Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model  

作　　者：John Abisheganaden Kheng Hock Lee Lian Leng Low Eugene Shum Han Leong Goh Christine Gia Lee Ang Andy Wee An Ta Steven M.Miller 

机构地区：[1]Department of Respiratory and Critical Care Medicine,Tan Tock Seng Hospital,National Healthcare Group,Singapore,Singapore [2]National Working Group for the Hospital to Home Program,Singapore,Singapore [3]Department of Family Medicine and Continuing Care,Singapore General Hospital,SingHealth Group,Singapore,Singapore [4]SingHealth Community Hospitals,SingHealth Group,Singapore,Singapore [5]Population Health and Integrated Care Office,SingHealth Group,Singapore,Singapore [6]Office of Community Development,Changi General Hospital,SingHealth Group,Singapore,Singapore [7]Data Analytics and AI Department,Integrated Health Information Systems,Singapore,Singapore [8]School of Computing and Information Systems,Singapore Management University,Singapore,Singapore

出　　处：《Health Care Science》2023年第3期153-163,共11页科学医疗（英文）

摘　　要：In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.

关 键 词：hospital to home community care hospital to home lessons learned transitional care integrated care multiple readmissions AI prediction model machine learning in healthcare healthcare technology 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]