Cross-site predictions of readmission after psychiatric hospitalization with mood or psychotic disorders

Abstract

Patients with mood or psychotic disorders have high rates of unplanned readmission, and predicting readmission likelihood may guide discharge decisions. In this retrospective, multi-site study, we assess the predictive power of various structured variables from electronic health records for all-cause readmission in each site separately and evaluate the generalizability of the in-site prediction models across sites. We find that the set of relevant predictors vary significantly across. For example, length of stay is strongly predictive of readmission at only three out of the four sites. We also find a general lack of cross-site generalizability of the in-site prediction models, with in-site predictions having an average F1 score of 0.666, compared to an average F1 score of 0.551 for cross-site predictions. The generalizability cannot be improved even after adjusting for differences in the distributions of predictors. These results indicate that, with this set of predictors, fitting individual models at each site is necessary to achieve reasonable prediction accuracy. Additionally, they suggest that more sophisticated predictors variables or predictive algorithms are needed to develop generalizable models capable of extracting robust insights into the root causes of early psychiatric readmissions.