A cost-benefit analysis of electronic medical records in primary care
Open accessAbstract
Electronic medical record systems improve the quality of patient care and decrease medical errors, but their financial effects have not been as well documented. The purpose of this study was to estimate the net financial benefit or cost of implementing electronic medical record systems in primary care. We performed a cost-benefit study to analyze the financial effects of electronic medical record systems in ambulatory primary care settings from the perspective of the health care organization. Data were obtained from studies at our institution and from the published literature. The reference strategy for comparisons was the traditional paper-based medical record. The primary outcome measure was the net financial benefit or cost per primary care physician for a 5-year period. The estimated net benefit from using an electronic medical record for a 5-year period was 86,400 US dollars per provider. Benefits accrue primarily from savings in drug expenditures, improved utilization of radiology tests, better capture of charges, and decreased billing errors. In one-way sensitivity analyses, the model was most sensitive to the proportion of patients whose care was capitated; the net benefit varied from a low of 8400 US dollars to a high of 140,100 US dollars . A five-way sensitivity analysis with the most pessimistic and optimistic assumptions showed results ranging from a 2300 US dollars net cost to a 330,900 US dollars net benefit. Implementation of an electronic medical record system in primary care can result in a positive financial return on investment to the health care organization. The magnitude of the return is sensitive to several key factors.
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Electronic patient data management is expensive, and hospitals in underprivileged regions might be unable to afford participation in studies requiring it, potentially perpetuating issues of bias and fairness.
“Electronic patient data management is expensive 20 , and hospitals in underprivileged regions might be unable to afford participation in studies requiring it, potentially perpetuating the aforementioned issues of bias and fairness.”
From Secure, privacy-preserving and federated machine learning in medical imaging (2020) · cites this paper as [20]
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- Authors
- Wang, S. J., Middleton, B., Prosser, L. A., Bardon, C. G., Spurr, C. D., Carchidi, P. J., Kittler, A. F., Goldszer, R. C., Fairchild, D. G., Sussman, A. J., Kuperman, G. J., & Bates, D. W.
- Affiliations
- Harvard University (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Wang, Samuel J.
- Venue
- The American Journal of Medicine
- Publisher
- Elsevier BV
- Published
- 2003-04
- Volume
- 114
- Issue
- 5
- Pages
- 397-403
- ISSN
- 0002-9343 (ISSN-L)
- OpenAlex
- W2153080225
- Cited by
- 740
- References
- 41
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 7 · 2023: 17 · 2022: 22 · 2021: 24 · 2020: 25 · 2019: 15 · 2018: 21 · 2017: 32 · 2016: 37 · 2015: 39 · 2014: 60 · 2013: 63 · 2012: 49
- FWCI
- 60.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The American Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- electronic health records, primary care, outpatient care, cost-benefit analysis, return on investment, paper medical records, imaging utilization, billing errors, capitation
- MeSH terms
- Computers, Cost-Benefit Analysis, Efficiency, Humans, Office Management, Primary Health Care, Sensitivity and Specificity, Software, Medical Records Systems, Computerized, Drug Costs
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