Using Machine Learning to Predict Hospital Disposition With Geriatric Emergency Department Innovation Intervention

Abstract

Study objective: The Geriatric Emergency Department Innovations (GEDI) program is a nurse-based geriatric assessment and care coordination program that reduces preventable admissions for older adults. Unfortunately, only 5% of older adults receive GEDI care because of resource limitations. The objective of this study was to predict the likelihood of hospitalization accurately and consistently with and without GEDI care using machine learning models to better target patients for the GEDI program.

Methods: We performed a cross-sectional observational study of emergency department (ED) patients between 2010 and 2018. Using propensity-score matching, GEDI patients were matched to other older adult patients. Multiple models, including random forest, were used to predict hospital admission. Multiple second-layer models, including random forest, were then used to predict whether GEDI assessment would change predicted hospital admission. Final model performance was reported as the area under the curve using receiver operating characteristic models.

Results: We included 128,050 patients aged over 65 years. The random forest ED disposition model had an area under the curve of 0.774 (95% confidence interval [CI] 0.741 to 0.806). In the random forest GEDI change-in-disposition model, 24,876 (97.3%) ED visits were predicted to have no change in disposition with GEDI assessment, and 695 (2.7%) ED visits were predicted to have a change in disposition with GEDI assessment.

Conclusion: Our machine learning models could predict who will likely be discharged with GEDI assessment with good accuracy and thus select a cohort appropriate for GEDI care. In addition, future implementation through integration into the electronic health record may assist in selecting patients to be prioritized for GEDI care.