The DOI matches its Crossref record; 131 of the 132 references it cites were found in a registry or an index.
Using Electronic Health Records for Population Health Research: A Review of Methods and Applications
Open accessAbstract
The use and functionality of electronic health records (EHRs) have increased rapidly in the past decade. Although the primary purpose of EHRs is clinical, researchers have used them to conduct epidemiologic investigations, ranging from cross-sectional studies within a given hospital to longitudinal studies on geographically distributed patients. Herein, we describe EHRs, examine their use in population health research, and compare them with traditional epidemiologic methods. We describe diverse research applications that benefit from the large sample sizes and generalizable patient populations afforded by EHRs. These have included reevaluation of prior findings, a range of diseases and subgroups, environmental and social epidemiology, stigmatized conditions, predictive modeling, and evaluation of natural experiments. Although studies using primary data collection methods may have more reliable data and better population retention, EHR-based studies are less expensive and require less time to complete. Future EHR epidemiology with enhanced collection of social/behavior measures, linkage with vital records, and integration of emerging technologies such as personal sensing could improve clinical care and population health.
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“Nevertheless, they have weaknesses inherent to an electronic health records study,29 such as the unknown completeness of records, no validation of diagnoses, and sparse information on socioeconomic and lifestyle factors.”
From 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records (2021) · cites this paper as [29]
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“Electronic health records (EHR) can be employed to identify healthcare use and outcomes among specified, large patient groups, which may not be possible through other means [25,26].”
From The Impact of demographic, socio-economic and geographic factors on mortality risk among people living with dementia in England (2002–2016) (2021) · cites this paper as [25]
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Metadata
- Authors
- Casey, J. A., Schwartz, B. S., Stewart, W. F., & Adler, N. E.
- Affiliations
- University of California, San Francisco (US)
- Johns Hopkins University (US)
- Sutter Health (US)
- Venue
- Annual Review of Public Health
- Publisher
- Annual Reviews
- Published
- 2016-03-18
- Volume
- 37
- Issue
- 1
- Pages
- 61-81
- ISSN
- 0163-7525 (ISSN-L)
- 1545-2093
- PMC
- PMC6724703
- OpenAlex
- W2140302476
- Cited by
- 590
- References
- 132
- Citations by year
- 2026: 64 · 2025: 62 · 2024: 76 · 2023: 75 · 2022: 75 · 2021: 70 · 2020: 70 · 2019: 47 · 2018: 28 · 2017: 19 · 2016: 3
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Public Health (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Environmental Health Sciences (K99ES027023, R00ES027023)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- electronic health records, population health research, epidemiological study design, predictive modeling, natural experiment, digital phenotyping
- MeSH terms
- Age Factors, Data Collection, Environment, Humans, Models, Theoretical, Sex Factors, Social Environment, Socioeconomic Factors, Reproducibility of Results, Epidemiologic Research Design, Health Behavior, Diagnostic Techniques and Procedures, Vital Signs, Electronic Health Records
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