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10 May 2023
Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model
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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.
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Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model
(
)
Abstract
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.
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Acknowledgements
We thankfully acknowledge the support and guidance received from the Ministry of Health (Singapore), Integrated Health Information Systems (IHIS), and all of our partners from the Singapore Public Healthcare sector: Agency of Integrated Care, Singapore Health Services (known more widely as SingHealth, including Singapore General Hospital and Changi General Hospital), National Healthcare Group (including Tan Tock Seng General Hospital and Khoo Teck Puat Hospital), and National University Health System (including National University Hospital and Ng Teng Fong General Hospital). We also thank Lauder Professor Emeritus of Marketing Jerry Wind of the Wharton School of Business for his encouragement to create this article further elaborating on the Home to Hospital program to share information on how AI is changing the nature of customer engagement in healthcare. Funding was not required for the preparation of this article. Funding for the efforts discussed in this article—the H2H Program and the supporting multiple readmissions AI prediction model—was provided by the Singapore Ministry of Health as well as by all of the other public healthcare entities.
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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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