Predicting hospitalizations from electronic health record data

Abstract

Objectives: Electronic health record (EHR) data have become increasingly available and may help inform clinical prediction. However, predicting hospitalizations among a diverse group of patients remains difficult. We sought to use EHR data to create and internally validate a predictive model for clinical use in predicting hospitalizations.

Study design: Retrospective observational cohort study.

Methods: We analyzed EHR data in patients 18 years or older seen at Atrius Health from June 2013 to November 2015. We selected variables among patient demographics, clinical diagnoses, medications, and prior utilization to train a logistic regression model predicting any hospitalization within 6 months and validated the model using a separate validation set. We performed sensitivity analysis on model performance using combinations of EHR-derived, claims-derived, or both EHR- and claims-derived data.

Results: After exclusions, 363,855 patient-months were included for analysis, representing 185,388 unique patients. The strongest features included sickle cell anemia (odds ratio [OR], 52.72), lipidoses and glycogenosis (OR, 8.44), heart transplant (OR, 6.12), and age 76 years or older (OR, 5.32). Model testing showed that EHR-only data had an area under the receiver operating characteristic curve (AUC) of 0.84 (95% CI, 0.838-0.853), which was similar to the claims-only data (AUC, 0.84; 95% CI, 0.831-0.848) and combined claims and EHR data (AUC, 0.846; 95% CI, 0.838-0.853).

Conclusions: Prediction models using EHR-only, claims-only, and combined data had similar predictive value and demonstrated strong discrimination for which patients will be hospitalized in the ensuing 6 months.