The impact of electronic health records (EHR) data continuity on prediction model fairness and racial-ethnic disparities

Electronic health records (EHR) data have considerable variability in data completeness across sites and patients. Lack of "EHR data-continuity" or "EHR data-discontinuity", defined as "having medical information recorded outside the reach of an EHR system" can lead to a substantial amount of information bias. The objective of this study was to comprehensively evaluate (1) how EHR data-discontinuity introduces data bias, (2) case finding algorithms affect downstream prediction models, and (3) how algorithmic fairness is associated with racial-ethnic disparities. We leveraged our EHRs linked with Medicaid and Medicare claims data in the OneFlorida+ network and used a validated measure (i.e., Mean Proportions of Encounters Captured [MPEC]) to estimate patients' EHR data continuity. We developed a machine learning model for predicting type 2 diabetes (T2D) diagnosis as the use case for this work. We found that using cohorts selected by different levels of EHR data-continuity affects utilities in disease prediction tasks. The prediction models trained on high continuity data will have a worse fit on low continuity data. We also found variations in racial and ethnic disparities in model performances and model fairness in models developed using different degrees of data continuity. Our results suggest that careful evaluation of data continuity is critical to improving the validity of real-world evidence generated by EHR data and health equity

Depression and Glycemic Control in Hispanic Primary Care Patients with Diabetes

CONTEXT: Maintaining optimal glycemic control is an important goal of therapy in patients with diabetes mellitus. Patients of Hispanic ancestry have been shown to have high rates of diabetes and poor glycemic control (PGC). Although depression is common in adults with diabetes, its relationship to glycemic control remains unclear, especially among Hispanics. OBJECTIVE: To assess the association of depression with PGC in Hispanics. DESIGN: Data from a cross-sectional mental health survey in primary care were crosslinked to the hospital\u27s computerized laboratory database. SETTING: Urban general medicine practice at a teaching hospital. PATIENTS: Two hundred and nine patients (mean [standard deviation] age, 57.1 [10.3] years; 68% females) with recent International Classification of Diseases, Ninth Revision (ICD-9) codes for diabetes mellitus, and 1 or more hemoglobin A1c (HbA1c) tests. MAIN OUTCOME MEASURE: Probability of PGC (HbA1c ≥8%). RESULTS: Probability for PGC steadily increased with severity of depression. Thirty-nine (55.7%) of the 70 patients with major depression had HbA 1c ≥8%, compared with 39/92 (42.4%) in the minimal to mild depression group, and 15/47 (31.9%) in the no depression group (P trend=.01: adjusted odds ratio, 3.27; 95% confidence interval, 1.23 to 8.64, for moderate or severe depression vs no depression). Only 29 (41.4%) of the patients with major depression received mental health treatment in the previous year. CONCLUSIONS: In this primary care sample of Hispanic patients with diabetes, we found a significant association between increasing depression severity and PGC. Yet, less than one half of the patients with moderate or severe depression received mental health treatment in the previous year. Improving identification and treatment of depression in this high-risk population might have favorable effects on diabetic outcomes

The OneFlorida Data Trust: a centralized, translational research data infrastructure of statewide scope

The OneFlorida Data Trust is a centralized research patient data repository created and managed by the OneFlorida Clinical Research Consortium ("OneFlorida"). It comprises structured electronic health record (EHR), administrative claims, tumor registry, death, and other data on 17.2 million individuals who received healthcare in Florida between January 2012 and the present. Ten healthcare systems in Miami, Orlando, Tampa, Jacksonville, Tallahassee, Gainesville, and rural areas of Florida contribute EHR data, covering the major metropolitan regions in Florida. Deduplication of patients is accomplished via privacy-preserving entity resolution (precision 0.97-0.99, recall 0.75), thereby linking patients' EHR, claims, and death data. Another unique feature is the establishment of mother-baby relationships via Florida vital statistics data. Research usage has been significant, including major studies launched in the National Patient-Centered Clinical Research Network ("PCORnet"), where OneFlorida is 1 of 9 clinical research networks. The Data Trust's robust, centralized, statewide data are a valuable and relatively unique research resource