{"id":6882,"date":"2026-04-27T15:11:58","date_gmt":"2026-04-27T07:11:58","guid":{"rendered":"https:\/\/medicine.nus.edu.sg\/nuhscg\/?p=6882"},"modified":"2026-04-27T15:15:48","modified_gmt":"2026-04-27T07:15:48","slug":"p4-personalised-patient-preference-predictor","status":"publish","type":"post","link":"https:\/\/medicine.nus.edu.sg\/nuhscg\/p4-personalised-patient-preference-predictor\/","title":{"rendered":"P4 (Personalised Patient Preference Predictor)"},"content":{"rendered":"\n<p><strong>Can AI help us make difficult medical decisions?<\/strong><\/p>\n\n\n\n<p>Patients, caregivers, and clinicians often struggle to translate personal priorities into concrete treatment decisions\u2014especially when choices involve multiple trade-offs under uncertainty. People may know what matters to them, but&nbsp;<strong>integrating these values with complex medical information can exceed normal cognitive capacity<\/strong><\/p>\n\n\n\n<p>We are developing&nbsp;<strong>P4 (Personalised Patient Preference Predictor)<\/strong>, an LLM-based \u201cpatient co-pilot\u201d designed to support this integration. Rather than treating preferences as fixed, P4 learns what matters to an individual through structured, scenario-based interactions. It captures how priorities\u2014such as longevity, comfort, independence, and burden on others\u2014shift across contexts, and applies this to new medical scenarios to suggest aligned treatment choices.<\/p>\n\n\n\n<p>Importantly, P4 is not just a prediction system. It makes trade-offs explicit, shows which values drive each recommendation, and surfaces tensions or inconsistencies. This reflects a shift from simply providing information to&nbsp;<strong>supporting deliberation<\/strong>&nbsp;as it helps users apply what they value to real decisions over time.<\/p>\n\n\n\n<p>In an early prototype, we observed 86.7% agreement between participants and the system. We are now extending this as a proof-of-concept with the aim of helping solo agers, easing caregiver burden, and supporting stakeholders to make more values-concordant decisions in high-stakes contexts.<\/p>\n\n\n\n<p>References:<br>Earp, B. D., van Veenendaal, T., Porsdam Mann, S., &amp; Savulescu, J. (2025). Digital psychological twins in medicine: addressing risks to human relationships. In&nbsp;<em>Artificial intelligence and the future of human relations: eastern and western perspectives<\/em>&nbsp;(pp. 239-257). Singapore: Springer Nature Singapore.<\/p>\n\n\n\n<p>Sim, K. Y. H., Foong, P. S., Zhao, C., Quek, M. Y. N., Mehta, S. S., &amp; Choo, K. T. W. (2026, April). Words to Describe What I\u2019m Feeling: Exploring the Potential of AI Agents for High Subjectivity Decisions in Advance Care Planning. In&nbsp;<em>Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems<\/em>&nbsp;(pp. 1-34).<\/p>\n","protected":false},"excerpt":{"rendered":"Can AI help us make difficult medical decisions? Patients, caregivers, and clinicians often struggle to translate personal priorities into concrete treatment decisions\u2014especially when choices involve multiple trade-offs under uncertainty. People may know what matters to them, but&nbsp;integrating these values with complex medical information can exceed normal cognitive capacity We are&#8230;","protected":false},"author":9459,"featured_media":6883,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[43,1,24],"tags":[],"class_list":["post-6882","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-example-projects","category-news","category-telehealth-core"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>P4 (Personalised Patient Preference Predictor) | Example Projects, News, Telehealth Core | NUHS Centre Grant - Singapore Core Facilities for Diabetes, Infectious Diseases and Neuroscience<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/medicine.nus.edu.sg\/nuhscg\/p4-personalised-patient-preference-predictor\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"P4 (Personalised Patient Preference Predictor) | Example Projects, News, Telehealth Core | NUHS Centre Grant - Singapore Core Facilities for Diabetes, Infectious Diseases and Neuroscience\" \/>\n<meta property=\"og:description\" content=\"Can AI help us make difficult medical decisions? Patients, caregivers, and clinicians often struggle to translate personal priorities into concrete treatment decisions\u2014especially when choices involve multiple trade-offs under uncertainty. 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