The effect of number of healthcare visits on study sample selection in electronic health record data.

Laura Rasmussen-Torvik,Nicholas Smith,Bernard Black,Elizabeth Chrischilles,Al'Ona Furmanchuk,John Meurer,Alexander Stoddard,Kristen Osinski,Abel Kho

doi:10.23889/ijpds.v5i1.1156

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.ConclusionIn 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.Key wordsElectronic Health Records, Sampling Studies, Prevalence, Methods

Highlights

Few studies have addressed how to select a study sample when using electronic health record (EHR) data
The objective of this report was to 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, i.e. the proportion of individuals in a defined population that have a disease during a specified time period
The prevalence of all three diseases increased as the number of visits required to enter the study sample increased across all health systems

Summary

Introduction

Few studies have addressed how to select a study sample when using electronic health record (EHR) data. Methods: Year 2016 EHR data from three health systems 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. Results: Across all health systems, increasing the minimum required number of visits to be included in the study sample monotonically increased crude period prevalence estimates. Researchers must specify criteria for determining which patients have sufficient information in the EHR to be included in the study sample. Often, this sample is defined by requiring that persons have a minimum number of visits in a defined period (for example 2 visits in a 3 year period 2)

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Conclusion