JMIR Publications

ABSTRACT

Background:

Patients with mood or psychotic disorders experience high rates of unplanned hospital readmissions. Predicting the likelihood of readmission can guide discharge decisions and optimize patient care.

Objective:

The purpose of this study is to evaluate the predictive power of structured variables from electronic health records (EHRs) for all-cause readmission across multiple sites within the Mass General Brigham (MGB) health systems and to assess the transportability of prediction models between sites.

Methods:

This retrospective, multi-site study analyzed structured variables from EHRs separately for each site to develop in-site prediction models. The transportability of these models was evaluated by applying them across different sites. The predictive performance was measured using the F1 score, and additional adjustments were made to account for differences in predictor distributions.

Results:

The study found that the relevant predictors of readmission varied significantly across sites. For instance, the length of stay was a strong predictor at only three of the four sites. In-site prediction models achieved an average F1 score of 0.666, whereas cross-site predictions resulted in a lower average F1 score of 0.551. Efforts to improve transportability by adjusting for differences in predictor distributions did not lead to better performance.

Conclusions:

The findings indicate that individual site-specific models are necessary to achieve reliable prediction accuracy. Furthermore, the results suggest that the current set of predictors may be insufficient for cross-site model transportability, highlighting the need for more advanced predictor variables and predictive algorithms to gain robust insights into the factors influencing early psychiatric readmissions.