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SESSION DETAILS

AI and Technological Innovations for Enhanced Efficiency and Patient Safety

Session Type:

Symposium

Session Date:

15 May 2026 (Friday)

Session Time (GMT+8):

1400 - 1500

Session Venue:

White Space

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Session Chairperson

Dr Sky Wei Chee Koh

Abstract

This symposium explores cutting-edge applications of artificial intelligence (AI) and technology in primary care settings, focusing on innovative studies conducted in Singapore primary care. The studies showcase the practical applications of AI and technology in addressing key challenges in primary care: patient safety and clinical decision support. The symposium will discuss the implications of these innovations, including their potential to improve care quality, enhance efficiency, and support healthcare professionals in their daily practice. Additionally, it will explore the limitations of current research and future directions for implementation, scalability, and long-term impact assessment of these technologies in primary care settings.

Workshop Objectives

Workshop Learning Outcomes

Session Details

Topic
Speaker

Improving Detection of Missed Specialist Referrals in Primary Care using an AI-Supported Safety Net

Missed referrals are a result of an intention-action gap by clinicians intending to refer patients for further specialist care but do not do so. At baseline, these are only surfaced through patient self reported mechanisms or through incidental findings. This presents a problem as may result in delayed treatment or diagnosis. Missed Referrals-AI (MR-AI) is an automated tool that provides additional detection channels for potential missed referrals. The intervention was deployed in a 20 week pilot period which demonstrated significantly increased detection rates over baseline.

Dr Wayne Han Lee

TBC

Dr Valerie Teo

AI-powered Chatbot for Guidelines Access in Primary Care: A Cross-sectional Time Motion Study among Junior Physicians

This cross-sectional time-motion study evaluated an AI-powered, context-based guideline chatbot for chronic care in primary healthcare settings. Conducted across seven Singapore polyclinics from September to October 2025, the study involved 105 junior physicians and employed a within-subjects comparison between chatbot and manual guideline retrieval, alongside a Technology Acceptance Model survey. Results showed that the chatbot significantly reduced guideline retrieval time. The tool demonstrated high acceptability and feasibility, with significant increase in physician confidence in diagnosing, investigating, and managing chronic diseases. The findings support the integration of AI-powered chatbots into primary care settings, with recommendations for follow-up studies to assess long-term impacts, clinical outcomes, and scalability.

Dr Sky Wei Chee Koh

Speakers

More information is coming soon.

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Dr Sky Wei Chee Koh

Family Physician, Consultant, Family Medicine Development,
National University Polyclinics

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