TY - JOUR
T1 - The effect of number of healthcare visits on study sample selection in electronic health record data
AU - Rasmussen-Torvik, L. J.
AU - Furmanchuk, A.
AU - Stoddard, A. J.
AU - Osinski, A. I.
AU - Meurer, J. R.
AU - Smith, N.
AU - Chrischilles, E.
AU - Black, B. S.
AU - Kho, A.
N1 - Funding Information:
This work was funded by CDRN-1306-04737-IC, CDRN-1306-04631, CDC -5U18DP006120-04-00, CDC 1U18DP006120-01, NCATS NIH UL1TR001436, and NIDDK 5U18DP006120-02. The authors would like to acknowledge Zahra Hosseinian and Charon Gladfelter for all their administrative help with this project.
Publisher Copyright:
April 2020 © The Authors. Open Access under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.en)
PY - 2020/1/30
Y1 - 2020/1/30
N2 - Introduction Few studies have addressed how to select a study sample when using electronic health record (EHR) data. Objective 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. Methods Year 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). 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. The rate at which prevalence estimates increased with number of visits varied across sites and across diseases. Conclusion In 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.
AB - Introduction Few studies have addressed how to select a study sample when using electronic health record (EHR) data. Objective 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. Methods Year 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). 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. The rate at which prevalence estimates increased with number of visits varied across sites and across diseases. Conclusion In 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.
KW - Electronic Health Records
KW - Methods
KW - Prevalence
KW - Sampling Studies
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U2 - 10.23889/ijpds.v5i1.1156
DO - 10.23889/ijpds.v5i1.1156
M3 - Article
C2 - 32864475
AN - SCOPUS:85086669541
VL - 5
JO - International Journal of Population Data Science
JF - International Journal of Population Data Science
SN - 2399-4908
IS - 1
M1 - 18
ER -