Using Electronic Health Records in Longitudinal Studies: Estimating Patient Attrition

Abstract

Background: Electronic health records (EHRs) provide rich data on many domains not routinely available in other data, as such, they are a promising source to study changes in health outcomes using longitudinal study designs (eg, cohort studies, natural experiments, etc.). Yet, patient attrition rates in these data are unknown.

Objective: The objective of this study was to estimate overall and among adults with diabetes or hypertension: (1) patient attrition over a 3-year period at community health centers; and (2) the likelihood that patients with Medicaid permanently switched their source of primary care.

Research design: A retrospective cohort study of 2012-2017 data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network of community health centers were used to assess EHR data attrition. Oregon Medicaid enrollment and claims data were used to estimate the likelihood of changing the source of primary care.

Subjects: A total of 827,657 patients aged 19-64 with ≥1 ambulatory visit from 76 community health center systems across 20 states. In all, 232,891 Oregon Medicaid enrollees (aged 19-64) with a gap of ≥6 months following a claim for a visit billed to a primary care source.

Measures: Percentage of patients not returning within 3 years of their qualifying visit (attrition). The probability that a patient with Medicaid permanently changed their primary care source.

Results: Attrition over the 3 years averaged 33.5%; attrition rates were lower (<25%) among patients with diabetes and/or hypertension. Among Medicaid enrollees, the percentage of provider change after a 6-month gap between visits was 12% for community health center patients compared with 39% for single-provider practice patients. Over 3 years, the likelihood of a patient changing to a new provider increased with length of time since their last visit but remained lowest among community health center patients.

Conclusion: This study demonstrates the use of the EHR dataset is a reliable source of data to support longitudinal studies while highlighting variability in attrition by primary care source and chronic conditions.

FIGURE 1.

Percent of primary care (PC) patients who changed PC provider by time elapsed since their visit to their last provider, stratified by year cohort. The percent of patients who changed provider was modeled using generalized estimating equation (GEE) logistic regression. As patients could have multiple gaps at a specified length (multiple gaps of 6 mo or more between visits resulting in multiple observations per patient within each analysis), we used GEE models to account for within-patient correlation using a robust sandwich variance estimator. GEE models included PC provider type, cohort year, and the interaction term between those 2 terms. Odds ratios from GEE logistic models were transformed to estimate predicted probabilities of provider change has occurred. Models were stratified by the year in which the PC visit starting the time interval occurred.

FIGURE 2.

Percent of PC patients in the 2014 cohort changed their PC provider by time elapsed since their visit to their last provider, stratified by disease diagnosis (diabetes or hypertension). The percent of patients who changed provider was modeled using GEE logistic regression. As patients could have multiple gaps at a specified length (multiple gaps of 6 mo or more between visits resulting in multiple observations per patient within each analysis), we used GEE models to account for within-patient correlation using a robust sandwich variance estimator. GEE models included PC provider type, cohort year, and the interaction term between those 2 terms. Odds ratios from GEE logistic models were transformed to estimate predicted probabilities of provider change has occurred. Models were stratified by the year in which the PC visit starting the time interval occurred. Patients with both diabetes and hypertension were included in both cohorts. Results for time intervals starting in 2014 are presented; results for 2012 and 2013 are included in Appendix 3 (Supplemental Digital Content 3, http://links.lww.com/MLR/B970). DM indicates diabetes mellitus; GEE, generalized estimating equation; HTN, hypertension; PC, primary care.