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<p>The workforce shortages caused by aging populations demand a transition from reactive to preventive healthcare strategies. Generative Artificial Intelligence offers a promising solution through the use of agents that can generate personalised guidance. We implement a digital assistant powered by a multi-agent framework that generates and refines personalised health plans based on user interactions. A pilot study with a cohort of 20 residents and 7 clinicians revealed positive user acceptance. Both groups rated four success metrics significantly above neutral satisfaction levels (<i>p</i> values: &lt;0.05). The majority of residents valued the personalisation (<i>p</i> value: 0.003), appreciated the level of granularity (<i>p</i> value: 0.0003), and did not express major concerns about the recommended plans (<i>p</i> value: 0.941). More than 50% of the collected feedback reflected a positive sentiment on the personalised diet (<i>p</i> value: 0.110), personalised exercise (<i>p</i> value: 0.003), and general features (<i>p</i> value: 6e–06). This pilot study highlights the potential of AI-driven digital assistants in supporting preventive healthcare programmes.</p>

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Personalised health plan development using agentic AI in Singapore’s national preventive care programme: a pilot study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月9日

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  • Han Leong Goh,
  • Vicente Sancenon,
  • Benjamin M. X. Chu,
  • Gerald C. H. Koh,
  • Leroy Koh,
  • Delia Teo,
  • Maybelline S. L. Ooi,
  • Corryne N. Thng,
  • Chia-Zhi Tan,
  • David W. L. Chua,
  • Andy W. A. Ta

摘要

The workforce shortages caused by aging populations demand a transition from reactive to preventive healthcare strategies. Generative Artificial Intelligence offers a promising solution through the use of agents that can generate personalised guidance. We implement a digital assistant powered by a multi-agent framework that generates and refines personalised health plans based on user interactions. A pilot study with a cohort of 20 residents and 7 clinicians revealed positive user acceptance. Both groups rated four success metrics significantly above neutral satisfaction levels (p values: <0.05). The majority of residents valued the personalisation (p value: 0.003), appreciated the level of granularity (p value: 0.0003), and did not express major concerns about the recommended plans (p value: 0.941). More than 50% of the collected feedback reflected a positive sentiment on the personalised diet (p value: 0.110), personalised exercise (p value: 0.003), and general features (p value: 6e–06). This pilot study highlights the potential of AI-driven digital assistants in supporting preventive healthcare programmes.