02. Saving lives, reducing costs: Predicting healthcare utilization from whole-person health using partially validated electronic health records data

A standardized measure of whole-person health in electronic health records (EHR) could be instrumental in identifying at-risk patients, preventing disease, and reducing patient engagement in the healthcare system. The allostatic load index (ALI), calculated from ten component stressors of the cardiovascular, metabolic, and inflammatory regulation systems, offers a promising estimate of holistic health. The ALI can be calculated from EHR data, but they are prone to error and missingness. Calculating the ALI from non-validated data can lead to inaccurate conclusions about patient health and the association with healthcare utilization. To address this challenge, EHR data for 1000 patients from a large academic health system were partially validated, with expert chart review completed for 100 patients to improve their data quality and completeness. Using machine learning techniques, these data were used to predict patient engagement in the healthcare system (hospitalization or emergency department visit) based on ALI. To better explore how ALI can predict whether people engage in the healthcare system, we explore additional models, evaluate methods to fill in information gaps for the 900 remaining patients, and assess different strategies (like imputation) for dealing with data quality issues for unvalidated patients.

allostatic load index (ALI)

electronic health record (EHR)

partially-validated

missing data 

Beginner

Knowledge

Women in Statistics and Data Science 2025