Importance of accurately identifying disease in studies using electronic health records
Abstract
Use of routinely collected electronic health data to identify people for epidemiology studies and performance reports can lead to serious bias
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The risk of misclassification bias in studies using routine health data may threaten the validity of study findings.
“The risk of misclassification bias in studies using routine health data, as in many other epidemiological studies, may threaten the validity of study findings [31].”
From The Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement. (2015) · cites this paper as [31]
Codes/algorithms to classify exposures, outcomes, confounders, or effect modifiers subject the research to potential misclassification bias.
“Just as with codes/algorithms used to identify the study population, codes/algorithms to classify exposures, outcomes, confounders, or effect modifiers subject the research to potential misclassification bias.”
From The Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement. (2015) · cites this paper as [31]
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- Authors
- Manuel, D. G., Rosella, L. C., & Stukel, T. A.
- Affiliations
- Statistics Canada (CA)
- University of Toronto (CA)
- Institute for Clinical Evaluative Sciences (CA)
- Corresponding author
- Manuel, D. G.
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2010-08-19
- Volume
- 341
- Issue
- aug19 1
- Pages
- c4226-c4226
- ISSN
- 0007-1447 (ISSN-L)
- 0959-8138
- 1468-5833
- OpenAlex
- W2050091824
- Cited by
- 159
- References
- 25
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 12 · 2023: 11 · 2022: 11 · 2021: 15 · 2020: 11 · 2019: 14 · 2018: 12 · 2017: 10 · 2016: 9 · 2015: 10 · 2014: 6 · 2013: 10 · 2012: 9
- FWCI
- 13.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- BMJ (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- electronic health records, disease identification, misclassification bias, administrative data, performance reporting
- MeSH terms
- Chronic Disease, Humans, Registries, Research Design, Bias, Medical Records Systems, Computerized
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