Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records

TL;DR

This study develops methods to improve the measurement of whole-person health using electronic health records by addressing data missingness and errors through targeted validation and robust statistical modeling.

Contribution

It introduces a novel validation protocol and sampling strategy, combined with semiparametric estimation, to enhance data quality and analysis of the allostatic load index in EHRs.

Findings

Validation increased non-missing ALI components from 6 to 7 per patient.

Residual sampling was most effective for validation.

Higher ALI was associated with increased healthcare utilization.

Abstract

The allostatic load index (ALI) is a 10-component measure of whole-person health. Data from electronic health records (EHR) present a huge opportunity to operationalize the ALI in learning health systems; however, these data are prone to missingness and errors. Validation (e.g., through chart reviews) provides better-quality data, but realistically, only a subset of patients' data can be validated, and most protocols do not recover missing data. Using a representative sample of 1000 patients from the EHR at an extensive learning health system (100 of whom could be validated), we propose methods to design, conduct, and analyze statistically efficient and robust studies of ALI and healthcare utilization. Employing semiparametric maximum likelihood estimation, we robustly incorporate all available patient information into statistical models. Using targeted design strategies, we examine ways to select the most informative patients for validation. Incorporating clinical expertise, we devise a novel validation protocol to promote EHR data quality and completeness. Chart reviews uncovered few errors (99% matched source documents) and recovered some missing data through auxiliary information in patients' charts. On average, validation increased the number of non-missing ALI components per patient from 6 to 7. Through simulations based on preliminary data, residual sampling was identified as the most informative strategy for completing our validation study. Incorporating validation data, statistical models indicated that worse whole-person health (higher ALI) was associated with higher odds of engaging in the healthcare system, adjusting for age.