The Economics of Electronic Health Records (EHRs): A Comparative Analysis of Implementation Costs, ROI, and Efficiency Metrics Across Healthcare Settings

The Economics of Electronic Health Records (EHRs): A Comparative Analysis of Implementation Costs, ROI, and Efficiency Metrics Across Healthcare Settings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Economics of Electronic Health Records (EHRs): A Comparative Analysis of Implementation Costs, ROI, and Efficiency Metrics Across Healthcare Settings Christopher Gransberry, Zeynep Behjet, Tonjua McCullough, Jaclyn Felder-Strauss This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6882060/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To investigate the economics of Electronic Health Records (EHRs) by conducting a comparative economic analysis of EHR implementations in varied healthcare settings with a focus on assessing workflow efficiency and patient outcomes across socioeconomic spectrums. Methodology The research adopted a mixed methodological framework that included both qualitative and quantitative analysis. The qualitative methodology involved a detailed meta-analytic and systematic review of literature while the quantitative methodology involved statistical analysis of data associated with the economic aspects of EHR implementation. The review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Findings The costs related to hardware were associated with computer equipment, related ancillary equipment and networking. The costs related to software were associated license or maintenance costs for EHR or related software Installation costs included vendor and contractor costs. The associated ROI and break-even points for the implementation of the EHR were computed based on the model by Jang et al. ( 2014 ). The performance efficiency metrics for the systems are based on availability, reliability, and latency (speed). The different patient outcomes associated with EHR implementation can include hospital costs, efficiency of admission process, quality of healthcare services and the recovery time. Conclusion Implementation costs, ROI, efficiency metrics, and patient outcome measures across different healthcare settings can be used to uncover the economic viability of EHR implementation. Reliability Latency Efficiency Parameters Patient Outcomes ROI Introduction In a modern healthcare system, it is quite unimaginable how a successful healthcare organization can operate without an electronic health record (EHR) system. With technological advancements, EHRs have established a domain as the go-to technological tool for achieving patient-centered health care in clinical settings. An upgrade to an advanced and optimized EHR optimization offers potential benefits to health stakeholders in terms of efficient operations, delivery of high-quality health care services, and reduction of operational costs (Baillieu et al., 2020 ). In the U.S., adoption of an EHR system is a requirement and failure to have such a system can incur financial penalties for non-compliance with the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. Since the HITECH Act of 2009 was passed, there has been a significant increase in the adoption and implementation of EHRs in clinical settings with 78% of U.S. office-based physicians and 96% of non-federal hospitals having such a system installed as reported by the National Coordinator for Health Information Technology (ONC). According to the HITECH Act 2009, healthcare providers and organizations have to provide verifiable proof of their meaningful use of federally certified EHR systems to receive government incentives. With the increased implementation of EHR projects in different healthcare facilities, there is a rich volume of research and academic work that have examined the processes, costs, benefits and challenges of implementation of the systems (Fennelly et al., 2020 ). A white paper by the Itransition Limited Group (ILG) (2023) links the increase in adoption of EHRs to the financial incentives provided by the federal government to implement the system in healthcare facilities back in 2011. According to Modi & Feldman ( 2022 ), the government has given approximately $ 27 billion in incentives to healthcare facilities that have adopted and implemented the systems based on federally defined criteria. While the implementation of EHRs is considered as an innovation enabler with potential benefits, existing literature have reported mixed results on the same which suggests that the implementation of the system is not an automatic guarantee of conversion of the potential benefits to realized benefits (Modi & Feldman, 2022 ; Lewkowicz et al. 2020 ). In most cases, the implementation process is a complex endeavor that involves a wide variety of obstacles in terms of financial resources, uncertainty about cost recovery, and adaptation to changes by the healthcare professionals (De Benedictis et al., 2020 ). From an economic perspective, the uncertainty of cost recovery is a significant concern which highlights the need for cost-benefit analysis of EHR implementations. According to an analytical article by Prasad (2013), it is quite challenging to quantify a return on EHR implementation investments considering the long-term measurement requirements and intangible criteria in patient outcomes. However, there are specific metrics and data that can be used to provide an analytical economic perspective of the process. An important economic metric of EHR implementation is the quantitative associated costs. The implementation costs associated with EHR implementation are based on a wide range of factors such as the size of the health facility, availability of financial resources, and the type of EHR software and hardware of interest. The implementation process is divided into different sub-categories based on the type of activity, tools and staff required. The costs include the purchase and installation of software and hardware, upgrade or optimization of the existing records program, and training of the end-users (Shestel, 2020; Gupta, 2022 ). The implementation process further requires staffing costs especially if the new existing team is unable to understand the functionalities of the new system. On the same note, the process might require new technological tools such as the practice management system, email servers and new software which might require highly skilled personnel (Shestel, 2020). Further, depending on the type of software and hardware of choice, the ongoing expenditures such as software development maintenance, hardware replacement, vendor transcription fees and training fees are additional costs associated with the implementation of the systems. Often, the implementation costs do not include the maintenance of the system and the associated labor costs, and the federal financial incentives under the HITECH Act 2009 are designed to compensate for some of the implementation costs especially for smaller organizations. A second economic metric of EHR implementation is the return on investment (ROI) which measures the amount of profit attributed to the investment. The standard ROI for a project is calculated as below; Return on Investment (ROI) = Profit from Investment/ Cost of Investment In the context of the current research, a break-even-point analysis is considered as the most appropriate indicator to determine the level of ROI of an EHR investment due to the sensitivity of benefits realized from such an investment and the lack of detailed financial data relating to gains and/or savings directly attributable to an EHR system to quantify ROI in primary care clinic settings. In this regard, the break-even point of an EHR investment is the period it takes for the facility to recover the associated implementation costs with either improved expenses and/or reduced expenses. Based on a model developed by Jang et al. ( 2014 ), the determination of the break-even point of the project is based on computation of the value of the revenues and expenses during three distinct periods of pre-EHR, peri-EHR, and post-EHR. In the model, the pre-EHR is the complete financial year before the start of the implementation process and the peri-EHR period is the financial years during the process while the post-EHR period is the complete financial year after the end of the project. Using the model, the breakeven point is computed using the following formula; M Breakeven = C EHR / [(NR Peri – NR pre )/12] Where; M Breakeven = Months to Break-Even, C EHR = Cost of implementation of the EHR, NR Peri = Annual facility net revenue in the peri-EHR period, NR pre = Annual facility net revenue during the pre-EHR period The validity of the model for the determination of the break-even point of the investment is based on the fact that the net revenue difference between pre-EHR and peri-EHR periods is enough to recover the cost of implementation of the system. As an evaluative economic indicator, performance efficiency metrics are based on the environment of the EHR system. First, it is important to consider that EHRs are not stand-alone systems but rather work in ‘closed’ or ‘open’ environments. Under closed environments, the system is controlled by a single command while the open environment is controlled by multiple commands with different objectives. The performance efficiency metrics for the systems are based on availability, reliability, and latency (speed). In terms of availability, the important statistical metrics for efficiency of the systems include the percentage of system uptime and system downtime. The system downtime can be caused by different factors or events such as scheduled or unexpected power outage, network issues and server issues. The reliability of the systems is measured on the basis of access to the system with ease by different personnel in terms of ability to log into the system. The latency of the system is measured in terms of the percentage network latency and percentage network packet loss (Dugas et al., 2020 ; Huang et al., 2018 ; Melnick et al., 2021 ). Other efficiency metrics for EHR investment include end-user metrics, clinical metrics and system metrics. The end-user metrics can include the transaction time and EUD configuration while the clinical metrics can include the number of booked appointments and number of completed appointments. The system metrics include number of users logged in, number of clinics and types of clinics (Huang, Gibson, & Terry, 2018 ). According to the U.S. Department of Health and Human Services, the patient reported outcome measures are important in offering complementary perspectives to the clinical assessments and can be used as an economic indicator of EHR systems. The different patient outcomes associated with EHR implementation can include hospital costs, efficiency of admission process, quality of healthcare services and the recovery time. Based on the different economic indicators of the EHR systems, the present study investigates the economics of Electronic Health Records (EHRs) by conducting a comparative economic analysis of EHR Implementations in varied healthcare settings with a focus on assessing workflow efficiency and patient outcomes across socioeconomic spectrums. The research aims to examine how economic disparities between healthcare settings (like urban vs. Rural hospitals, high-income vs. Low-income regions) affect the cost-benefit outcomes of EHR implementations. The findings are important to uncover the economic viability and ROI of EHR systems in diverse economic environments (Nguyen et al., 2022 ). A quantitative analysis utilizing statistical tools to analyze data on implementation costs, ROI, efficiency metrics, and patient outcome measures across different healthcare settings was employed. Methodology ​​The research adopted a mixed methodological framework that included both qualitative and quantitative analysis. The qualitative methodology involved a detailed meta-analytic and systematic review of literature while the quantitative methodology involved statistical analysis of data associated with the economic aspects of EHR implementation. To achieve the objectives of the qualitative methodology, the researcher adopted the methodological framework outlined by the Joanna Briggs Institute (2015) and was informed by Arksey and O’Malley’s (2005) approach of summary and dissemination of research findings and, in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The adopted three-step review framework by Joanna Briggs Institute (2015) was also used for collection of quantitative data for the research. The author searched for publication titles, keywords and abstracts using concept terms in the different sources and selected articles and statistical information that were associated with economic aspects of EHR implementation. The article and data search were conducted using the databases and data sources such as government agencies, academic and research databases, industry report and white papers, government health IT departments, Healthcare Provider Network and Associations, International Organizations, and University Research Centers and Libraries. The author adopted a step-wise search strategy and screening process for the articles and statistical information incorporated in the research. The screening for literature and statistical information was based on titles and abstracts that were used to review the eligibility of inclusion of a study. The author identified and compiled all the potentially relevant literature and statistical information, and then excluded any duplicate article or statistical information. After exclusion of the duplicate information, the author searched for articles and statistical information that were not relevant to the research objectives as well as those with irrelevant titles and research questions. Thereafter, the articles or statistical information that did not align with the research objective and were written as reviews, editorials or commentaries were discarded. The suitability of the remaining articles and information was based on a pre-set inclusion and exclusion criterion and an independent review of the identified information was conducted to confirm their suitability. The inclusion criterion of the articles was based on the relevancy to the topic of research in terms of answering the research question, and the inclusion of relevant concept terms and keywords. Studies with titles and abstracts that contain most concept terms or keywords and those that focus on the economic aspects of EHR implementation were included. After confirmation that the included studies met the inclusion and exclusion criteria, the researcher proceeded to data extraction and quantitative analysis of collected statistical data. The extraction of information and data from the selected articles and data sources was based on the framework by Kraus et al. (2020). In the review of the chosen qualitative articles, the researcher read and closely analyzed the sources in several cycles as key themes and questions emerged from the synthesis. The identified themes were cross-checked against the primary sources of the selected articles and the focus and range of data extraction was informed by the research objectives. The framework by Kraus et al. (2020) was complemented by the Cochrane review framework for more in-depth synthesis of the research findings through extraction of a wide set of research themes and items. Apart from the primary items associated with economics of EHR implementation, the author developed explanatory models and provided their own interpretations of the collected data. As expected, the data extraction process assumed an iterative approach and was quite intensive. For every single outcome of interest that was extracted from the data, the author recorded the associated values in an Excel spreadsheet for synthesis and interpretation. The author then employed the Cochrane Handbook for Systematic Reviews of Interventions for estimation of values that had not been published. The results from the extracted data were summarized in tables and sub-groups of studies were formed based on the research objectives. Results and Findings Quantitative Analysis The search for statistical information on economic factors associated with implementation of EHR systems yielded three case studies that provided a detailed cost breakdown of the implementation costs. The three case studies included EHR implementation at Health Texas Provider Network, Belleville Family Medical Clinic, and 14 solo/small-group primary care practices in 12 states. The statistical data was sourced from three different studies that were cited by Shestel (2020), and adjusted accordingly based on the set criteria. The costs related to hardware were associated with computer equipment (desktop computers, laptops, servers, storage), related ancillary equipment (printers, scanners, monitors), and networking (routers, wiring). The costs related to software were associated license or maintenance costs for EHR or related software (for interfaces, databases). Installation costs included vendor and contractor costs. The associated ROI and break-even points for the implementation of the EHR in the three case studies were computed based on the model by Jang et al. ( 2014 ), and a standardized value $82,500 for calculation of the profit (Menachemi & Brooks, 2005 ). The detailed results and analysis of the three case studies are outlined as below; 1. Health Texas Provider Network Health Texas Provider Network is a member of the Baylor Scott & White Health system and offers healthcare services across different socioeconomic spectrums including urban areas, rural areas, high-income group and low-income group. The current case study focuses on the cost of implementation of an EHR for a five-physician practice through the first year. The detailed breakdown of the costs is shown in Table 1 below; Table 1 Detailed Breakdown of EHR implementation costs at Health Texas Provider Network EHR Implementation Case Study: Health Texas Provider Network EHR Implementation Cost Breakdown for five physician practice Expenditure Amount Fixed Costs Hardware $ 25,000.00 Network implementation team $ 28,000.00 Practice Implementation team $ 7,500.00 Variable Costs Hardware $ 35,300.00 Software Licensing and Hosting $ 85,500.00 End-user practice $ 51,700.00 Total for the Practice $ 233,000.00 Total per physician $ 46,600.00 Based on the data, the facility incurred hardware costs, compensation for the implementation team, software licensing and end-user practice. The total amount for the practice was $233,000 with the total amount per physician of $46,600. The ROI and break-even points are calculated as below; Return on Investment (ROI) = Profit from Investment/ Cost of Investment Return on Investment (ROI) = 82,500/233,000 Return on Investment = 0.35 M Breakeven = C EHR / [(NR Peri – NR pre )/12] M Breakeven = 0.86 months 2. Belleville Family Medical Clinic Belleville Family Medical Clinic is located at 311 West Lincoln Street, Suite 200, Belleville, IL 62220, an urban and high-income region. The current case study focuses on the cost of implementation of an EHR for practice with six resident physicians through the first year. The detailed breakdown of the costs is shown in Table 2 below; Table 2 Detailed Breakdown of EHR implementation costs at Belleville Family Medical Clinic. EHR Implementation Case Study: Belleville Family Medical Clinic EHR Implementation Cost Breakdown for practice with six resident physicians Expenditure Amount Vendor Costs including software and interface $ 51,500.00 Hardware costs including database server and printers $ 67,000.00 Other costs including wiring and remodeling $ 12,000.00 Compensation for Project Management Team $ 104,000.00 Staff Training $ 6,300.00 Total for the Practice $ 240,800.00 Total Per Physician $ 40,133.33 Based on the data, the facility incurred expenditure on vendor costs including software and interface, hardware costs including database server and printers, compensation for project management team, staff training, other costs including wiring and remodeling (Janssen et al., 2021 ). The total cost for the practice was $240,800 while the total cost per physician was $40,133. The ROI and break-even points are calculated as below; Return on Investment (ROI) = Profit from Investment/ Cost of Investment Return on Investment (ROI) = 82,500/240,800 Return on Investment (ROI) = 0.34 M Breakeven = C EHR / [(NR Peri – NR pre )/12] M Breakeven = 0.87 months 3. EHR Implementation Case Study: 16 small-group primary care practices in 14 states The data provided in Table 3 shows the implementation costs of 16 small-group primary care practices that offer healthcare services across different socioeconomic spectrums including urban areas, rural areas, high-income group and low-income group in 14 states per full-time equivalent (FTE) per physician per year. Table 3 EHR Implementation Case Study: 16 primary care practices in 14 states EHR Implementation Case Study: 14 solo/small-group primary care practices in 12 states EHR Implementation Cost Breakdown per physician Expenditure Amount Minimum Maximum Average Software Purchase, Training and Installation Costs $ 8,475.00 $ 32,600.00 $ 20,537.50 Hardware Costs $ 5,300.00 $ 23,600.00 $ 14,450.00 Software Maintenance and Support $ 1,200.00 $ 3,800.00 $ 2,500.00 Staff and Third-Party Contractors $ - $ 5,500.00 $ 2,750.00 Abstraction and Communication Costs $ - $ 12,400.00 $ 6,200.00 Other Costs $ - $ 20,000.00 $ 10,000.00 Total per Physician $ 14,975.00 $ 97,900.00 $ 56,437.50 Based on the data, the facilities spent on Software Purchase, Training and Installation Cost, Hardware Costs, Software Maintenance and Support, Staff and Third-Party Contractors, abstraction and Communication Costs, and Other Costs. The minimum total cost per physician was $14,975 while the maximum total cost per physician was $97,900. The average cost per physician across the 14 states was $56,437.50 annually. The ROI and break-even points are calculated as below; Return on Investment (ROI) = Profit from Investment/ Cost of Investment Return on Investment (ROI) = 82,500/56,437 Return on Investment (ROI) = 1.46 M Breakeven = C EHR / [(NR Peri – NR pre )/12] M Breakeven = 0.67 months. Qualitative Analysis The article search strategy for qualitative information on the research topic yielded a total of 124 sources. After the researcher had excluded the duplicate material sources, only 35 articles and research materials remained, which highlights the scarcity of research studies on the topic area. Of the remaining article sources, 17 did not meet the inclusion criteria and, therefore, only 18 were obtained for detailed eligibility assessment and inclusion in the research. The sources included published journal articles from PubMed, ScienceDirect, Google Scholar, JSTOR and EBSCOhost. Also, the sources included reports from major healthcare consulting firms like McKinsey & Company, Deloitte, or Accenture, and industry-specific publications and white papers from health IT companies (like Epic, Cerner, or Meditech). Other sources included organizations including the American Hospital Association (AHA) and the National Association for Healthcare Quality (NAHQ). Discussion The findings show that the implementation costs associated with EHR implementation are based on a wide range of factors such as the size of the health facility, availability of financial resources, and the residency and number of physicians. According to the findings, the implementation costs of EHR implementation are related to purchase of the purchase and installation of software and hardware, upgrade or optimization of the existing records program, and training of the end-users (Shestel, 2020; Gupta, 2022 ). The hardware costs include computer equipment such as desktop computers, laptops, servers, storage, related ancillary equipment such as printers, scanners, monitors, and networking equipment such as routers and wiring. The software costs were associated license or maintenance costs for EHR or related software for interfaces and databases while installation costs included vendor and contractor costs. Also, the findings show that there were revenue losses at implementation which were attributable to provider productivity decreases as a result of reduced visit schedules. Further, the implementation process requires staffing costs especially if the new existing team is not conversant with the operation of the new system. On the same note, depending on the type of software and hardware of choice, the ongoing expenditures such as software development maintenance, hardware replacement, vendor transcription fees and training fees are additional costs associated with the implementation of the systems. Often, the implementation costs do not include the maintenance of the system and the associated labor costs. Based on the statistical information of the case studies, the highest expenditure on EHR implementation by a facility such as the Health Texas Provider Network, which works under an umbrella healthcare system was software licensing and hosting, and end-user practice. The data shows that the Baylor Scott & White Health system focuses on procuring high-end software licensing and hosting for its independent healthcare facilities and invests heavily on end-user practice to promote the quality of healthcare service provided and the efficiency parameters of the end-product. As such, it can be inferred that most healthcare facilities under an umbrella healthcare company and offers services to patients across different socioeconomic spectrums invest on improved efficiency parameters through procurement of high-end and software licenses and hosts, and training of their staff to improve patient outcomes, increase ROI and reduce operational costs. Also, based on the statistical information derived from the case study of Belleville Family Medical Clinic, a large proportion of implementation costs are incurred on hardware equipment and compensation for the project management team. The findings show that the successful implementation of EHR in a healthcare facility is influenced by the quality of the hardware equipment and the skills of the project management team (Janssen et al., 2021 ). According to the findings of the case studies of the 16 small-group primary care in the 14 states, healthcare facilities also spend on communication costs and staff training (Yeung et al., 2020 ). The findings highlight the significance of effective communication between stakeholders and end-user training on EHR implementation. In terms of ROI and break-even points, the quantitative research results show that the three case studies reported positive ROI and break-even points of less than one year. The quantitative results are supported by the qualitative findings which highlighted potential benefits of implementation of EHR to health stakeholders in terms of efficient operations, delivery of high-quality health care services, and reduction of operational costs (Baillieu et al., 2020 ). However, as reported by Modi & Feldman ( 2022 ), while the implementation of EHRs is considered as an innovation enabler with potential benefits, it is not an automatic guarantee of conversion of the potential benefits to realized benefits (Lewkowicz et al., 2020 ). It is reported that the implementation process is a complex endeavor and involves different challenges in terms of financial resources, uncertainty about cost recovery, and adaptation to changes by the healthcare professionals (De Benedictis et al., 2020 ). From an economic perspective, the uncertainty of cost recovery is a significant concern which highlights the need for cost-benefit analysis of EHR implementations (Shestel, 2020). In terms of efficiency metrics, the findings show that the important statistical metrics for efficiency of the systems include the percentage of system uptime and system downtime. The system downtime can be caused by different factors or events such as scheduled or unexpected power outage, network issues and server issues. Other economic efficiency metrics include end-user metrics, clinical metrics and system metrics (Tayefi et al., 2021 ). Conclusion and Policy Implications The current quantitative research and meta-analytic review investigated the economics of Electronic Health Records (EHRs) by conducting a comparative economic analysis of EHR Implementations in varied healthcare settings with a focus on assessing workflow efficiency and patient outcomes across socioeconomic spectrums. Practically, it is quite challenging to quantify a return on EHR implementation investments considering the long-term measurement requirements, intangible criteria in patient outcomes, and the lack of detailed financial data relating to gains and/or savings directly attributable to an EHR system. However, the results show that there are specific indicators including ROI, implementation costs, efficiency metrics and patient outcomes, which can be used as economic indicators for EHR implementation. The findings show how economic factors and healthcare settings influence the effectiveness and efficiency of EHR systems, providing valuable insights for policymakers, healthcare providers, and technology developers. Declarations Authors’ information (optional) Not applicable. Ethics approval and consent to participate Not applicable. This study used only publicly available secondary data and did not involve human participants or identifiable human data. However, the research adhered to the principles outlined in the Declaration of Helsinki ( https://www.wma.net/policies-post/wma-declaration-of-helsinki/ ) in ensuring ethical use and interpretation of health-related data. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this work. Author Contribution C.G. (Christopher Gransberry) served as the Principal Investigator and led the conceptual design, literature review, case study selection, data analysis, and drafting of the original manuscript. Z.B. (Zeynep Behjet) contributed to refining the methodological framework and interpreting findings through a clinical and health informatics lens. T.M. (Tonjua McCullough) provided guidance on economic evaluation models and contributed to the cost analysis and ROI interpretation. J.F.S. (Jaclyn Felder-Strauss) supported the analysis of financial metrics and assisted in aligning economic indicators with healthcare accounting principles.All authors—C.G., Z.B., T.M., and J.F.S.—substantively revised the manuscript, approved the submitted version (and any substantially modified version involving their contributions), and agreed to be personally accountable for their own contributions. Each author commits to ensuring that any questions related to the accuracy or integrity of any part of the work, even those in which they were not directly involved, will be appropriately investigated, resolved, and the resolution documented in the literature. Acknowledgements The authors wish to thank their respective institutions for supporting interdisciplinary research in health information systems and accounting departments. Availability of data and materials All data generated or analyzed during this study are included in this published article. The datasets supporting the conclusions of this article are derived from publicly available sources, including published literature, government reports, and institutional white papers, etc. cited within the manuscript. References Baillieu R, Hoang H, Sripipatana A, Nair S, Lin SC. (2020). Impact of health information. technology optimization on. clinical quality performance in health centers: A national cross sectional study. PLoS ONE, 15(7), e0236019. De Benedictis A, Lettieri E, Gastaldi L, Masella C, Urgu A, Tartaglini D. (2020). Electronic. Medical Records implementation in hospital. An empirical investigation of individual and. organizational determinants. PLoS ONE, 15(6), e0234108. Dugas M, Tapuria A, Bruland P, Delaney B, Kalra D, Curcin V, Informatics T. (2020). Conceptual design. implementation, and evaluation of generic and Standard-Compliant data. transfer into electronic health records. Appl Clin Inf, 11(03), 374–86. Fennelly O, Cunningham C, Grogan L, Cronin H, O’Shea C, Roche M, O’Hare N. (2020). Successfully implementing a national electronic health record: a rapid umbrella. review. Int J Med Informatics, 144, 104281. Gupta D. July 20). How to build an EHR system? Features and cost breakdown. Appinventiv; 2022. https:// appinventiv.com/blog/ehr-software-development/ Huang MZ, Gibson CJ, Terry AL. (2018). Measuring electronic health record use in primary. care. a scoping review. Appl Clin Inf, 9 (01), 015–33. Jang Y, Lortie MA, Sanche S. (2014). Return on investment in electronic health records in. primary care practices. : a mixed-methods study. JMIR Med Inf, 2(2), e3631. Janssen A, Donnelly C, Elder E, Pathmanathan N, Shaw T. (2021). Electronic medical record. implementation in tertiary care. factors influencing adoption of an electronic medical record. in a cancer centre. BMC Health Serv Res, 21(1), 1–9. Nguyen KH, Wright C, Simpson D, Woods L, Comans T, Sullivan C. (2022). Economic. evaluation and analyses. of hospital-based electronic medical records (EMRs): a scoping review of international literature. NPJ Digit Med, 5(1), 29. Melnick ER, Sinsky CA, Krumholz HM. (2021). Implementing measurement science for. electronic health record use. JAMA, 325(21), 2149–50. Menachemi N, Brooks RG. (2005). Exploring the return on investment associated with health. information technologies. Florida State University College of Medecine: Center for Patient Safety . Modi S, Feldman SS. (2022). The Value of Electronic Health Records Since the Health. Information Technology for Economic. and Clinical Health Act: Systematic Review. JMIR. Medical, Informatics. 10(9), e37283. Lewkowicz D, Wohlbrandt A, Boettinger E. (2020). Economic impact of clinical decision. support interventions based on electronic health records. BMC Health Services. Research. 20(1), 1–12. Rudin RS, Friedberg MW, Shekelle P, Shah N, Bates DW. (2020). Getting value from. electronic health records. research needed to improve practice. Annals of Internal. Medicine. 172(11_Supplement), S130-S136. Tayefi M, Ngo P, Chomutare T, Dalianis H, Salvi E, Budrionis A, Godtliebsen F. (2021). Challenges. and opportunities beyond structured data in analysis of electronic health. records. Wiley Interdisciplinary Reviews: Comput Stat, 13(6), e1549. Yeung K, Richards J, Goemer E, Lozano P, Lapham G, Williams E, Bradley K. (2020). Costs of using. evidence-based implementation strategies for behavioral health integration in a. large primary care system. Health Serv Res, 55(6), 913–23. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6882060","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485825818,"identity":"1e55f82d-21b8-4eb9-bf7e-067b832d2cc9","order_by":0,"name":"Christopher Gransberry","email":"data:image/png;base64,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","orcid":"","institution":"Purdue University Global","correspondingAuthor":true,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Gransberry","suffix":""},{"id":485825819,"identity":"918f7664-c6cd-4fb2-a1ea-5c120291d657","order_by":1,"name":"Zeynep Behjet","email":"","orcid":"","institution":"Purdue University Global","correspondingAuthor":false,"prefix":"","firstName":"Zeynep","middleName":"","lastName":"Behjet","suffix":""},{"id":485825820,"identity":"0449aff6-d87f-4961-a40e-9fc9ec9f044e","order_by":2,"name":"Tonjua McCullough","email":"","orcid":"","institution":"Purdue University Global","correspondingAuthor":false,"prefix":"","firstName":"Tonjua","middleName":"","lastName":"McCullough","suffix":""},{"id":485825821,"identity":"71e93e4e-cc83-4dce-9c03-cbdb91f70f26","order_by":3,"name":"Jaclyn Felder-Strauss","email":"","orcid":"","institution":"Purdue University Global","correspondingAuthor":false,"prefix":"","firstName":"Jaclyn","middleName":"","lastName":"Felder-Strauss","suffix":""}],"badges":[],"createdAt":"2025-06-12 16:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6882060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6882060/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93679823,"identity":"dd5bf5f1-57be-4f9c-a2af-407f852a1889","added_by":"auto","created_at":"2025-10-16 11:53:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":797377,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6882060/v1/f12f7df5-dd2d-470f-98ab-f498bd759b80.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Economics of Electronic Health Records (EHRs): A Comparative Analysis of Implementation Costs, ROI, and Efficiency Metrics Across Healthcare Settings","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn a modern healthcare system, it is quite unimaginable how a successful healthcare organization can operate without an electronic health record (EHR) system. With technological advancements, EHRs have established a domain as the go-to technological tool for achieving patient-centered health care in clinical settings. An upgrade to an advanced and optimized EHR optimization offers potential benefits to health stakeholders in terms of efficient operations, delivery of high-quality health care services, and reduction of operational costs (Baillieu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the U.S., adoption of an EHR system is a requirement and failure to have such a system can incur financial penalties for non-compliance with the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. Since the HITECH Act of 2009 was passed, there has been a significant increase in the adoption and implementation of EHRs in clinical settings with 78% of U.S. office-based physicians and 96% of non-federal hospitals having such a system installed as reported by the National Coordinator for Health Information Technology (ONC). According to the HITECH Act 2009, healthcare providers and organizations have to provide verifiable proof of their meaningful use of federally certified EHR systems to receive government incentives. With the increased implementation of EHR projects in different healthcare facilities, there is a rich volume of research and academic work that have examined the processes, costs, benefits and challenges of implementation of the systems (Fennelly et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA white paper by the Itransition Limited Group (ILG) (2023) links the increase in adoption of EHRs to the financial incentives provided by the federal government to implement the system in healthcare facilities back in 2011. According to Modi \u0026amp; Feldman (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the government has given approximately \u003cspan\u003e$\u003c/span\u003e27\u0026nbsp;billion in incentives to healthcare facilities that have adopted and implemented the systems based on federally defined criteria. While the implementation of EHRs is considered as an innovation enabler with potential benefits, existing literature have reported mixed results on the same which suggests that the implementation of the system is not an automatic guarantee of conversion of the potential benefits to realized benefits (Modi \u0026amp; Feldman, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lewkowicz et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In most cases, the implementation process is a complex endeavor that involves a wide variety of obstacles in terms of financial resources, uncertainty about cost recovery, and adaptation to changes by the healthcare professionals (De Benedictis et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From an economic perspective, the uncertainty of cost recovery is a significant concern which highlights the need for cost-benefit analysis of EHR implementations. According to an analytical article by Prasad (2013), it is quite challenging to quantify a return on EHR implementation investments considering the long-term measurement requirements and intangible criteria in patient outcomes. However, there are specific metrics and data that can be used to provide an analytical economic perspective of the process.\u003c/p\u003e\u003cp\u003eAn important economic metric of EHR implementation is the quantitative associated costs. The implementation costs associated with EHR implementation are based on a wide range of factors such as the size of the health facility, availability of financial resources, and the type of EHR software and hardware of interest. The implementation process is divided into different sub-categories based on the type of activity, tools and staff required. The costs include the purchase and installation of software and hardware, upgrade or optimization of the existing records program, and training of the end-users (Shestel, 2020; Gupta, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The implementation process further requires staffing costs especially if the new existing team is unable to understand the functionalities of the new system. On the same note, the process might require new technological tools such as the practice management system, email servers and new software which might require highly skilled personnel (Shestel, 2020). Further, depending on the type of software and hardware of choice, the ongoing expenditures such as software development maintenance, hardware replacement, vendor transcription fees and training fees are additional costs associated with the implementation of the systems. Often, the implementation costs do not include the maintenance of the system and the associated labor costs, and the federal financial incentives under the HITECH Act 2009 are designed to compensate for some of the implementation costs especially for smaller organizations.\u003c/p\u003e\u003cp\u003eA second economic metric of EHR implementation is the return on investment (ROI) which measures the amount of profit attributed to the investment. The standard ROI for a project is calculated as below;\u003c/p\u003e\u003cp\u003eReturn on Investment (ROI)\u0026thinsp;=\u0026thinsp;Profit from Investment/ Cost of Investment\u003c/p\u003e\u003cp\u003eIn the context of the current research, a break-even-point analysis is considered as the most appropriate indicator to determine the level of ROI of an EHR investment due to the sensitivity of benefits realized from such an investment and the lack of detailed financial data relating to gains and/or savings directly attributable to an EHR system to quantify ROI in primary care clinic settings. In this regard, the break-even point of an EHR investment is the period it takes for the facility to recover the associated implementation costs with either improved expenses and/or reduced expenses. Based on a model developed by Jang et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the determination of the break-even point of the project is based on computation of the value of the revenues and expenses during three distinct periods of pre-EHR, peri-EHR, and post-EHR. In the model, the pre-EHR is the complete financial year before the start of the implementation process and the peri-EHR period is the financial years during the process while the post-EHR period is the complete financial year after the end of the project. Using the model, the breakeven point is computed using the following formula;\u003c/p\u003e\u003cp\u003eM\u003csub\u003eBreakeven\u003c/sub\u003e = C\u003csub\u003eEHR\u003c/sub\u003e / [(NR\u003csub\u003ePeri\u003c/sub\u003e \u0026ndash; NR\u003csub\u003epre\u003c/sub\u003e)/12]\u003c/p\u003e\u003cp\u003eWhere;\u003c/p\u003e\u003cp\u003eM\u003csub\u003eBreakeven\u003c/sub\u003e = Months to Break-Even,\u003c/p\u003e\u003cp\u003eC\u003csub\u003eEHR\u003c/sub\u003e = Cost of implementation of the EHR,\u003c/p\u003e\u003cp\u003eNR\u003csub\u003ePeri\u003c/sub\u003e = Annual facility net revenue in the peri-EHR period,\u003c/p\u003e\u003cp\u003eNR\u003csub\u003epre\u003c/sub\u003e= Annual facility net revenue during the pre-EHR period\u003c/p\u003e\u003cp\u003eThe validity of the model for the determination of the break-even point of the investment is based on the fact that the net revenue difference between pre-EHR and peri-EHR periods is enough to recover the cost of implementation of the system.\u003c/p\u003e\u003cp\u003eAs an evaluative economic indicator, performance efficiency metrics are based on the environment of the EHR system. First, it is important to consider that EHRs are not stand-alone systems but rather work in \u0026lsquo;closed\u0026rsquo; or \u0026lsquo;open\u0026rsquo; environments. Under closed environments, the system is controlled by a single command while the open environment is controlled by multiple commands with different objectives. The performance efficiency metrics for the systems are based on availability, reliability, and latency (speed). In terms of availability, the important statistical metrics for efficiency of the systems include the percentage of system uptime and system downtime. The system downtime can be caused by different factors or events such as scheduled or unexpected power outage, network issues and server issues. The reliability of the systems is measured on the basis of access to the system with ease by different personnel in terms of ability to log into the system. The latency of the system is measured in terms of the percentage network latency and percentage network packet loss (Dugas et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Melnick et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Other efficiency metrics for EHR investment include end-user metrics, clinical metrics and system metrics. The end-user metrics can include the transaction time and EUD configuration while the clinical metrics can include the number of booked appointments and number of completed appointments. The system metrics include number of users logged in, number of clinics and types of clinics (Huang, Gibson, \u0026amp; Terry, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to the U.S. Department of Health and Human Services, the patient reported outcome measures are important in offering complementary perspectives to the clinical assessments and can be used as an economic indicator of EHR systems. The different patient outcomes associated with EHR implementation can include hospital costs, efficiency of admission process, quality of healthcare services and the recovery time. Based on the different economic indicators of the EHR systems, the present study investigates the economics of Electronic Health Records (EHRs) by conducting a comparative economic analysis of EHR Implementations in varied healthcare settings with a focus on assessing workflow efficiency and patient outcomes across socioeconomic spectrums. The research aims to examine how economic disparities between healthcare settings (like urban vs. Rural hospitals, high-income vs. Low-income regions) affect the cost-benefit outcomes of EHR implementations. The findings are important to uncover the economic viability and ROI of EHR systems in diverse economic environments (Nguyen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A quantitative analysis utilizing statistical tools to analyze data on implementation costs, ROI, efficiency metrics, and patient outcome measures across different healthcare settings was employed.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e​​The research adopted a mixed methodological framework that included both qualitative and quantitative analysis. The qualitative methodology involved a detailed meta-analytic and systematic review of literature while the quantitative methodology involved statistical analysis of data associated with the economic aspects of EHR implementation. To achieve the objectives of the qualitative methodology, the researcher adopted the methodological framework outlined by the Joanna Briggs Institute (2015) and was informed by Arksey and O’Malley’s (2005) approach of summary and dissemination of research findings and, in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The adopted three-step review framework by Joanna Briggs Institute (2015) was also used for collection of quantitative data for the research. The author searched for publication titles, keywords and abstracts using concept terms in the different sources and selected articles and statistical information that were associated with economic aspects of EHR implementation. The article and data search were conducted using the databases and data sources such as government agencies, academic and research databases, industry report and white papers, government health IT departments, Healthcare Provider Network and Associations, International Organizations, and University Research Centers and Libraries. The author adopted a step-wise search strategy and screening process for the articles and statistical information incorporated in the research.\u003c/p\u003e\u003cp\u003eThe screening for literature and statistical information was based on titles and abstracts that were used to review the eligibility of inclusion of a study. The author identified and compiled all the potentially relevant literature and statistical information, and then excluded any duplicate article or statistical information. After exclusion of the duplicate information, the author searched for articles and statistical information that were not relevant to the research objectives as well as those with irrelevant titles and research questions. Thereafter, the articles or statistical information that did not align with the research objective and were written as reviews, editorials or commentaries were discarded. The suitability of the remaining articles and information was based on a pre-set inclusion and exclusion criterion and an independent review of the identified information was conducted to confirm their suitability. The inclusion criterion of the articles was based on the relevancy to the topic of research in terms of answering the research question, and the inclusion of relevant concept terms and keywords. Studies with titles and abstracts that contain most concept terms or keywords and those that focus on the economic aspects of EHR implementation were included. After confirmation that the included studies met the inclusion and exclusion criteria, the researcher proceeded to data extraction and quantitative analysis of collected statistical data.\u003c/p\u003e\u003cp\u003eThe extraction of information and data from the selected articles and data sources was based on the framework by Kraus et al. (2020). In the review of the chosen qualitative articles, the researcher read and closely analyzed the sources in several cycles as key themes and questions emerged from the synthesis. The identified themes were cross-checked against the primary sources of the selected articles and the focus and range of data extraction was informed by the research objectives. The framework by Kraus et al. (2020) was complemented by the Cochrane review framework for more in-depth synthesis of the research findings through extraction of a wide set of research themes and items. Apart from the primary items associated with economics of EHR implementation, the author developed explanatory models and provided their own interpretations of the collected data. As expected, the data extraction process assumed an iterative approach and was quite intensive. For every single outcome of interest that was extracted from the data, the author recorded the associated values in an Excel spreadsheet for synthesis and interpretation. The author then employed the Cochrane Handbook for Systematic Reviews of Interventions for estimation of values that had not been published. The results from the extracted data were summarized in tables and sub-groups of studies were formed based on the research objectives.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Results and Findings","content":"\u003ch2\u003eQuantitative Analysis\u003c/h2\u003e\n\u003cp\u003eThe search for statistical information on economic factors associated with implementation of EHR systems yielded three case studies that provided a detailed cost breakdown of the implementation costs. The three case studies included EHR implementation at Health Texas Provider Network, Belleville Family Medical Clinic, and 14 solo/small-group primary care practices in 12 states. The statistical data was sourced from three different studies that were cited by Shestel (2020), and adjusted accordingly based on the set criteria. The costs related to hardware were associated with computer equipment (desktop computers, laptops, servers, storage), related ancillary equipment (printers, scanners, monitors), and networking (routers, wiring). The costs related to software were associated license or maintenance costs for EHR or related software (for interfaces, databases). Installation costs included vendor and contractor costs. The associated ROI and break-even points for the implementation of the EHR in the three case studies were computed based on the model by Jang et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), and a standardized value $82,500 for calculation of the profit (Menachemi \u0026amp; Brooks, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). The detailed results and analysis of the three case studies are outlined as below;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e1. Health Texas Provider Network\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eHealth Texas Provider Network is a member of the Baylor Scott \u0026amp; White Health system and offers healthcare services across different socioeconomic spectrums including urban areas, rural areas, high-income group and low-income group. The current case study focuses on the cost of implementation of an EHR for a five-physician practice through the first year. The detailed breakdown of the costs is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e below;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetailed Breakdown of EHR implementation costs at Health Texas Provider Network\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eEHR Implementation Case Study: Health Texas Provider Network\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eEHR Implementation Cost Breakdown for five physician practice\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpenditure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHardware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 25,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNetwork implementation team\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 28,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractice Implementation team\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 7,500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable Costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHardware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 35,300.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoftware Licensing and Hosting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 85,500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnd-user practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 51,700.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal for the Practice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e$ 233,000.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal per physician\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e$ 46,600.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBased on the data, the facility incurred hardware costs, compensation for the implementation team, software licensing and end-user practice. The total amount for the practice was $233,000 with the total amount per physician of $46,600. The ROI and break-even points are calculated as below;\u003c/p\u003e\n\u003cp\u003eReturn on Investment (ROI)\u0026thinsp;=\u0026thinsp;Profit from Investment/ Cost of Investment\u003c/p\u003e\n\u003cp\u003eReturn on Investment (ROI)\u0026thinsp;=\u0026thinsp;82,500/233,000\u003c/p\u003e\n\u003cp\u003eReturn on Investment\u0026thinsp;=\u0026thinsp;0.35\u003c/p\u003e\n\u003cp\u003eM\u003csub\u003eBreakeven\u003c/sub\u003e = C\u003csub\u003eEHR\u003c/sub\u003e / [(NR\u003csub\u003ePeri\u003c/sub\u003e \u0026ndash; NR\u003csub\u003epre\u003c/sub\u003e)/12]\u003c/p\u003e\n\u003cp\u003eM\u003csub\u003eBreakeven\u003c/sub\u003e = 0.86 months\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cstrong\u003e2. Belleville Family Medical Clinic\u003c/strong\u003e\u003c/div\u003e\n\u003cp\u003eBelleville Family Medical Clinic is located at 311 West Lincoln Street, Suite 200, Belleville, IL 62220, an urban and high-income region. The current case study focuses on the cost of implementation of an EHR for practice with six resident physicians through the first year. The detailed breakdown of the costs is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e below;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetailed Breakdown of EHR implementation costs at Belleville Family Medical Clinic.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEHR Implementation Case Study: Belleville Family Medical Clinic\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEHR Implementation Cost Breakdown for practice with six resident physicians\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpenditure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmount\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVendor Costs including software and interface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 51,500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHardware costs including database server and printers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 67,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther costs including wiring and remodeling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 12,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompensation for Project Management Team\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 104,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaff Training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e$ 6,300.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal for the Practice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e$ 240,800.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Per Physician\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e$ 40,133.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBased on the data, the facility incurred expenditure on vendor costs including software and interface, hardware costs including database server and printers, compensation for project management team, staff training, other costs including wiring and remodeling (Janssen et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The total cost for the practice was $240,800 while the total cost per physician was $40,133. The ROI and break-even points are calculated as below;\u003c/p\u003e\n\u003cp\u003eReturn on Investment (ROI)\u0026thinsp;=\u0026thinsp;Profit from Investment/ Cost of Investment\u003c/p\u003e\n\u003cp\u003eReturn on Investment (ROI)\u0026thinsp;=\u0026thinsp;82,500/240,800\u003c/p\u003e\n\u003cp\u003eReturn on Investment (ROI)\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e\n\u003cp\u003eM\u003csub\u003eBreakeven\u003c/sub\u003e = C\u003csub\u003eEHR\u003c/sub\u003e / [(NR\u003csub\u003ePeri\u003c/sub\u003e \u0026ndash; NR\u003csub\u003epre\u003c/sub\u003e)/12]\u003c/p\u003e\n\u003cp\u003eM\u003csub\u003eBreakeven\u003c/sub\u003e = 0.87 months\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cstrong\u003e3. EHR Implementation Case Study: 16 small-group primary care practices in 14 states\u003c/strong\u003e\u003c/div\u003e\n\u003cp\u003eThe data provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the implementation costs of 16 small-group primary care practices that offer healthcare services across different socioeconomic spectrums including urban areas, rural areas, high-income group and low-income group in 14 states per full-time equivalent (FTE) per physician per year.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEHR Implementation Case Study: 16 primary care practices in 14 states\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eEHR Implementation Case Study: 14 solo/small-group primary care practices in 12 states\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eEHR Implementation Cost Breakdown per physician\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpenditure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoftware Purchase, Training and Installation Costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 8,475.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 32,600.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 20,537.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHardware Costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 5,300.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 23,600.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 14,450.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoftware Maintenance and Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 1,200.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 3,800.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 2,500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaff and Third-Party Contractors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 5,500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 2,750.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbstraction and Communication Costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 12,400.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 6,200.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 20,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 10,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal per Physician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 14,975.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 97,900.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$ 56,437.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBased on the data, the facilities spent on Software Purchase, Training and Installation Cost, Hardware Costs, Software Maintenance and Support, Staff and Third-Party Contractors, abstraction and Communication Costs, and Other Costs. The minimum total cost per physician was $14,975 while the maximum total cost per physician was $97,900. The average cost per physician across the 14 states was $56,437.50 annually. The ROI and break-even points are calculated as below;\u003c/p\u003e\n\u003cp\u003eReturn on Investment (ROI)\u0026thinsp;=\u0026thinsp;Profit from Investment/ Cost of Investment\u003c/p\u003e\n\u003cp\u003eReturn on Investment (ROI)\u0026thinsp;=\u0026thinsp;82,500/56,437\u003c/p\u003e\n\u003cp\u003eReturn on Investment (ROI)\u0026thinsp;=\u0026thinsp;1.46\u003c/p\u003e\n\u003cp\u003eM\u003csub\u003eBreakeven\u003c/sub\u003e = C\u003csub\u003eEHR\u003c/sub\u003e / [(NR\u003csub\u003ePeri\u003c/sub\u003e \u0026ndash; NR\u003csub\u003epre\u003c/sub\u003e)/12]\u003c/p\u003e\n\u003cp\u003eM\u003csub\u003eBreakeven\u003c/sub\u003e = 0.67 months.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eQualitative Analysis\u003c/h2\u003e\n \u003cp\u003eThe article search strategy for qualitative information on the research topic yielded a total of 124 sources. After the researcher had excluded the duplicate material sources, only 35 articles and research materials remained, which highlights the scarcity of research studies on the topic area. Of the remaining article sources, 17 did not meet the inclusion criteria and, therefore, only 18 were obtained for detailed eligibility assessment and inclusion in the research. The sources included published journal articles from PubMed, ScienceDirect, Google Scholar, JSTOR and EBSCOhost. Also, the sources included reports from major healthcare consulting firms like McKinsey \u0026amp; Company, Deloitte, or Accenture, and industry-specific publications and white papers from health IT companies (like Epic, Cerner, or Meditech). Other sources included organizations including the American Hospital Association (AHA) and the National Association for Healthcare Quality (NAHQ).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings show that the implementation costs associated with EHR implementation are based on a wide range of factors such as the size of the health facility, availability of financial resources, and the residency and number of physicians. According to the findings, the implementation costs of EHR implementation are related to purchase of the purchase and installation of software and hardware, upgrade or optimization of the existing records program, and training of the end-users (Shestel, 2020; Gupta, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The hardware costs include computer equipment such as desktop computers, laptops, servers, storage, related ancillary equipment such as printers, scanners, monitors, and networking equipment such as routers and wiring. The software costs were associated license or maintenance costs for EHR or related software for interfaces and databases while installation costs included vendor and contractor costs. Also, the findings show that there were revenue losses at implementation which were attributable to provider productivity decreases as a result of reduced visit schedules. Further, the implementation process requires staffing costs especially if the new existing team is not conversant with the operation of the new system. On the same note, depending on the type of software and hardware of choice, the ongoing expenditures such as software development maintenance, hardware replacement, vendor transcription fees and training fees are additional costs associated with the implementation of the systems. Often, the implementation costs do not include the maintenance of the system and the associated labor costs.\u003c/p\u003e\u003cp\u003eBased on the statistical information of the case studies, the highest expenditure on EHR implementation by a facility such as the Health Texas Provider Network, which works under an umbrella healthcare system was software licensing and hosting, and end-user practice. The data shows that the Baylor Scott \u0026amp; White Health system focuses on procuring high-end software licensing and hosting for its independent healthcare facilities and invests heavily on end-user practice to promote the quality of healthcare service provided and the efficiency parameters of the end-product. As such, it can be inferred that most healthcare facilities under an umbrella healthcare company and offers services to patients across different socioeconomic spectrums invest on improved efficiency parameters through procurement of high-end and software licenses and hosts, and training of their staff to improve patient outcomes, increase ROI and reduce operational costs. Also, based on the statistical information derived from the case study of Belleville Family Medical Clinic, a large proportion of implementation costs are incurred on hardware equipment and compensation for the project management team. The findings show that the successful implementation of EHR in a healthcare facility is influenced by the quality of the hardware equipment and the skills of the project management team (Janssen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to the findings of the case studies of the 16 small-group primary care in the 14 states, healthcare facilities also spend on communication costs and staff training (Yeung et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The findings highlight the significance of effective communication between stakeholders and end-user training on EHR implementation.\u003c/p\u003e\u003cp\u003eIn terms of ROI and break-even points, the quantitative research results show that the three case studies reported positive ROI and break-even points of less than one year. The quantitative results are supported by the qualitative findings which highlighted potential benefits of implementation of EHR to health stakeholders in terms of efficient operations, delivery of high-quality health care services, and reduction of operational costs (Baillieu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, as reported by Modi \u0026amp; Feldman (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while the implementation of EHRs is considered as an innovation enabler with potential benefits, it is not an automatic guarantee of conversion of the potential benefits to realized benefits (Lewkowicz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is reported that the implementation process is a complex endeavor and involves different challenges in terms of financial resources, uncertainty about cost recovery, and adaptation to changes by the healthcare professionals (De Benedictis et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From an economic perspective, the uncertainty of cost recovery is a significant concern which highlights the need for cost-benefit analysis of EHR implementations (Shestel, 2020). In terms of efficiency metrics, the findings show that the important statistical metrics for efficiency of the systems include the percentage of system uptime and system downtime. The system downtime can be caused by different factors or events such as scheduled or unexpected power outage, network issues and server issues. Other economic efficiency metrics include end-user metrics, clinical metrics and system metrics (Tayefi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion and Policy Implications","content":"\u003cp\u003eThe current quantitative research and meta-analytic review investigated the economics of Electronic Health Records (EHRs) by conducting a comparative economic analysis of EHR Implementations in varied healthcare settings with a focus on assessing workflow efficiency and patient outcomes across socioeconomic spectrums. Practically, it is quite challenging to quantify a return on EHR implementation investments considering the long-term measurement requirements, intangible criteria in patient outcomes, and the lack of detailed financial data relating to gains and/or savings directly attributable to an EHR system. However, the results show that there are specific indicators including ROI, implementation costs, efficiency metrics and patient outcomes, which can be used as economic indicators for EHR implementation. The findings show how economic factors and healthcare settings influence the effectiveness and efficiency of EHR systems, providing valuable insights for policymakers, healthcare providers, and technology developers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eAuthors\u0026rsquo; information (optional)\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eNot applicable. This study used only publicly available secondary data and did not involve human participants or identifiable human data. However, the research adhered to the principles outlined in the Declaration of Helsinki (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wma.net/policies-post/wma-declaration-of-helsinki/\u003c/span\u003e\u003cspan address=\"https://www.wma.net/policies-post/wma-declaration-of-helsinki/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in ensuring ethical use and interpretation of health-related data.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.G. (Christopher Gransberry) served as the Principal Investigator and led the conceptual design, literature review, case study selection, data analysis, and drafting of the original manuscript. Z.B. (Zeynep Behjet) contributed to refining the methodological framework and interpreting findings through a clinical and health informatics lens. T.M. (Tonjua McCullough) provided guidance on economic evaluation models and contributed to the cost analysis and ROI interpretation. J.F.S. (Jaclyn Felder-Strauss) supported the analysis of financial metrics and assisted in aligning economic indicators with healthcare accounting principles.All authors\u0026mdash;C.G., Z.B., T.M., and J.F.S.\u0026mdash;substantively revised the manuscript, approved the submitted version (and any substantially modified version involving their contributions), and agreed to be personally accountable for their own contributions. Each author commits to ensuring that any questions related to the accuracy or integrity of any part of the work, even those in which they were not directly involved, will be appropriately investigated, resolved, and the resolution documented in the literature.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors wish to thank their respective institutions for supporting interdisciplinary research in health information systems and accounting departments.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eAll data generated or analyzed during this study are included in this published article. The datasets supporting the conclusions of this article are derived from publicly available sources, including published literature, government reports, and institutional white papers, etc. cited within the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaillieu R, Hoang H, Sripipatana A, Nair S, Lin SC. (2020). Impact of health information.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003etechnology optimization on. clinical quality performance in health centers: A national cross sectional study. PLoS ONE, 15(7), e0236019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Benedictis A, Lettieri E, Gastaldi L, Masella C, Urgu A, Tartaglini D. (2020). Electronic.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMedical Records implementation in hospital. An empirical investigation of individual and.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eorganizational determinants. PLoS ONE, 15(6), e0234108.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDugas M, Tapuria A, Bruland P, Delaney B, Kalra D, Curcin V, Informatics T. (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConceptual design. implementation, and evaluation of generic and Standard-Compliant data.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003etransfer into electronic health records. Appl Clin Inf, 11(03), 374\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFennelly O, Cunningham C, Grogan L, Cronin H, O\u0026rsquo;Shea C, Roche M, O\u0026rsquo;Hare N. (2020). Successfully implementing a national electronic health record: a rapid umbrella.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ereview. Int J Med Informatics, 144, 104281.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta D. July 20). How to build an EHR system? Features and cost breakdown. Appinventiv; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ehttps://\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eappinventiv.com/blog/ehr-software-development/\u003c/span\u003e\u003cspan address=\"http://appinventiv.com/blog/ehr-software-development/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang MZ, Gibson CJ, Terry AL. (2018). Measuring electronic health record use in primary.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ecare. a scoping review. Appl Clin Inf, \u003cem\u003e9\u003c/em\u003e(01), 015\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJang Y, Lortie MA, Sanche S. (2014). Return on investment in electronic health records in.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eprimary care practices. : a mixed-methods study. JMIR Med Inf, 2(2), e3631.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanssen A, Donnelly C, Elder E, Pathmanathan N, Shaw T. (2021). Electronic medical record.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eimplementation in tertiary care. factors influencing adoption of an electronic medical record.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ein a cancer centre. BMC Health Serv Res, 21(1), 1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen KH, Wright C, Simpson D, Woods L, Comans T, Sullivan C. (2022). Economic.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eevaluation and analyses. of hospital-based electronic medical records (EMRs): a scoping review of international literature. NPJ Digit Med, 5(1), 29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMelnick ER, Sinsky CA, Krumholz HM. (2021). Implementing measurement science for.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eelectronic health record use. JAMA, 325(21), 2149\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMenachemi N, Brooks RG. (2005). Exploring the return on investment associated with health.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003einformation technologies. \u003cem\u003eFlorida State University College of Medecine: Center for Patient Safety\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eModi S, Feldman SS. (2022). The Value of Electronic Health Records Since the Health.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInformation Technology for Economic. and Clinical Health Act: Systematic Review. JMIR.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMedical, Informatics. 10(9), e37283.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLewkowicz D, Wohlbrandt A, Boettinger E. (2020). Economic impact of clinical decision.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003esupport interventions based on electronic health records. BMC Health Services.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eResearch. 20(1), 1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRudin RS, Friedberg MW, Shekelle P, Shah N, Bates DW. (2020). Getting value from.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eelectronic health records. research needed to improve practice. Annals of Internal.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMedicine. 172(11_Supplement), S130-S136.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTayefi M, Ngo P, Chomutare T, Dalianis H, Salvi E, Budrionis A, Godtliebsen F. (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChallenges. and opportunities beyond structured data in analysis of electronic health.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003erecords. Wiley Interdisciplinary Reviews: Comput Stat, 13(6), e1549.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYeung K, Richards J, Goemer E, Lozano P, Lapham G, Williams E, Bradley K. (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCosts of using. evidence-based implementation strategies for behavioral health integration in a.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003elarge primary care system. Health Serv Res, 55(6), 913\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Reliability, Latency, Efficiency Parameters, Patient Outcomes, ROI","lastPublishedDoi":"10.21203/rs.3.rs-6882060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6882060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the economics of Electronic Health Records (EHRs) by conducting a comparative economic analysis of EHR implementations in varied healthcare settings with a focus on assessing workflow efficiency and patient outcomes across socioeconomic spectrums.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethodology\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe research adopted a mixed methodological framework that included both qualitative and quantitative analysis. The qualitative methodology involved a detailed meta-analytic and systematic review of literature while the quantitative methodology involved statistical analysis of data associated with the economic aspects of EHR implementation. The review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFindings\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe costs related to hardware were associated with computer equipment, related ancillary equipment and networking. The costs related to software were associated license or maintenance costs for EHR or related software Installation costs included vendor and contractor costs. The associated ROI and break-even points for the implementation of the EHR were computed based on the model by Jang et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The performance efficiency metrics for the systems are based on availability, reliability, and latency (speed). The different patient outcomes associated with EHR implementation can include hospital costs, efficiency of admission process, quality of healthcare services and the recovery time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eImplementation costs, ROI, efficiency metrics, and patient outcome measures across different healthcare settings can be used to uncover the economic viability of EHR implementation.\u003c/p\u003e","manuscriptTitle":"The Economics of Electronic Health Records (EHRs): A Comparative Analysis of Implementation Costs, ROI, and Efficiency Metrics Across Healthcare Settings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 14:24:54","doi":"10.21203/rs.3.rs-6882060/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4ea37df-67db-43de-a68b-918c3ea1f2f9","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-16T11:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-17 14:24:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6882060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6882060","identity":"rs-6882060","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}