relevant to the outcomes of interest, Additional categories were added as necessitated throughout (2) not relevant to EHRs, (3) nonempirical, and (4) non–peer the coding and category generation process, which was part of reviewed. After the application of the exclusion criteria, 80 the larger data analysis process. For example, introduction and studies remained for a full-text review. After evaluating the full gap categories were generated as they assisted in the writing of text of the residual 80 studies, 26 (33%) studies were excluded the introduction and gap and supplied context for this review as they did not address the impact of EHR adoption on the of literature; however, quotations included in these categories outcomes of interest. Following this, 4 additional studies were did not necessarily factor into the results presented. https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Table 1. Search strings from the literature search for the impact of electronic health records on financial and clinical outcomes (N=971). Database and keywords Results, n (%) Filters Results after apply- ing filters, n (%) PubMed ([([([(((Finance*[Title] OR monetary[Title] OR economic*[Title] OR fiscal[Ti- 193 (19.9) Years: 2009-2019; lan- 179 (18.4) tle] OR commercial[Title] OR cost[Title])) OR (Finance*[Other Term] OR guage: English monetary[Other Term] OR economic*[Other Term] OR fiscal[Other Term] OR cost[Other Term])) OR “Economics” [Mesh]]) OR ([(Clinical[Title] OR quality[Title] OR)] OR [Clinical[Other Term] OR quality[Other Term]]) AND ((((((Adopt*[Title] OR (Adopt*[Other Term]) OR implement*(Title)] OR implement*[Other Term])] AND [([(Follow-up-stud*[Title] OR prognos*[Title] OR predict*[Title] OR course[Title] OR followup-stud*[Title] OR efficacy[Ti- tle] OR complication[Title] OR chang*[Title] OR effective*[Title] OR evalu- at*[Title] OR improve*[Title] OR indicat*[Title] OR impact*[Title] OR con- sequence*[Title] OR development*[Title] OR Result*[Title] OR outcome*[Ti- tle])] OR [Follow-up-stud*(Other Term) OR prognos*[Other Term] OR pre- dict*(Other Term) OR course(Other Term) OR followup-stud*(Other Term) OR efficacy(Other Term) OR complication(Other Term) OR chang*(Other Term) OR effective*(Other Term) OR evaluat*(Other Term) OR improve*(Oth- er Term) OR indicat*(Other Term) OR impact*(Other Term) OR conse- quence*(Other Term) OR development*(Other Term) OR Result*(Other Term) OR outcome*(Other Term)]) OR “follow-up studies” (mesh)]) AND ([([Elec- tronic-health-record*(Title) OR electronic-medical-record*(Title) OR comput- erized-health-record*(Title) OR computerized-medical-record*(Title) OR EHR(Title) OR electronic-patient-record*(Title)]) OR (Electronic-health- record*[Other Term] OR electronic-medical-record*[Other Term] OR comput- erized-health-record*[Other Term] OR computerized-medical-record*[Other Term] OR EHR[Other Term] OR electronic-patient-record*[Other Term])] OR “electronic health records” [mesh]) ([“electronic health records adoption”(Title/Abstract)] OR “EHR adoption”[Ti- 0 (0) 0 (0) N/A tle/Abstract]) AND “financial outcomes”(Title/Abstract) ([“electronic health records adoption”(Title/Abstract)] OR “EHR adoption”[Ti- 39 (4) Years: 2009-2019; lan- 33 (3.4) tle/Abstract]) AND “financial”(Title/Abstract) guage: English ([“electronic health records adoption”(Title/Abstract)] OR “EHR adoption”[Ti- 1 (0.1) Years: 2009-2019; lan- 1 (0.1) tle/Abstract]) AND “clinical outcomes”(Title/Abstract) guage: English ([“electronic health records adoption”(Title/Abstract)] OR “EHR adoption”[Ti- 99 (10.2) Years: 2009-2019; lan- 89 (9.2) tle/Abstract]) AND “clinical”(Title/Abstract) guage: English Scopus (TITLE-ABS-KEY [electronic-health-record* OR electronic-medical-record* 70 (7.2) Years: 2009-2019; lan- 35 (3.6) OR computerized-health-record* OR computerized-medical-record* OR ehr guage: English; coun- OR electronic-patient-record* OR “electronic health record”] AND TITLE- try: United States ABS-KEY [finance* OR monetary OR economic* OR fiscal OR “economic”] AND TITLE-ABS-KEY [clinical OR quality] AND TITLE-ABS-KEY [“fol- low-cup studies” OR follow-up-stud* OR prognos* OR predict* chang* OR effective* OR evaluat* OR improve* OR indicat* OR impact* OR conse- quence* OR outcome*] AND TITLE-ABS-KEY [Adopt* OR implement*]) TITLE-ABS-KEY (“EHR adoption” OR “electronic health records adoption” 0 (0) N/A 0 (0) AND “financial outcomes”) TITLE-ABS-KEY (“EHR adoption” OR “electronic health records adoption” 61 (6.3) Years: 2009-2019; lan- 41 (4.2) AND “financial”) guage: English; coun- try: United States TITLE-ABS-KEY (“ehr adoption” OR “electronic health records adoption” 2 (0.2) Years: 2009-2019; lan- 2 (0.2) AND “clinical outcomes”) guage: English; coun- try: United States TITLE-ABS-KEY (“EHR adoption” OR “electronic health records adoption” 173 (17.8) Years: 2009-2019; lan- 155 (16) AND “clinical”) guage: English; coun- try: United States Embase https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Database and keywords Results, n (%) Filters Results after apply- ing filters, n (%) (“electronic health record*”:ti,ab,kw OR “electronic medical record*”:ti,ab,kw 350 (36) Years: 2009-2019 303 (31.2) OR “computerized health record*”:ti,ab,kw OR “computerized medical record*”:ti,ab,kw OR ehr:ti,ab,kw OR “electronic patient record*”:ti,ab,kw OR “electronic health record”:ti,ab,kw) AND (finance*:ti,ab,kw OR mone- tary:ti,ab,kw OR economic*:ti,ab,kw OR fiscal:ti,ab,kw OR “econom- ic”:ti,ab,kw) AND (clinical:ti,ab,kw OR quality:ti,ab,kw) AND (“follow-up studies”:ti,ab,kw OR “follow up stud*”:ti,ab,kw OR prognos*:ti,ab,kw OR predict*:ti,ab,kw OR course:ti,ab,kw OR “followup stud*”:ti,ab,kw OR effica- cy:ti,ab,kw OR complication:ti,ab,kw OR chang*:ti,ab,kw OR effec- tive*:ti,ab,kw OR evaluat*:ti,ab,kw OR imptove*:ti,ab,kw OR indicat*:ti,ab,kw OR impact*:ti,ab,kw OR consequence*:ti,ab,kw OR development*:ti,ab,kw OR result*:ti,ab,kw OR outcome*:ti,ab,kw) AND (adopt*:ti,ab,kw OR imple- ment*:ti,ab,kw) (“electronic health records adoption”:ti,ab,kw OR “ehr adoption”:ti,ab,kw) 0 (0) N/A 0 (0) AND “financial outcomes”:ti,ab,kw (“electronic health records adoption”:ti,ab,kw OR “ehr adoption”:ti,ab,kw) 42 (4.3) Years: 2009-2019 35 (3.6) AND “financial”:ti,ab,kw (“electronic health records adoption”:ti,ab,kw OR “ehr adoption”:ti,ab,kw) 3 (0.3) Years: 2009-2019 3 (0.3) AND “clinical outcomes”:ti,ab,kw (“electronic health records adoption”:ti,ab,kw OR “ehr adoption”:ti,ab,kw) 104 (10.7) Years: 2009-2019 95 (9.8) AND “clinical”:ti,ab,kw N/A: not applicable. https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram [22]. EHR: electronic health record. Outcomes” category. Different measures of financial outcomes Results were used in these studies, such as cost [26-29], revenue [28,29], profit margins [8,27], reimbursement [30], and return on assets Information from the reviewed articles (n=58) was analyzed to [8]. These different financial outcome measures are described ascertain how the value of EHRs is determined regarding and detailed in Table 2. The included studies contained positive financial and clinical outcomes relative to how they are defined (17/58, 81%), negative (4/58, 19%), and no (3/58, 14%) earlier in this paper. In addition, findings from this review of association relationships between EHR adoption and financial the literature describe how EHR adoption affects each outcome outcomes. There were overlapping positive and negative impacts category. of EHR adoption on financial outcomes in some of the reviewed Financial Outcomes studies. Of the 58 studies reviewed, 21 (36%) studies incorporated segments that were coded under the “Value-Financial https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Table 2. Reviewed studies on the impact of EHR adoption and financial and clinical outcomes. Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Adler-Milstein et Health Services Re- AHA IT Supplement To examine the relation- Efficiency (measured by ✓ al [18] search Survey (2008-2011), ship between EHR adop- the ratio of a hospital’s AHA Annual Survey tion and hospital out- total expenditures to ad- comes justed patient days), pro- (2009-2012), CMS cess adherence, and pa- Hospital Compare data tient satisfaction set (2009-2012), and CMS EHR Incentive Program Reports Appari et al [31] The American Journal Cross-sectional retrospec- To determine whether Adverse event indicators ✓ d e of Managed Care tive study, data on hospi- HIT systems are associ- developed by AHRQ tal patient safety perfor- ated with better patient (death among surgical mance (2008-2010) com- safety in acute care set- patients with serious, bined with IT systems tings treatable complications; data (2007; n=3002 non- collapsed lung that re- federal acute care hospi- sults from medical treat- tals) ment [iatrogenic pneu- mothorax]; breathing failure after surgery [postoperative respiratory failure]; blood clots in the lung or a large vein after surgery [postopera- tive pulmonary embolism or deep venous thrombo- sis]; wounds that split open after surgery [post- operative wound dehis- cence]; accidental cuts and tears [accidental puncture or laceration]; death after surgery to re- pair a weakness in the abdominal aorta [abdom- inal aortic aneurysm mortality rate]; and death among patients with hip fractures [hip fracture mortality rate]) Bae et al [32] BioMed Central National Ambulatory To analyze the impact of Duration measured in ✓ Health Services Re- Medical Care Survey EHRs on primary care minutes of the face-to- search (37,962 patient visits to physicians’ workloads face encounter between 1470 primary care physi- physicians and patients cians from 2006 to 2009) (patient face time) for di- rect patient care during the office visit and num- ber of total patient office visits per physician per week (patient volume) Behkami et al [33] Studies in Health Simulation of clinic-type To describe a framework Revenue ✓ Technology and Infor- scenarios to capture the that allows decision- matics dynamic nature of policy makers to efficiently interventions that affect evaluate factors that af- the adoption of EHR fect EHR adoption and test financial incentives https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Bishop et al [34] Health Affairs Interviews of medical To understand how prima- The convenience of ac- ✓ group leaders (n=21) ry care practices can use cess, patient satisfaction, who use electronic com- electronic communica- efficiency, safety and munication with patients tion to manage clinical quality of care, and extensively and staff issues that are usually workload from 6 of the groups managed during clinic visits; determine per- ceived advantages and disadvantages of the electronic communica- tion programs for pa- tients, physicians, and practices; and determine the barriers to and facili- tators of the implementa- tion of the electronic communication programs Brown Jr et al [29] Journal of Addiction Data collected from pa- To evaluate the impact of Financial performance ✓ ✓ Medicine per patient charts (for (revenue), quality (timeli- an EMR system on the preimplementation data) ness of medical assess- Opioid Agonist Treat- and electronic patient ments), productivity ment Program charts (for postimplemen- (clinic visits), patient sat- tation data); patients, isfaction, and risk man- clinicians, and manage- agement (incident re- ment stakeholders partic- ports) ipated in surveys Bucher et al [35] Journal of the Ameri- To analyze the impact of Hospital compliance with ✓ CMS SCIP measuring can College of Sur- EHR adoption on hospi- SCIP core measures compliance rates; geons tal compliance with qual- HIMSS hospital EHR ity and process measures adoption survey from 2006 to 2012 Burke et al [36] Journal of Innovation Notes of outpatients with To analyze the impact of HbA values ✓ 1c in Health Informatics type 2 diabetes analyzed EHR use on clinical (n=537) for 5.5 years quality measures and HbA 1c Cheriff et al [37] International Journal The practice management To describe the changes Average monthly charge, ✓ ✓ of Medical Informat- system used to extract in physician productivity visit volume, and work- ics physician productivity in an academic multispe- relative value units data (n=203) cialty group because of ambulatory EHR adop- tion Chiang et al [38] Journal of American Academic pediatric oph- To analyze the impact of Clinical volume ✓ Association for Pedi- thalmology practice data EHR implementation on atric Ophthalmology for the year 2006 (n=4 the volume and time for and Strabismus faculty providers) pediatric ophthalmology Chiang et al [39] Transactions of the Outpatient clinical exam- To evaluate clinical vol- Clinical volume, time re- ✓ American Ophthalmo- inations (n=120,490) ume, time requirements, quirements, and nature of logical Society from faculty providers and nature of clinical clinical documentation (n=23) at an academic documentation related to ophthalmology depart- EHR implementation ment analyzed for 3 years Choi et al [40] Journal of Medical Retrospective chart re- To analyze the organiza- Documentation of medi- ✓ Systems view study—a conve- tional performance and cation and patient status nience sample of 60 to 80 regulatory compliance charts reviewed every before and after imple- month from (January 1, mentation of the Anesthe- 2006, to October 4, 2009, sia Information Manage- n=3997; October 5, 2009, ment System to December 31, 2010, n=984) https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Collum et al [8] Healthcare Manage- To examine how EHR Profit margins and return ✓ AHA Annual survey ment Review adoption affects hospital on assets (2007-2010), AHA IT financial performance Supplement (2007-2010), and CMS Medicare Cost Reports (2007-2011) Dandu et al [41] Clinical Orthopedics Data were collected from To evaluate the impact of Billing, outpatient vol- ✓ ✓ and Related Research a combination of the EHRs on provider produc- ume, and surgical volume Physician Compare data tivity, billing, and ortho- set (2016), Meaningful pedic surgery Use Eligible Professional public use files (2011- 2016), and Medicare Uti- lization and Payment da- ta sets (2012-2016) Daniel et al [42] Academic Emergency Health plan and electron- To evaluate the use of Plan payment for ED en- ✓ ✓ Medicine ic hospital data from a paper-based EHR in an counters and ED LOS k l large urban ED ED on LOS and plan (November 1, 2004, to payments March 31, 2005, n=1509 ED encounters compared with September 1, 2005, to February 17, 2006, n=779 ED encounters) Deily et al [43] Health Research and Administrative claims To examine whether HIT Incidence of obstetric ✓ Educational Trust data in Pennsylvania at nonhospital facilities trauma and preventable from 1998 to 2004 improves health out- complications; LOS (n=491,832) comes and decreases re- source use at hospitals within the same network and whether the effect of HIT differs as providers obtain more experience with it Edwardson et al Medical Care Re- Financial panel data from To examine the effect of Average per-patient ✓ [44] search and Review the pediatric primary care EHR adoption on charge charge, average per-pa- network comprising 260 capture tient collections, and providers across 42 prac- charge-to-collection ra- tices (2008-2013) tios Ehrlich et al [45] Applied Clinical Infor- Survey responses from To comprehend and de- Documentation quality, ✓ matics 32 ophthalmologists after scribe the perceptions of workflow, and efficiency implementation, 28 at 3 ophthalmologists during months, 35 at 7 months, EHR implementation in 40 at 13 months, and 39 an academic department at 24 months after imple- of ophthalmology mentation (implementa- tion in 2012) Flatow et al [46] Applied Clinical Infor- Retrospective chart re- To evaluate key quality LOS, mortality, central ✓ matics view for all patients ad- measures of a surgical line–associated blood- mitted to the surgical in- intensive care unit follow- stream infection rates, tensive care unit ing EHR implementation clostridium difficile coli- (n=3742; January 1, in a tertiary hospital tis rates, readmission 2009, to December 31, rates, and number of 2010) coded diagnoses Furukawa et al Journal of the Ameri- Data collected from To evaluate the impact of Rate of adverse drug ✓ [47] can Medical Informat- Medicare Patient Safety meaningful use capabili- events ics Association Monitoring System ties on in-hospital ad- (2010-2013) and HIMSS verse drug events Analytics database (2008-2013) https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Han et al [48] American Journal of A prospective observa- To determine the effect MICU mortality, hospital ✓ the Medical Sciences tional study (n=797 pa- of EHR on MICU mortal- LOS, and medication er- tients) at an urban teach- ity, hospital LOS, and rors ing hospital from July medication errors 2010 to June 2011 in the MICU Hepp et al [17] Value in Health The decision-analytic To assess the cost-effec- Costs (CPOE system ✓ ✓ model was used to esti- tiveness of CPOE in the costs, personnel costs, mate the cost-effective- reduction of medication administrative costs, and errors and adverse drug prescribing costs), finan- ness of CPOE in a multi- events in an ambulatory cial incentives (Health disciplinary medical setting Information Technology group for the years 2010 for Economic and Clini- to 2014 (n=400 cal Health meaningful providers) use incentives and pay- for-performance incen- tives), medication error probability, and adverse drug event probability Herasevich et al Critical Care A prospective study at To design and test an Prevalence of acute lung ✓ [49] Medicine Mayo Clinic, Rochester, electronic algorithm that injury Minnesota (n=1159 pa- includes patient character- tients) from February 16, istics and ventilator set- 2008, to February 16, tings, allowing notifica- 2009 tion to bedside providers about potentially injuri- ous ventilator settings to improve the safety of ventilator care and de- crease the risk of ventila- tor-related lung injury Hessels et al [50] Online Journal of Data on 854,258 adult To examine the relation- Prolonged LOS and pa- ✓ Nursing Informatics patients discharged from ship between the EHR tient satisfaction 70 New Jersey hospitals adoption stage, missed and 7679 nurses working nursing care, nursing in those same hospitals practice environment, for the year 2006 and adverse outcomes and satisfaction of pa- tients who are hospital- ized Howley et al [51] Journal of the Ameri- Compared practice pro- To evaluate how EHR Reimbursement and ✓ ✓ can Medical Informat- ductivity and reimburse- implementation affects practice productivity ics Association ment of ambulatory prac- the financial performance (number of patient visits) tices (n=30) for 2 years of ambulatory practices after EHR implementa- tion to their per-EHR im- plementation baseline Jones et al [52] American Journal of Database with 2021 hos- To analyze longitudinal Composite measures of ✓ Managed Care pitals collected by link- data on EHR adoption to hospital process quality ing the AHA Annual evaluate the impact of for acute myocardial in- Survey database, Hospi- new EHR adoption on farction, health failure, tal Compare database, quality improvement and pneumonia and HIMSS database for the years 2004 and 2007 Katzer et al [53] Applied Clinical Infor- Prehospital patient care To describe whether im- Mean physical exam ✓ matics reports (n=154) at plementing an electronic documentation Georgetown University’s patient care report system student-run Emergency influenced improvement Medical Services organi- in physical exam docu- zation mentation https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Kritz et al [54] Journal of Evaluation Opioid treatment pro- Prospective, comparative Revenue, quality, produc- ✓ ✓ in Clinical Practice gram clinics (7 clinics) in study using a pre- and tivity, risk management, New York State—paper postimplementation de- and satisfaction patient charts and elec- sign to establish whether tronic patient charts (to EHR implementation analyze pre- and postim- yielded any improve- plementation data), as- ments sessment meetings and surveys with patients, di- rect care providers, and supervisors or managers Lam et al [55] BioMed Central Data from physicians To analyze the impact of Patient volume per ✓ Health Services Re- with practices at the Uni- EHR adoption on patient provider search versity of Washington visit volume at an aca- Department of Ophthal- demic ophthalmology mology for the years department 2008 to 2012 (n=8 physi- cians) Lim et al [28] Journal of American Population-based, cross- To evaluate the adoption Net revenues and produc- ✓ ✓ Medical Association sectional study (n=348) rate and perceptions of tivity Ophthalmology financial and clinical outcomes of EHRs among ophthalmologists in the United States Love et al [3] Journal of American 2007 state-wide survey To characterize and de- Medical errors, quality of ✓ Medical Informatics of Massachusetts physi- scribe physicians’ atti- care, and physician satis- Association cians (n=541) tudes toward EHR’s po- faction tential to cause new er- rors, improve health care quality, and change physician satisfaction Lowe et al [56] Journal of Wound Os- Data were collected from To evaluate the impact of Documentation of wound ✓ tomy Continence a regional Veterans Af- a 1-year intervention of care and documentation Nurses Society fairs database and com- an EMR wound care of coding for diagnoses puterized patient medical template on the complete- and procedures records for a year after ness of wound care docu- implementation of the mentation and medical EMR wound care tem- coding and compare re- plate (October 1, 2006, sults with the preinterven- to September 30, 2007) tion period and 2 years before the in- tervention https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 11 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) McCullough et al Health Affairs AHA Annual Survey, To analyze the impact of Quality indicators: per- ✓ [57] HIMSS Analytics, and HIT on the quality of centage of patients with CMS Hospital Compare care in US hospitals heart failure given an- database for the years giotensin-converting en- 2004 to 2007 (n=3401 zyme inhibitor or an- nonfederal acute care US giotensin II receptor hospitals) blocker for left ventricu- lar systolic dysfunction; the percentage of smok- ers with heart failure and pneumonia who were given smoking cessation advice; the percentage of patients with pneumonia assessed and given pneu- mococcal vaccination if indicated; the percentage of patients with pneumo- nia whose initial blood culture in the ED preced- ed their first dose of the hospital-administered an- tibiotics; and the percent- age of patients with pneumonia given the most appropriate initial antibiotic McCullough et al Generating Evidence Manual review of the pa- To analyze the quality Clinical quality mea- ✓ [58] and Methods to im- per and electronic charts measure performance in sures: antithrombotic prove patient out- for 6007 patients across small practices before therapy, BMI recorded, comes) 35 small primary care and after EHR adoption smoking status recorded, practices smoking cessation inter- vention offered, HbA 1c testing and control, cholesterol testing and control, and BP control Mirani and ACM Transactions on “Data and Reports” and To analyze the impact of The average cost of ancil- ✓ ✓ Harpalani [27] Management Informa- “Hospital Cost Report” the Medicare EHR incen- lary services per patient, tion Systems from the CMS website tive program on acute profit margins, inpatient for 2008 to 2010 care hospitals bed debts, outpatient bed debts, and patient stay durations Mitchell et al [59] The Journal of Rural AHA EHR adoption sur- To investigate whether Percentage of hospitals ✓ Health vey and CMS Hospital there is an association meeting quality require- Compare data set for the between clinical decision ments and pneumonia year 2009 support system use and process composite scores quality disparities in pneumonia process indi- cators between rural and urban hospitals Patterson et al [60] Applied Clinical Infor- Data used from the AHA To compare 30 days all- 30-day all-cause readmis- ✓ matics Health IT survey and cause readmission rates sion rates Medicare Part A claims for Medicare patients (n=52,048 Medicare ben- with health failure dis- eficiaries discharged for charged from hospitals heart failure anytime with fully implemented during the calendar year comprehensive EHR vs 2008) without it https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Persell et al [61] Medical Care Time series analysis at a To implement and ana- Quality measures pertain- ✓ large internal medicine lyze a multifaceted quali- ing to coronary heart dis- practice from February 1, ty improvement interven- ease, health failure, dia- 2007, to February 1, tion using EHRs as tools betes mellitus, and pre- 2009 (n=12,299 patients for improving perfor- vention eligible at the beginning mance of the intervention) Radley et al [62] Journal of American Systematic literature re- To analyze the adoption Likelihood of medication ✓ Medical Informatics view and random-effects of CPOE systems on the errors Association meta-analytic techniques, reduction in medication American Society of errors in hospitals Health System Pharma- cists Annual survey (2007), AHA Annual Survey (2007), and AHA EHR Adoption Database supplement (2008) Rao et al [63] Journal of American Mailed surveys to a na- To analyze variation in Physician perceptions of ✓ Medical Informatics tionally representative the adoption of EHR quality of clinical deci- Association random sample of practic- functionalities and their sion, quality of communi- ing physicians from the use patterns, barriers to cation with patients and Physician Masterfile of adoption, and perceived other providers, delivery the American Medical benefits by physician of preventive or chronic Association (n=2769) practice size care that met the guide- lines, avoiding medica- tion errors and prescrip- tion refills Risko et al [64] Healthcare Patient processing met- To analyze the impact of Patient workup times and ✓ rics (n=374 observations) EHR implementation on LOS were collected for ED ED physician efficiency physicians (34 physi- and patient throughput cians) at 2 hospitals for 7 months before and 10 months after EHR imple- mentation Ryan et al [65] Medical Care Data collected from 143 To analyze whether EHR Quality of care was ana- ✓ practices with EHR im- implementation and lyzed from 8 separate in- plementation (2009- complementary interven- dicators; 4 cardiovascular 2011) tions, such as clinical de- measures (smoking cessa- cision support, technical tion intervention, BP assistance, and financial control, cholesterol con- incentives improved, the trol, and aspirin or an- quality of care provided tithrombotic treatment) and 4 additional clinical- ly important measures (BMI measurement, HbA control, pneumo- 1c coccal vaccine, and asth- ma control) Schreiber and Sha- Journal of Innovation Data collected from a To evaluate whether an LOS and cost measured ✓ ✓ ha [66] in Health Informatics community hospital for increase in adoption of by LOS 5 years after CPOE CPOE leads to a decrease adoption in LOS Scott et al [67] The Journal of Bone Data collected from an To evaluate the impact of Labor cost, documenta- ✓ ✓ and Joint Surgery outpatient adult recon- EMR implementation us- tion time for providers, struction clinic (n=143 ing advanced cost-ac- and time spent interact- patients) before imple- counting methods on or- ing with patients menting the hospital sys- thopedic surgeons in an tem–wide EMR system outpatient setting and 2 months, 6 months, and 2 years after imple- mentation https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 13 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Shen et al [68] International Journal National Inpatient Sam- To examine how EHR Cost of care for the 8 ✓ ✓ of Healthcare Technol- ple and AHA EHR imple- adoption affected the cost quality indicators (cardio- ogy and Management mentation survey for the of care and quality out- vascular and cerebrovas- year 2009 comes in an acute care cular) and quality indica- hospital setting tors for 5 cardiovascular and 3 cerebrovascular conditions and proce- dures Silow-Carroll et al Issue Brief (Common- Interviews with individu- To analyze the experi- Communication among ✓ [69] wealth Fund) als in the 9 hospitals that ence of 9 hospitals in us- providers, care coordina- implemented a compre- ing EHR to improve tion, patient engagement, hensive EHR system quality and efficiency and medical errors Singh et al [70] Journal of American Retrospective case-con- To evaluate the impact of Net revenue, revenue to ✓ ✓ Medical Association trol study comparing the EHR system implementa- volume ratio, capital and Ophthalmology pre- (n=13,969 patient tion from clinical and implementation costs, encounters) and post- economic perspectives at EHR incentive payments EHR (n=14,191 patient a large multidisciplinary received, patient volume, encounters) implementa- ophthalmic practice diagnostic and procedure tion periods at an eye in- volume, and coding vol- stitute umes Sockolow et al Applied Clinical Infor- Pre- and postobservation- To compare workflows, Number of days required ✓ ✓ [30] matics al mixed methods study, financial billing, and pa- to create a financial reim- Philadelphia-based tient outcomes before bursement bill, productiv- homecare agency with and after implementation ity, behavioral outcomes, 137 clinicians—data in- to analyze the effect of a and clinicians’ percep- cluded clinician EHR homecare point of care tions of patient safety documentation comple- EHR tion, EHR use data, Medicare billing data, an EHR Nurse Satisfaction survey, clinician observa- tions, clinician inter- views, and patient out- comes Thirukumaran et al Health Services Re- Data collected from the To evaluate the effect of SCIP scores ✓ [71] search SCIP Core Measure data EHR placement on SCIP set from the CMS Hospi- measures in a tertiary tal Inpatient Quality Re- care teaching hospital porting (n=1816) pro- gram (March 2010 to February 2012) Tidwell et al [72] Obstetrics and Gyne- Data collected from an To evaluate whether a Net profit, days in ac- ✓ ✓ cology obstetrics and gynecolo- low-cost electronic prac- counts receivables, pa- gy practice comprising 6 tice management system tient visits, no-show rate, physicians and 6 mid- (EHR) can improve care and quality data gather- wives with 150 daily vis- coordination and finan- ing its cial measures Varpio et al [73] Medical Education A 2-phase longitudinal To evaluate the impact of Clinician experience was ✓ study; data collected adopting EHR on clini- measured in terms of through field observa- cian experience cognitive workload, clin- tions (146 hours with 300 ical reasoning support providers, 22 patients, mechanisms, and knowl- and 32 patient family edge about the patient members), think-aloud (n=13) and think-after (n=11) sessions, inter- views (n=39) and docu- ment retrieval (n=392) https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 14 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Walker-Czyz et al Journal of Nursing Data for a quantitative, To evaluate how an inte- Cost (nurse hours per pa- ✓ ✓ [74] Administration retrospective analysis grated EHR innovation tient day, nurse turnover, collected from urban adoption affects cost, and nurse overtime), hospitals (431 beds) with nurse satisfaction, and quality nursing care out- 10 medical-surgical units nursing care delivered in comes (hospital-acquired and 2 critical care units terms of quality falls and pressure ulcers, ventilator-associated pneumonia, central line–associated blood- stream infections, and catheter-associated uri- nary tract infections) Wang et al [75] Preventing Chronic Clinical quality measure To analyze how clinical 4 key quality measures: ✓ Disease performance data collect- quality measures for inde- antithrombotic therapy, ed from 151 primary care pendent primary care BP control, HbA test- 1c practices that implement- practices improve as a ing, and smoking cessa- ed EHR (October 2009 result of EHR use and tion intervention to October 2011) technical support from a local public health agen- cy Wang et al [26] International Journal Definitive health care da- To evaluate how HIT ex- Return on assets, produc- ✓ ✓ of Accounting Informa- ta set for hospital-level penses and intermediate tivity ([net revenue, 1 tion Systems data for the years 2011 to business processes affect million]), and number of 2016 (n=3266 observa- hospital financial perfor- staff beds) tions) mance and productivity Xiao et al [76] Perspectives in Health Charts were reviewed to To describe how electron- Note completion and ✓ Information Manage- collect data from a large ic charting implementa- documentation of medica- ment tertiary public medical tion in a large public out- tion center (3 years before patient clinic improves and 3 years after EHR clinical documentation implementation in July 2009) Yeung [16] International Journal 433 local health depart- To determine the impact The health of a popula- ✓ of Medical Informat- ments’ population-based of the adoption of EHR tion at the county level, ics data for 433 counties and health information as measured by health exchange changes by lo- outcomes such as prema- cal health departments on ture death and health-re- population health lated quality of life Wani and Malhotra Journal of Operations Acute care hospitals in To analyze the impact of LOS and readmission ✓ [77] Management California EHR adoption in terms rates of full adoption vs mean- ingful assimilation on clinical outcomes https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 15 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman Study Journal or conference Study period or data set Objective Outcome measures Financial Clinical (n=21) (n=54) Zhou et al [78] Journal of the Ameri- To evaluate the extent of Quality measures are ag- Quality measures aggre- ✓ can Medical Informat- EHR use and how the gregated into 6 clinical gated into 6 clinical cate- ics Association quality of care delivered categories (asthma care, gories (asthma care, be- in ambulatory care prac- behavioral and mental havioral and mental tices varied according to health, cancer screening, health, cancer screening, the duration of EHR diabetes care, well-child diabetes care, well child availability and adolescent visits, and and adolescent visit, women’s health screen- women’s health screen- ings) ings) EHR: electronic health record. CMS: Centers for Medicare and Medicaid Services. ✓: indicates that the outcome was discussed in the study. HIT: health IT. AHRQ: Agency for Healthcare Research and Quality. EMR: electronic medical record. SCIP: Surgical Care Improvement Project. HIMSS: Healthcare Information and Management Systems Society. HbA : hemoglobin A . 1c 1c AHA: American Hospital Association. ED: emergency department. LOS: length of stay. MICU: medical intensive care unit. CPOE: certified provider order entry. BP: blood pressure. Most of the studies included in this review of the literature had EHR adoption [29,51]. A few studies reported improved financial outcome measures that demonstrated some form of financial performance concerning savings [42], net profit, and improvement. One of the studies reported that costs that days in account receivables [72] as a result of EHR adoption. increased during the implementation period were equivalent to One of the studies examined the association among HIT the preimplementation level after 6 months [67]. Hepp et al [17] expenses, hospital financial performance, and productivity, with found that the certified physician order entry (CPOE) system EHR adoption being an intermediate variable. This study (part of the EHR system) generated lower costs in addition to indicated a direct and positive association between HIT improving medication safety. A few other studies also confirmed investment and positive financial performance regarding return that patients in facilities with EHR systems incurred lower costs on assets [26]. than those in facilities without an EHR system [54,68,69]. By contrast, a set of results from a survey of ophthalmologists In terms of mixed financial outcomes, the analysis of indicated increasing costs and decreasing revenue and Adler-Milstein et al [18] exhibited that greater EHR adoption productivity with the adoption of EHRs [28]. Other studies have did not improve financial efficiency (measured by the ratio of similarly reported findings in terms of a decrease in revenue a hospital’s total expenditures to adjusted patient days) for [54,70] and an increase in cost [29] as a result of EHR adoption. nonfederal acute care hospitals immediately after the adoption Dandu et al [41] did not provide any statistically significant of EHR; however, the results from this study reported evidence to report a direct association between EHR adoption improvements in financial efficiency for the years 2010 and and higher-level billing [41]. Similarly, Mirani and Harpalani 2011 compared with the years 2008 and 2009 [18]. [27] did not provide any statistically significant evidence to report a direct association between EHR adoption and revenue. Regarding the reimbursement measure, EHR systems were Findings from Collum et al [8] suggested that alterations in the thought to be responsible for significant improvements in the level of EHR adoption were not related to increases in revenue timeliness of clinical documentation and billing for and the reduction of operating margins. reimbursement [30,41,76]. The analysis of Cheriff et al [37] documented that physicians who adopted EHRs in a large Clinical Outcomes academic multispecialty physician group captured higher Of the 58 reviewed studies, 55 (95%) contained segments that average monthly charges than before the use of EHRs. Similarly, were coded under the category of “Value-Clinical Outcomes.” another study reported that the introduction of EHRs was The differing measures for clinical outcomes in these studies associated with an increase in average per-patient charge and were productivity [26,28,30], workflow inefficiency, medical an increase in average per-patient collection [44]. errors, patient safety [3], patient satisfaction, clinical volume, readmission rates, patient LOS [27], and quality indicators at In terms of revenues, profit margins, and return on assets, the individual patient level. The different measures of clinical revenues were reported to have increased in conjunction with https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 16 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman outcomes are listed and described in depth in Table 2. The [28,34,45]. EHR implementation was reportedly associated with studies detailed both positive (33/58, 57%), negative (16/58, increased documentation effort and time, with little to no 28%), and no (7/58, 12%) association relationships between increase in clinical volume and little to no or perhaps a negative EHR adoption and clinical outcomes. Similar to financial impact on clinical and surgical volume [38,39,41]. Increased outcomes, an overlap of both positive and negative impacts documentation time because of EHR adoption resulted in a pertaining to EHR adoption on clinical outcomes was observed decrease in the time spent reviewing patient records and in some of the studies. performing physical examinations [67]. The results from one of the studies did not identify any differences in productivity Most of the clinical outcome measures involved in this review (total visit volume) resulting from EHR adoption [70]; however, exhibited some form of improvement. The Hessels et al [50] 3% (2/58) of other studies detailed a decrease in productivity study reported a statistically significant association between immediately following the adoption of EHR [51,64]. Another EHR adoption and LOS. A significant reduction of LOS in example includes significant and consistent decreases in patient emergency departments [42] and medical errors in emergency volume spanning 4 years after EHR adoption in an academic and critical care departments [48,49], as well as inpatient acute outpatient ophthalmology practice [55]. EHR systems were said care settings [62], were indicated as a result of EHR adoption. to increase the number of missed assessments, decrease the The rising and falling CPOE rates were also determined to be timely completion rate of assessments, and negatively affect in correlation with the increase and decrease in LOS [66]. the productivity of clinicians [54]. A study reported that In connection with workflow efficiency and productivity, EHR physicians were mostly checking boxes to complete the EHR use was reportedly helpful in improving the promptness of data process instead of developing or using investigative clinical documentation [30], enhancing productivity and strategies, which are common among diagnosticians [73]. efficiency in the workloads of primary care physicians [32], Considering the patient satisfaction, quality, safety, LOS, and and increasing productivity [37]. Furthermore, EHR was found readmission rate perspectives, EHR use resulted in lower patient to be responsible for an increase in patient visits (which results satisfaction [79] and quality of care [71] for a few years in increased revenue), a decrease in no-show rates (also following the adoption of EHRs. In addition, EHR use was increasing revenue), and improved care coordination [72]. There associated with an increase in hospital-acquired conditions was statistically significant progress in terms of completion during EHR implementation [74]. No relationship was found rates of assessments [29,54], better documentation of to exist between practice size and the impact of EHR on the medication, patients’ vital signs and pain scores [40], and quality of patient care from the perspectives of physicians [63]. improved clinical documentation [53,56,76] as a result of EHR Some studies reported no association between EHR adoption adoption. and improvement in the quality of care provided [36,52,68,78], For the category of patient satisfaction, physicians recognized readmission rates [60], and LOS [48]. Findings from another electronic communication permitted through EHR as a secure study that examined physician perceptions of EHRs indicated and efficient way of communicating with patients, resulting in that physicians believed that EHRs could create new improvements in patient satisfaction [34]. A study discovered opportunities for error [3]. evidence that higher levels of EHR adoption were positively The Intersection of Financial and Clinical Outcomes associated with performance and patient satisfaction. This study Having reported on studies that examined financial and clinical detected improvements in performance and patient satisfaction outcomes as individual factors, we now report on studies that for the years 2010 and 2011 compared with the years 2008 and examined both financial and clinical outcomes. 2009 [18]. Overall, 9% (5/58) of studies surveyed for this review of the With regard to patient safety and medical errors, surgical IT literature reported on the intersection of financial and clinical systems (as a subset of EHR systems) positively affected levels outcomes. To further investigate this intersection, the category of patient safety, compliance, and quality and process measures “Value–Intersection of Financial and Clinical Outcomes” was for patients undergoing surgical procedures in hospitals [31,35]. generated. Furthermore, 80% (4/5) of these studies specified a Outside of surgical IT systems, clinical decision support has positive association between EHR adoption and financial and also been shown to address other areas of patient safety [59]. clinical outcomes. For example, adverse drug events decreased by 20% [47], and CPOE was reported to provide exceptional value by improving In terms of the financial outcomes, hospitals that had adopted medication safety in a cost-effective manner [17]. EHR selectively increased the efficiency of their turnover rate of Medicare patients to receive higher MU incentives [27]. Indicators of quality at the individual patient level, such as rates These findings point toward the impact of EHR adoption on a of antithrombotic therapy and nicotine use documentation, patient’s stay duration on average (clinical outcome), which, in increased immediately following EHR adoption [58]. Similarly, turn, affects their compensation because of the loss of patient another study reported improvements in antibiotic therapy, days (financial outcome) from CMS. EHR adoption was blood pressure control, hemoglobin A testing, and smoking 1c associated with enabling the prioritization of improvements in cessation interventions because of EHR systems [75]. clinical documentation time to improve agency cash flow [30]. In contrast, for productivity and workload efficiency, the results EHR use was thought to contribute to shortened emergency of a survey indicated that physicians perceived that EHR department LOS, which led to a positive impact in terms of adoption harmed productivity and increased their workload CMS compensation [42]. Similarly, CPOE, a subset of EHR, https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 17 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman was said to be an independent factor in the impact of LOS; was also responsible for an increase in personnel costs in therefore, it indirectly contributed to lower costs [66]. By association with the new technology’s initial steep learning contrast, 20% (1/5) of the studies reported that EHR adoption curve [67]. Overall, these studies indicated interdependence required a learning period, where increased medical assistant between financial and clinical outcomes, in essence, how one time, patient time, and physician documentation time incurred was associated with the other in some form. additional costs [67]. This review of the literature discovered some studies with contradictory findings. For example, financial outcomes such Discussion as profit margins, return on assets, and costs were some of the measures that reported contradictory findings. A potential reason Principal Findings could be that the studies that reported an inverse relationship The primary goal of this literature review was to substantiate reviewed these measures right after the adoption, as opposed how EHR value is described concerning 2 different outcome to studies that reported it after a longer period. Organizational categories, financial and clinical outcomes, and to further the performance measures such as return on assets, ROI, and return exploration of the impact of EHR adoption on these 2 outcome on equity could be examined to explore the cyclical relationship categories. Subsequently, this review incorporated studies that between IT inputs and productivity [80]. Future research may described relationships between EHR adoption along with be required to investigate the trajectory and extent of the financial and clinical outcomes with a priori categories (financial relationship between IT investments and reinvestments, such outcomes and clinical outcomes) and with an additional category as EHR adoption or readoption, and clinical outcomes to further that included the intersection of financial and clinical outcomes. expand upon this question. This review of the literature included a total of 58 studies. Limitations Overall, 76% (16/21) of the studies that discussed the financial The comprehensive findings of this literature review should be outcomes of EHR adoption presented a positive relationship considered along with the limitations. Concerning the searched between EHR adoption and financial outcomes. These studies databases, PubMed, Scopus, and Embase—the primary health observed changes in financial outcomes in terms of profit ratios, services and HIT databases—were used. It is possible that costs, revenues, reimbursements, and return on assets. Consistent studies on the value of EHRs were published outside of with the literature, value realization, especially in terms of health-focused journals and if so, may not have been included financial outcomes, is lagging as it involves a large upfront cost in this literature review. Another limitation of this review [18]. involves the keywords used in the selection criteria of the article Regarding clinical outcomes, 76% (35/58) of the studies that search process. It is possible that the used keywords were not examined the clinical outcomes of EHR adoption indicated a exhaustive, and studies could have been overlooked. Finally, positive relationship between EHR adoption and clinical this review included English-only studies that were conducted outcomes in terms of LOS, readmission rates, patient in the United States. It is possible that other countries with EHRs satisfaction, medical errors, patient safety, user productivity, may have had an experiential understanding that could have and quality indicators at individual patient levels. Similar to contributed to this review. To mitigate bias, manual screening financial outcomes, value realization regarding clinical outcomes of all the references of included studies was conducted. also improved over time. For instance, clinical outcome Conclusions measures such as rates of hemoglobin A testing, recorded 1c This review of the literature reports on the individual and BMI, and cholesterol testing decreased before rebounding, collective value of EHRs from a financial and clinical outcomes following the adoption of EHR [57]. perspective. The collective perspective examined the intersection Of the 58 studies in this review of the literature, 5 (9%) studies of financial and clinical outcomes, suggesting a reversal of the highlighted the intersection of financial and clinical outcomes. current understanding of how IT investments could generate EHR adoption allowed for improvements in clinical productivity improvements, and prompted a new question to be documentation time and LOS and sequentially reduced overall asked about whether an increase in productivity could potentially costs and improved reimbursement [27,30,42,66]. EHR adoption lead to more IT investments. Conflicts of Interest None declared. References 1. Electronic Health Records. Healthcare Information and Management Systems Society. 2011. URL: https://www.himss.org/ library/ehr [accessed 2022-09-12] 2. Garrett P, Seidman J. EMR vs EHR – What is the Difference? Health IT Buzz. 2011 Jan 4. URL: https://www.healthit.gov/ buzz-blog/electronic-health-and-medical-records/emr-vs-ehr-difference [accessed 2022-09-12] 3. Love JS, Wright A, Simon SR, Jenter CA, Soran CS, Volk LA, et al. Are physicians' perceptions of healthcare quality and practice satisfaction affected by errors associated with electronic health record use? J Am Med Inform Assoc 2012;19(4):610-614 [FREE Full text] [doi: 10.1136/amiajnl-2011-000544] [Medline: 22199017] https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 18 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman 4. Redd TK, Read-Brown S, Choi D, Yackel TR, Tu DC, Chiang MF. Electronic health record impact on productivity and efficiency in an academic pediatric ophthalmology practice. J AAPOS 2014 Dec;18(6):584-589 [FREE Full text] [doi: 10.1016/j.jaapos.2014.08.002] [Medline: 25456030] 5. McAlearney AS, Sieck C, Hefner J, Robbins J, Huerta TR. Facilitating ambulatory electronic health record system implementation: evidence from a qualitative study. Biomed Res Int 2013;2013:629574 [FREE Full text] [doi: 10.1155/2013/629574] [Medline: 24228257] 6. Adler-Milstein J, Green CE, Bates DW. A survey analysis suggests that electronic health records will yield revenue gains for some practices and losses for many. Health Aff (Millwood) 2013 Mar;32(3):562-570. [doi: 10.1377/hlthaff.2012.0306] [Medline: 23459736] 7. Medicare and Medicaid Promoting Interoperability Program Basics. Centers for Medicare & Medicaid Services. 2018. URL: https://www.cms.gov [accessed 2019-01-16] 8. Collum TH, Menachemi N, Sen B. Does electronic health record use improve hospital financial performance? Evidence from panel data. Health Care Manage Rev 2016;41(3):267-274. [doi: 10.1097/HMR.0000000000000068] [Medline: 26052785] 9. Jang Y, Lortie MA, Sanche S. Return on investment in electronic health records in primary care practices: a mixed-methods study. JMIR Med Inform 2014 Sep 29;2(2):e25 [FREE Full text] [doi: 10.2196/medinform.3631] [Medline: 25600508] 10. Pine R, Tart K. Return on investment: benefits and challenges of baccalaureate nurse residency program. Nurs Econ 2007;25(1):13-19. [Medline: 17402673] 11. Peterson LT, Ford EW, Eberhardt J, Huerta TR, Menachemi N. Assessing differences between physicians' realized and anticipated gains from electronic health record adoption. J Med Syst 2011 Apr;35(2):151-161. [doi: 10.1007/s10916-009-9352-z] [Medline: 20703574] 12. value definition. Oxford Dictionary. 2019. URL: https://en.oxforddictionaries.com/definition/value [accessed 2019-01-16] 13. Payne TH, Bates DW, Berner ES, Bernstam EV, Covvey HD, Frisse ME, et al. Healthcare information technology and economics. J Am Med Inform Assoc 2013;20(2):212-217 [FREE Full text] [doi: 10.1136/amiajnl-2012-000821] [Medline: 22781191] 14. Feldman SS, Horan TA. Collaboration in electronic medical evidence development: a case study of the Social Security Administration's MEGAHIT System. Int J Med Inform 2011 Aug;80(8):e127-e140. [doi: 10.1016/j.ijmedinf.2011.01.012] [Medline: 21333588] 15. Riskin L, Koppel R, Riskin D. Re-examining health IT policy: what will it take to derive value from our investment? J Am Med Inform Assoc 2015 Mar;22(2):459-464. [doi: 10.1136/amiajnl-2014-003065] [Medline: 25326600] 16. Yeung T. Local health department adoption of electronic health records and health information exchanges and its impact on population health. Int J Med Inform 2019 Aug;128:1-6. [doi: 10.1016/j.ijmedinf.2019.04.011] [Medline: 31160006] 17. Hepp Z, Forrester SH, Roth J, Wirtz HS, Devine EB. Cost-effectiveness of a computerized provider order entry system in improving medication safety: a case study in ambulatory care. Value Health 2013 May 1;16(3):A205-A206. [doi: 10.1016/j.jval.2013.03.1038] 18. Adler-Milstein J, Everson J, Lee SD. EHR adoption and hospital performance: time-related effects. Health Serv Res 2015 Dec;50(6):1751-1771 [FREE Full text] [doi: 10.1111/1475-6773.12406] [Medline: 26473506] 19. Shah GH, Leider JP, Castrucci BC, Williams KS, Luo H. Characteristics of local health departments associated with implementation of electronic health records and other informatics systems. Public Health Rep 2016;131(2):272-282 [FREE Full text] [doi: 10.1177/003335491613100211] [Medline: 26957662] 20. World Health Organization. 2019. URL: https://www.who.int/en/ [accessed 2022-09-12] 21. Velentgas P, Dreyer NA, Nourjah P, Smith SR, Torchia MM. Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide. Rockville, MD, USA: Agency for Healthcare Research and Quality; 2013. 22. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med 2009 Jul 21;6(7):e1000100 [FREE Full text] [doi: 10.1371/journal.pmed.1000100] [Medline: 19621070] 23. Fond G, Hamdani N, Kapczinski F, Boukouaci W, Drancourt N, Dargel A, et al. Effectiveness and tolerance of anti-inflammatory drugs' add-on therapy in major mental disorders: a systematic qualitative review. Acta Psychiatr Scand 2014 Mar;129(3):163-179. [doi: 10.1111/acps.12211] [Medline: 24215721] 24. Pichler G, Cheung PY, Aziz K, Urlesberger B, Schmölzer GM. How to monitor the brain during immediate neonatal transition and resuscitation? A systematic qualitative review of the literature. Neonatology 2014;105(3):205-210 [FREE Full text] [doi: 10.1159/000357162] [Medline: 24481411] 25. Friese S. Qualitative Data Analysis with ATLAS.ti. 2nd edition. Thousand Oaks, CA, USA: Sage Publications; 2014. 26. Wang T, Wang Y, McLeod A. Do health information technology investments impact hospital financial performance and productivity? Int J Account Inf Syst 2018 Mar;28:1-13. [doi: 10.1016/j.accinf.2017.12.002] 27. Mirani R, Harpalani A. Business benefits or incentive maximization? Impacts of the medicare EHR incentive program at acute care hospitals. ACM Trans Manage Inf Syst 2013 Dec;4(4):1-19. [doi: 10.1145/2543900] https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 19 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman 28. Lim MC, Boland MV, McCannel CA, Saini A, Chiang MF, Epley KD, et al. Adoption of electronic health records and perceptions of financial and clinical outcomes among ophthalmologists in the United States. JAMA Ophthalmol 2018 Feb 01;136(2):164-170 [FREE Full text] [doi: 10.1001/jamaophthalmol.2017.5978] [Medline: 29285542] 29. Brown Jr LS, Kritz S, Lin M, Zavala R. Evaluation of an electronic medical record system at an opioid agonist treatment program. J Addict Med 2014;8(2):96-101 [FREE Full text] [doi: 10.1097/ADM.0000000000000018] [Medline: 24562402] 30. Sockolow PS, Bowles KH, Adelsberger MC, Chittams JL, Liao C. Impact of homecare electronic health record on timeliness of clinical documentation, reimbursement, and patient outcomes. Appl Clin Inform 2014 Apr 30;5(2):445-462 [FREE Full text] [doi: 10.4338/ACI-2013-12-RA-0106] [Medline: 25024760] 31. Appari A, Johnson EM, Anthony DL. Information technology and hospital patient safety: a cross-sectional study of US acute care hospitals. Am J Manag Care 2014 Nov;20(11 Spec No. 17):eSP39-eSP47 [FREE Full text] [Medline: 25811818] 32. Bae J, Encinosa WE. National estimates of the impact of electronic health records on the workload of primary care physicians. BMC Health Serv Res 2016 May 10;16:172 [FREE Full text] [doi: 10.1186/s12913-016-1422-6] [Medline: 27160147] 33. Behkami NA, Dorr DA, Morrice S. A business case for HIT adoption: effects of "meaningful use" EHR financial incentives on clinic revenue. Stud Health Technol Inform 2010;160(Pt 1):779-783 [FREE Full text] [Medline: 20841792] 34. Bishop TF, Press MJ, Mendelsohn JL, Casalino LP. Electronic communication improves access, but barriers to its widespread adoption remain. Health Aff (Millwood) 2013 Aug;32(8):1361-1367 [FREE Full text] [doi: 10.1377/hlthaff.2012.1151] [Medline: 23918479] 35. Bucher BT, Swords DS, Robinson J, Jackson GP, Finlayson SR. Advanced electronic health record adoption improves hospital compliance with surgical care improvement project core measures. J Am Coll Surgeons 2016 Oct;223(4):e33. [doi: 10.1016/j.jamcollsurg.2016.08.088] 36. Burke HB, Becher DA, Hoang A, Gimbel RW. The adoption of an electronic health record did not improve A1c values in type 2 diabetes. J Innov Health Inform 2016 Apr 15;23(1):144 [FREE Full text] [doi: 10.14236/jhi.v23i1.144] [Medline: 27348484] 37. Cheriff AD, Kapur AG, Qiu M, Cole CL. Physician productivity and the ambulatory EHR in a large academic multi-specialty physician group. Int J Med Inform 2010 Jul;79(7):492-500. [doi: 10.1016/j.ijmedinf.2010.04.006] [Medline: 20478738] 38. Chiang MF, Read-Brown S, Tu DC, Beaudet K, Yackel TR. Electronic health record implementation in pediatric ophthalmology: impact on volume and time. J AAPOS 2013 Feb 1;17(1):e3. [doi: 10.1016/j.jaapos.2012.12.011] 39. Chiang MF, Read-Brown S, Tu DC, Choi D, Sanders DS, Hwang TS, et al. Evaluation of electronic health record implementation in ophthalmology at an academic medical center (an American Ophthalmological Society thesis). Trans Am Ophthalmol Soc 2013 Sep;111:70-92 [FREE Full text] [Medline: 24167326] 40. Choi CK, Saberito D, Tyagaraj C, Tyagaraj K. Organizational performance and regulatory compliance as measured by clinical pertinence indicators before and after implementation of Anesthesia Information Management System (AIMS). J Med Syst 2014 Jan;38(1):5. [doi: 10.1007/s10916-013-0005-x] [Medline: 24424430] 41. Dandu N, Zmistowski B, Chen AF, Chapman T, Howley M. How are electronic health records associated with provider productivity and billing in orthopaedic surgery? Clin Orthop Relat Res 2019 Nov;477(11):2443-2451 [FREE Full text] [doi: 10.1097/CORR.0000000000000896] [Medline: 31389875] 42. Daniel GW, Ewen E, Willey VJ, Reese Iv CL, Shirazi F, Malone DC. Efficiency and economic benefits of a payer-based electronic health record in an emergency department. Acad Emerg Med 2010 Aug;17(8):824-833 [FREE Full text] [doi: 10.1111/j.1553-2712.2010.00816.x] [Medline: 20670319] 43. Deily ME, Hu T, Terrizzi S, Chou SY, Meyerhoefer CD. The impact of health information technology adoption by outpatient facilities on pregnancy outcomes. Health Serv Res 2013 Feb;48(1):70-94 [FREE Full text] [doi: 10.1111/j.1475-6773.2012.01441.x] [Medline: 22742682] 44. Edwardson N, Kash BA, Janakiraman R. Measuring the impact of electronic health record adoption on charge capture. Med Care Res Rev 2017 Oct;74(5):582-594. [doi: 10.1177/1077558716659408] [Medline: 27416948] 45. Ehrlich JR, Michelotti M, Blachley TS, Zheng K, Couper MP, Greenberg GM, et al. A two-year longitudinal assessment of ophthalmologists' perceptions after implementing an electronic health record system. Appl Clin Inform 2016 Oct 12;7(4):930-945 [FREE Full text] [doi: 10.4338/ACI-2016-05-RA-0075] [Medline: 27730248] 46. Flatow VH, Ibragimova N, Divino CM, Eshak DS, Twohig BC, Bassily-Marcus AM, et al. Quality outcomes in the surgical intensive care unit after electronic health record implementation. Appl Clin Inform 2015 Oct 7;6(4):611-618 [FREE Full text] [doi: 10.4338/ACI-2015-04-RA-0044] [Medline: 26767058] 47. Furukawa MF, Spector WD, Rhona Limcangco MR, Encinosa WE. Meaningful use of health information technology and declines in in-hospital adverse drug events. J Am Med Inform Assoc 2017 Jul 01;24(4):729-736 [FREE Full text] [doi: 10.1093/jamia/ocw183] [Medline: 28339642] 48. Han JE, Rabinovich M, Abraham P, Satyanarayana P, Liao TV, Udoji TN, et al. Effect of electronic health record implementation in critical care on survival and medication errors. Am J Med Sci 2016 Jun;351(6):576-581. [doi: 10.1016/j.amjms.2016.01.026] [Medline: 27238919] 49. Herasevich V, Tsapenko M, Kojicic M, Ahmed A, Kashyap R, Venkata C, et al. Limiting ventilator-induced lung injury through individual electronic medical record surveillance. Crit Care Med 2011 Jan;39(1):34-39. [doi: 10.1097/CCM.0b013e3181fa4184] [Medline: 20959788] https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 20 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman 50. Hessels A, Flynn L, Cimiotti JP, Bakken S, Gershon R. Impact of heath information technology on the quality of patient care. Online J Nurs Inform 2015;19:1 [FREE Full text] [Medline: 27570443] 51. Howley MJ, Chou EY, Hansen N, Dalrymple PW. The long-term financial impact of electronic health record implementation. J Am Med Inform Assoc 2015 Mar;22(2):443-452. [doi: 10.1136/amiajnl-2014-002686] [Medline: 25164255] 52. Jones SS, Adams JL, Schneider EC, Ringel JS, McGlynn EA. Electronic health record adoption and quality improvement in US hospitals. Am J Manag Care 2010 Dec;16(12 Suppl HIT):SP64-SP71 [FREE Full text] [Medline: 21314225] 53. Katzer R, Barton DJ, Adelman S, Clark S, Seaman EL, Hudson KB. Impact of implementing an EMR on physical exam documentation by ambulance personnel. Appl Clin Inform 2012 Jul 25;3(3):301-308 [FREE Full text] [doi: 10.4338/ACI-2012-03-RA-0008] [Medline: 23646077] 54. Kritz S, Brown LS, Chu M, John-Hull C, Madray C, Zavala R, et al. Electronic medical record system at an opioid agonist treatment programme: study design, pre-implementation results and post-implementation trends. J Eval Clin Pract 2012 Aug;18(4):739-745 [FREE Full text] [doi: 10.1111/j.1365-2753.2011.01664.x] [Medline: 21414112] 55. Lam JG, Lee BS, Chen PP. The effect of electronic health records adoption on patient visit volume at an academic ophthalmology department. BMC Health Serv Res 2016 Jan 13;16:7 [FREE Full text] [doi: 10.1186/s12913-015-1255-8] [Medline: 26762304] 56. Lowe JR, Raugi GJ, Reiber GE, Whitney JD. Does incorporation of a clinical support template in the electronic medical record improve capture of wound care data in a cohort of veterans with diabetic foot ulcers? J Wound Ostomy Continence Nurs 2013;40(2):157-162 [FREE Full text] [doi: 10.1097/WON.0b013e318283bcd8] [Medline: 23466720] 57. McCullough JS, Casey M, Moscovice I, Prasad S. The effect of health information technology on quality in U.S. hospitals. Health Aff (Millwood) 2010 Apr;29(4):647-654. [doi: 10.1377/hlthaff.2010.0155] [Medline: 20368594] 58. McCullough CM, Wang JJ, Parsons AS, Shih SC. Quality measure performance in small practices before and after electronic health record adoption. EGEMS (Wash DC) 2015 Jan 6;3(1):1131 [FREE Full text] [doi: 10.13063/2327-9214.1131] [Medline: 25848635] 59. Mitchell J, Probst J, Brock-Martin A, Bennett K, Glover S, Hardin J. Association between clinical decision support system use and rural quality disparities in the treatment of pneumonia. J Rural Health 2014;30(2):186-195. [doi: 10.1111/jrh.12043] [Medline: 24689543] 60. Patterson ME, Marken P, Zhong Y, Simon SD, Ketcherside W. Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure. Appl Clin Inform 2014 Jul 30;5(3):670-684 [FREE Full text] [doi: 10.4338/ACI-2014-01-RA-0008] [Medline: 25298808] 61. Persell SD, Kaiser D, Dolan NC, Andrews B, Levi S, Khandekar J, et al. Changes in performance after implementation of a multifaceted electronic-health-record-based quality improvement system. Med Care 2011 Feb;49(2):117-125. [doi: 10.1097/MLR.0b013e318202913d] [Medline: 21178789] 62. Radley DC, Wasserman MR, Olsho LE, Shoemaker SJ, Spranca MD, Bradshaw B. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc 2013 May 01;20(3):470-476 [FREE Full text] [doi: 10.1136/amiajnl-2012-001241] [Medline: 23425440] 63. Rao SR, Desroches CM, Donelan K, Campbell EG, Miralles PD, Jha AK. Electronic health records in small physician practices: availability, use, and perceived benefits. J Am Med Inform Assoc 2011 May 01;18(3):271-275 [FREE Full text] [doi: 10.1136/amiajnl-2010-000010] [Medline: 21486885] 64. Risko N, Anderson D, Golden B, Wasil E, Barrueto F, Pimentel L, et al. The impact of electronic health record implementation on emergency physician efficiency and patient throughput. Healthc (Amst) 2014 Sep;2(3):201-204. [doi: 10.1016/j.hjdsi.2014.06.003] [Medline: 26250507] 65. Ryan AM, McCullough CM, Shih SC, Wang JJ, Ryan MS, Casalino LP. The intended and unintended consequences of quality improvement interventions for small practices in a community-based electronic health record implementation project. Med Care 2014 Sep;52(9):826-832. [doi: 10.1097/MLR.0000000000000186] [Medline: 25100231] 66. Schreiber R, Shaha SH. Computerised provider order entry adoption rates favourably impact length of stay. J Innov Health Inform 2016 Apr 18;23(1):166 [FREE Full text] [doi: 10.14236/jhi.v23i1.166] [Medline: 27348485] 67. Scott DJ, Labro E, Penrose CT, Bolognesi MP, Wellman SS, Mather 3rd RC. The impact of electronic medical record implementation on labor cost and productivity at an outpatient orthopaedic clinic. J Bone Joint Surg Am 2018 Sep 19;100(18):1549-1556. [doi: 10.2106/JBJS.17.01339] [Medline: 30234619] 68. Shen JJ, Cochran CR, Neish S, Moseley CB, Mukalian R. Level of EHR adoption and quality and cost of care - evidence from vascular conditions and procedures. Int J Healthcare Technol Manag 2015 Jul 13;15(1):4-21. [doi: 10.1504/ijhtm.2015.070514] 69. Silow-Carroll S, Edwards JN, Rodin D. Using electronic health records to improve quality and efficiency: the experiences of leading hospitals. Issue Brief (Commonw Fund) 2012 Jul;17:1-40. [Medline: 22826903] 70. Singh RP, Bedi R, Li A, Kulkarni S, Rodstrom T, Altus G, et al. The practice impact of electronic health record system implementation within a large multispecialty ophthalmic practice. JAMA Ophthalmol 2015 Jun;133(6):668-674. [doi: 10.1001/jamaophthalmol.2015.0457] [Medline: 25880083] https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 21 (page number not for citation purposes) XSL FO RenderX JMIR MEDICAL INFORMATICS Modi & Feldman 71. Thirukumaran CP, Dolan JG, Reagan Webster P, Panzer RJ, Friedman B. The impact of electronic health record implementation and use on performance of the Surgical Care Improvement Project measures. Health Serv Res 2015 Feb;50(1):273-289 [FREE Full text] [doi: 10.1111/1475-6773.12191] [Medline: 24965357] 72. Tidwell C, Wolfberg A, Corrigan H. Leveraging technology to improve finances and care coordination in a small rural practice. Obstetrics Gynecology 2016 May;127:54S-55S. [doi: 10.1097/01.aog.0000483870.50355.52] 73. Varpio L, Day K, Elliot-Miller P, King JW, Kuziemsky C, Parush A, et al. The impact of adopting EHRs: how losing connectivity affects clinical reasoning. Med Educ 2015 May;49(5):476-486. [doi: 10.1111/medu.12665] [Medline: 25924123] 74. Walker-Czyz A. The impact of an integrated electronic health record adoption on nursing care quality. J Nurs Adm 2016;46(7-8):366-372. [doi: 10.1097/NNA.0000000000000360] [Medline: 27379908] 75. Wang JJ, Sebek KM, McCullough CM, Amirfar SJ, Parsons AS, Singer J, et al. Sustained improvement in clinical preventive service delivery among independent primary care practices after implementing electronic health record systems. Prev Chronic Dis 2013 Aug 01;10:E130 [FREE Full text] [doi: 10.5888/pcd10.120341] [Medline: 23906330] 76. Xiao AQ, Acosta FX. Implementation and impact of psychiatric electronic medical records in a public medical center. Perspect Health Inf Manag 2016 Oct 1;13(Fall):1e [FREE Full text] [Medline: 27843422] 77. Wani D, Malhotra M. Does the meaningful use of electronic health records improve patient outcomes? J Oper Manag 2018 Jun 22;60(1):1-18. [doi: 10.1016/j.jom.2018.06.003] 78. Zhou L, Soran CS, Jenter CA, Volk LA, Orav EJ, Bates DW, et al. The relationship between electronic health record use and quality of care over time. J Am Med Inform Assoc 2009;16(4):457-464 [FREE Full text] [doi: 10.1197/jamia.M3128] [Medline: 19390094] 79. Marmor RA, Clay B, Millen M, Savides TJ, Longhurst CA. The impact of physician EHR usage on patient satisfaction. Appl Clin Inform 2018 Jan;9(1):11-14 [FREE Full text] [doi: 10.1055/s-0037-1620263] [Medline: 29298451] 80. Baker J, Song J, Jones DR. Closing the loop: empirical evidence for a positive feedback model of IT business value creation. J Strategic Inf Syst 2017 Jun;26(2):142-160. [doi: 10.1016/j.jsis.2016.12.001] Abbreviations CMS: Center for Medicare and Medicaid Services CPOE: certified physician order entry EHR: electronic health record HIT: health IT HITECH: Health Information Technology for Economic and Clinical Health LOS: length of stay MU: meaningful use PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses ROI: return on investment Edited by C Lovis; submitted 14.02.22; peer-reviewed by M Pinto da Costa, C Ta; comments to author 27.03.22; revised version received 10.05.22; accepted 31.07.22; published 27.09.22 Please cite as: Modi S, Feldman SS The Value of Electronic Health Records Since the Health Information Technology for Economic and Clinical Health Act: Systematic Review JMIR Med Inform 2022;10(9):e37283 URL: https://medinform.jmir.org/2022/9/e37283 doi: 10.2196/37283 PMID: ©Shikha Modi, Sue S Feldman. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 27.09.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included. https://medinform.jmir.org/2022/9/e37283 JMIR Med Inform 2022 | vol. 10 | iss. 9 | e37283 | p. 22 (page number not for citation purposes) XSL FO RenderX

Journal

JMIR Medical Informatics – JMIR Publications

Published: Sep 27, 2022

Keywords: electronic health records; EHRs; value; financial outcomes; clinical outcomes; health informatics; clinical informatics

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

The Value of Electronic Health Records Since the Health Information Technology for Economic and Clinical Health Act: Systematic Review

The Value of Electronic Health Records Since the Health Information Technology for Economic and Clinical Health Act: Systematic Review

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

The Value of Electronic Health Records Since the Health Information Technology for Economic and Clinical Health Act: Systematic Review

The Value of Electronic Health Records Since the Health Information Technology for Economic and Clinical Health Act: Systematic Review

References (80)

Abstract

Journal

Recommended Articles

There are no references for this article.

Our policy towards the use of cookies