Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals

Abstract

Singapore is one of the first known countries to implement an individual-centric discharge process across all public hospitals to manage frequent admissions—a perennial challenge for public healthcare, especially in an aging population. Specifically, the process provides daily lists of high-risk patients to all public hospitals for customized discharge procedures within 24hrs of admission. We analyzed all public hospital admissions (N =150,322) in a year. Among four models, the gradient boosting machine performed the best (AUC = 0.79) with a positive predictive value set at 70%. Interestingly, the cumulative length of stay (LOS) in the past 12 months was a stronger predictor than the number of previous admissions, as it is a better proxy for acute care utilization. Another important predictor was the “number of days from previous non-elective admission,” which is different from previous studies that included both elective and non-elective admissions. Of note, the model did not include LOS of the index admission—a key predictor in other models—since our predictive model identified frequent admitters for pre-discharge interventions during the index (current) admission. The scientific ingredients that built the model did not guarantee its successful implementation—an “art” that requires the alignment of processes, culture, human capital, and senior management sponsorship. Change management is paramount, otherwise data-driven health policies, no matter how well-intended, may not be accepted or implemented. Overall, our study demonstrated the viability of using artificial intelligence (AI) to build a near real-time nationwide prediction tool for individual-centric discharge, and the critical factors for successful implementation.

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