Discussion
REGRESSION
In this clinical trial, we found that hospital readmission prediction models that used traditional parametric regression approaches with electronic health record data until the time of hospital discharge performed similar to previously published models with AUCs ranging from 0.60 to 0.70Our findings reveal several important insights for future implementation and research efforts. Prediction in the wearable arm was significantly greater than the smartphone arm, but the difference was small (AUC 0.85 for wearable vs. 0.84 for smartphone). Prior work has found that physical activity data collected by these devices is similar in accuracyA recent systematic review indicated that machine learning methods showed promise for improving hospital readmission models but that more rigorous evaluation was neededThis study has several strengths. First, we evaluated an important clinical outcome and used several data sources including electronic health records and state databases to identify hospital readmissions. Second, we enrolled a diverse patient sample that of which 47% were 50 years or older, 53% were Black or Hispanic, and 26% had Medicaid. Third, we evaluated 5 different modeling techniques including traditional parametric and nonparametric machine learning approaches and found consistent results across these models. Fourth, we collected remote patient monitoring data on patient behavior after hospital discharge, which has not traditionally been integrated into hospital readmission prediction models. Fifth, we conducted a pragmatic, randomized trial to provide a rigorous assessment of the difference in prediction between two of the most common devices used for remote patient monitoring.