Can Patient-Reported Outcome Measures Help Predict Unplanned Hospital Readmission?

Administrative data used to predict unplanned hospital readmissions often lack patient-reported symptoms and functional status. Integrating patient-reported outcome measures (PROMs) may improve risk prediction.

To assess the incremental value of PROMs in predicting unplanned readmissions to inform postdischarge monitoring and ongoing care management.

This population-based retrospective cohort study used linked administrative and PROMs data from British Columbia, Canada. Adults discharged from acute care who provided response to the EQ-5D-5L and Veterans RAND 12-Item Health Survey (VR-12) within 60 days were included. Aggregated Cox proportional hazards models were fitted to estimate unplanned readmission risk across 30-, 180-, and 360-day horizons. The primary prediction horizons were 30 and 180 days. The 360-day horizon was a secondary focus. Model performance was assessed using the concordance statistics and calibration, with subgroup analysis for Ambulatory Care Sensitive Conditions (ACSC).

Among 11,177 individuals, observed unplanned readmission rates within 30, 180, and 360 days of discharge were 5.6%, 18.4%, and 25.0%, respectively. Conditional on surviving to weekly landmarks (23-60 days postdischarge), PROMs modestly improved discrimination. For the 180-day horizon following landmarks, the C-index was 0.762 (95% CI, 0.761-0.763) using predictors from administrative data alone, increasing to 0.774 (95% CI, 0.773-0.774) with EQ-5D-5L and 0.782 (95% CI, 0.781-0.783) with VR-12. Similar gains in discrimination were observed at 30-day and 360-day horizons. All models showed adequate calibration. Among patients with ACSCs, including PROMs improved discrimination by 2.4%-3.0%.

PROMs added predictive value for unplanned hospital readmissions, particularly among patients with ACSCs.