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 24 h 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.
Highlights
Frequent admissions (FAs) to public hospitals are a global and perennial challenge [1], exacerbated during pandemics
We present one of the first-known implementations of a nationwide predictive model for individual-centric discharge across all public hospitals in Singapore
Given the need to identify frequent admitters prior to discharge, we developed a predictive model to risk stratify patients for enrolment into the individual-centric discharge planning program across all Singapore public hospitals
Summary
Frequent admissions (FAs) to public hospitals are a global and perennial challenge [1], exacerbated during pandemics. There are numerous solutions at the proof-of-concept stage; few are implemented at scale. There are two roadblocks: One, research funding stops at knowledge creation (e.g., publication/patent) or prototypes. There is a massive gap in funding implementation, leaving wildly successful prototypes to languish among the weeds [2]. The brilliant scientists that created these wildly successful prototypes are hardly suited to carry them to implementation—the skillsets needed are diametrically different at various points of the value chain from inception to implementation. The art of analytics (implementation) is as important, if not more, than the science of analytics (prototype creation). As described by managerial epidemiologists [3,4]—a sub-discipline that applies epidemiologic techniques to healthcare management—successful implementation requires artful alignment of holes across the
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