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

Autor:	Abisheganaden J; Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital National Healthcare Group Singapore Singapore.; National Working Group for the Hospital to Home Program Singapore Singapore., Lee KH; Department of Family Medicine and Continuing Care, Singapore General Hospital, SingHealth Group Singapore Singapore.; SingHealth Community Hospitals, SingHealth Group Singapore Singapore., Low LL; Department of Family Medicine and Continuing Care, Singapore General Hospital, SingHealth Group Singapore Singapore.; Population Health and Integrated Care Office, SingHealth Group Singapore Singapore., Shum E; Office of Community Development Changi General Hospital, SingHealth Group Singapore Singapore., Goh HL; Data Analytics and AI Department, Integrated Health Information Systems Singapore Singapore., Ang CGL; Data Analytics and AI Department, Integrated Health Information Systems Singapore Singapore., Ta AWA; Data Analytics and AI Department, Integrated Health Information Systems Singapore Singapore., Miller SM; School of Computing and Information Systems Singapore Management University Singapore Singapore.
Jazyk:	angličtina
Zdroj:	Health care science [Health Care Sci] 2023 May 10; Vol. 2 (3), pp. 153-163. Date of Electronic Publication: 2023 May 10 (Print Publication: 2023).
DOI:	10.1002/hcs2.44
Abstrakt:	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. Competing Interests: The authors declare there are no conflict of interest. (© 2023 The Authors. Health Care Science published by John Wiley & Sons Ltd on behalf of Tsinghua University Press.)
Databáze:	MEDLINE
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