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This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation‐wide screening programs. The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases, targeting the rapidly increasing number of adults in the country with diabetes. In the second example, the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the months following discharge. Ways in which healthcare needs and the clinical operations context influenced the approach to designing or deploying the AI systems are highlighted, illustrating the multiplicity of factors that shape the requirements for successful large‐scale deployments of AI systems that are deeply embedded within clinical workflows. In the first example, the choice was made to use the system in a semi‐automated (vs. fully automated) mode as this was assessed to be more cost‐effective, though still offering substantial productivity improvement. In the second example, machine learning algorithm design and model execution trade‐offs were made that prioritized key aspects of patient engagement and inclusion over higher levels of predictive accuracy. The article concludes with several lessons learned related to deploying AI systems within healthcare settings, and also lists several other AI efforts already in deployment and in the pipeline for Singapore's public healthcare system.
This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation‐wide screening programs. The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases, targeting the rapidly increasing number of adults in the country with diabetes. In the second example, the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the months following discharge. Ways in which healthcare needs and the clinical operations context influenced the approach to designing or deploying the AI systems are highlighted, illustrating the multiplicity of factors that shape the requirements for successful large‐scale deployments of AI systems that are deeply embedded within clinical workflows. In the first example, the choice was made to use the system in a semi‐automated (vs. fully automated) mode as this was assessed to be more cost‐effective, though still offering substantial productivity improvement. In the second example, machine learning algorithm design and model execution trade‐offs were made that prioritized key aspects of patient engagement and inclusion over higher levels of predictive accuracy. The article concludes with several lessons learned related to deploying AI systems within healthcare settings, and also lists several other AI efforts already in deployment and in the pipeline for Singapore's public healthcare system.
We thank the support and guidance received from the Ministry of Health (Singapore), Integrated Health Information Systems (IHIS) Senior Management, and all of our partners from the Singapore Public Healthcare sector: Agency of Integrated Care (Singapore), Changi General Hospital, Khoo Teck Huat Hospital, National University Hospital, Ng Teng Fong General Hospital, Singapore General Hospital, Tan Tock Seng General Hospital, Singapore National Eye Center (SNEC), and Singapore Eye Research Institute (SERI). We also thank Professor Emeritus of Marketing Jerry Wind of the Wharton School of Business for his encouragement to create this article in order to share information on how AI is changing the nature of customer engagement in healthcare. There was no direct funding involved in creating this case study article. The lead author, Professor Emeritus Steven Miller, did all the required work to prepare and write this article on a volenteer basis. All of the other co‐authors are full‐time employees of Integrated Health Information Systems. Their efforts to provide inputs to this article and to reveiw multiple drafts was all done on an "extra‐curricular" basis on top of their every day work. No additional funds or compensation was provided to them for working on this article.
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.