Pilot Testing the Johns Hopkins Early Discharge Planning Calculator.

Abstract

Objectives: To validate the Johns Hopkins Early Discharge Planning Calculator (JH-EDPC) machine learning algorithm in routine clinical practice for predicting postacute care (PAC) needs and evaluate whether knowledge of predictions influenced physical therapy (PT) consultation rates.

Study design: Pilot controlled clinical validation study conducted from October 2022 to March 2023.

Methods: The JH-EDPC mobile app was deployed to 7 hospitalists (intervention group patients, n = 51 adult inpatients hospitalized ≥ 48 hours); hospitalists on the same units served as controls (patients, n = 111). Predictors included age, admission date, living status, surgery, and daily Activity Measure for Post Acute Care (AM-PAC) mobility scores. Accuracy metrics (area under curve [AUC], sensitivity, specificity, negative predictive value [NPV]) were calculated at a PAC probability cutoff of 0.25 using lowest 48-hour AM-PAC scores. Day-7 PT consultation rates were compared using inverse probability of treatment weighting.

Results: Overall, 11.7% of patients required PAC. The JH-EDPC correctly predicted discharge location for 85% of patients (AUC = 0.77), demonstrating 68% sensitivity, 87% specificity, and a high NPV of 95.4%. Adjusted day-7 PT consultation rates were 38% for intervention vs 35% for controls. Among patients predicted for home discharge, intervention patients received fewer PT consultations (19% vs 23%).

Conclusions: The JH-EDPC successfully integrated into clinical workflows with high specificity and NPV to rule out PAC needs. Findings suggest potential for optimizing hospital resource allocation by directing rehabilitation consultations to patients with the highest need.