ArticleCurrent epidemiology reports2017
Addressing Bias in Electronic Health Record-Based Surveillance of Cardiovascular Disease Risk: Finding the Signal Through the Noise.
Article in Current epidemiology reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Structural requirements for intelligent clinical digital twins in feedback-driven care.npj health systems · 2026Review
- Defining Prenatal Care Surveillance Metrics Using Electronic Health Record Data.JAMA health forum · 2026Article
- How representative are electronic health records? A record linkage study using individual-level census data.Social science & medicine (1982) · 2026Article
- Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification.Current heart failure reports · 2026Review
- Access to care affects electronic health record reliability and AI-driven disease prediction.Nature health · 2026Article
- Retrospective cohort study applying membership and utilisation criteria to electronic health records for diabetes incidence estimation in an integrated healthcare delivery system in Southern California.BMJ public health · 2026Article
- Modern sources of controls in case-control studies.American journal of epidemiology · 2025Article
- The Lifecycle of Electronic Health Record Data in HIV-Related Big Data Studies: Qualitative Study of Bias Instances and Potential Opportunities for Minimization.Journal of medical Internet research · 2025Article
- Validation of risk assessment models for venous thromboembolism in patients with cancer receiving systemic therapies.Blood advances · 2025Observational
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- Early dysphagia following anterior cervical discectomy and fusion: a centre experience.BMC research notes · 2025Article
- Outdoor artificial light-at-night and cardiometabolic disease risk: an urban perspective from the Catalan GCAT cohort study.American journal of epidemiology · 2025Article
- Atrial Fibrillation Treatment Pathway in the Emergency Department Reduces Median 30-Day Health Service Charges.Journal of the American Heart Association · 2025Article
- Weighted EHR-based prevalence estimates for hypertension at the state and local levels in Louisiana.BMC public health · 2025Article
- Epidemiological methods in transition: Minimizing biases in classical and digital approaches.PLOS digital health · 2025Review
- Assessing Electronic Health Records for Describing Racial and Ethnic Health Disparities: A Research Note.Demography · 2024Article
- Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study.JMIR medical informatics · 2024Article
- Inferring Population HIV Viral Load From a Single HIV Clinic's Electronic Health Record: Simulation Study With a Real-World Example.Online journal of public health informatics · 2024Article
- Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS.Resuscitation plus · 2024Article
- Inconsistency in UK Biobank Event Definitions From Different Data Sources and Its Impact on Bias and Generalizability: A Case Study of Venous Thromboembolism.American journal of epidemiology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purpose of reviewUse of the electronic health record (EHR) for CVD surveillance is increasingly common. However, these data can introduce systematic error that influences the internal and external validity of study findings. We reviewed recent literature on EHR-based studies of CVD risk to summarize the most common types of bias that arise. Subsequently, we recommend strategies informed by work from others as well as our own to reduce the impact of these biases in future research. RECENT
findingsSystematic error, or bias, is a concern in all observational research including EHR-based studies of CVD risk surveillance. Patients captured in an EHR system may not be representative of the general population, due to issues such as informed presence bias, perceptions about the healthcare system that influence entry, and access to health services. Further, the EHR may contain inaccurate information or be missing key data points of interest due to loss to follow-up or over-diagnosis bias. Several strategies, including implementation of unique patient identifiers, adoption of standardized rules for inclusion/exclusion criteria, statistical procedures for data harmonization and analysis, and incorporation of patient-reported data have been used to reduce the impact of these biases. SUMMARY: EHR data provide an opportunity to monitor and characterize CVD risk in populations. However, understanding the biases that arise from EHR datasets is instrumental in planning epidemiological studies and interpreting study findings. Strategies to reduce the impact of bias in the context of EHR data can increase the quality and utility of these data.
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