ArticleInternational journal of population data science2020
The effect of number of healthcare visits on study sample selection in electronic health record data.
Article in International journal of population data science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Discordance Between Electronic Health Records and Self-Reported Data: Evidence from Traumatic Brain Injury and Colorectal Cancer.Healthcare (Basel, Switzerland) · 2026Article
- An Online Tool for Correcting Performance Measures of Electronic Phenotyping Algorithms for Verification Bias.ACI open · 2024Article
- Diagnostic rate estimation from Medicare records: Dependence on claim numbers and latent clinical features.Journal of biomedical informatics · 2023Article
- The Association of Body Mass Index and Waist Circumference with the Risk of Achilles Tendon Problems: A Nationwide Population-Based Longitudinal Cohort Study.Clinics in orthopedic surgery · 2023Article
- Racial Disparities in Comorbidity Patterns of Early-Onset Liver Cancer: A Machine Learning Analysis.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
introductionFew studies have addressed how to select a study sample when using electronic health record (EHR) data.
objectiveTo examine how changing criterion for number of visits in EHR data required for inclusion in a study sample would impact one basic epidemiologic measure: estimates of disease period prevalence.
methodsYear 2016 EHR data from three Midwestern health systems (Northwestern Medicine in Illinois, University of Iowa Health Care, and Froedtert & the Medical College of Wisconsin, all regional tertiary health care systems including hospitals and clinics) was used to examine how alternate definitions of the study sample, based on number of healthcare visits in one year, affected measures of disease period prevalence. In 2016, each of these health systems saw between 160,000 and 420,000 unique patients. Curated collections of ICD-9, ICD-10, and SNOMED codes (from CMS-approved electronic clinical quality measures) were used to define three diseases: acute myocardial infarction, asthma, and diabetic nephropathy).
resultsAcross all health systems, increasing the minimum required number of visits to be included in the study sample monotonically increased crude period prevalence estimates. The rate at which prevalence estimates increased with number of visits varied across sites and across diseases.
conclusionsIn addition to providing thorough descriptions of case definitions, when using EHR data authors must carefully describe how a study sample is identified and report data for a range of sample definitions, including minimum number of visits, so that others can assess the sensitivity of reported results to sample definition in EHR data.
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Registered trials
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