SESSION DETAILS
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
Turning Insight Into Impact: AI-driven Risk Prediction, Pressing Pause on CKD
This presentation describes the implementation of an AI-enabled risk prediction tool that utilises generalised metabolic fluxes (GMF) based to simulate a digital twin of a person’s metabolic state. This tool is designed to identify patients with type 2 diabetes at risk of developing chronic kidney disease 3a in the next 3 years. And the aim of the sharing if to share not just about the model but the challenges and successes of implementing such a model in primary care.
Unlike traditional risk stratification approaches that rely on current renal function markers, the model simulates biological pathways to estimate the probability of developed CKD within 3 year, even in patients with currently preserved kidney function. The tool generates relatively real-time risk scores that can be used during clinical consultations. This allows primary are clinicians to prioritise patients for earlier therapeutic optimisation, including earlier initiation of renoprotective therapies, tighter cardiometabolic control and most importantly, patient empowerment and intensification of lifestyle therapies. Beyond clinical prediction, the aim is to shift care from reactive disease management to proactive prevention. Early results from our pilot explore clinician adoption, changes in treatment behaviour and the potential for improved patient outcomes and healthcare cost savings.
This experience has allowed us to explore how AI-enabled clinical decision support can augment primary care practice, improve patient safety, and enable precision prevention at scale.
Asst Prof Valerie Teo Hui Ying
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.
Dr Sky Wei Chee Koh
Family Physician, Consultant, Family Medicine Development,
National University Polyclinics
Asst Prof Valerie Teo Hui Ying
Head and Senior Consultant Family Physician,
Kallang Polyclinic, NHG Polyclinics
Dr Wayne Han Lee
Associate Consultant, Family Physician,
Queenstown Polyclinic, National University Polyclinics