Factors and determinants of primary care to tertiary care referrals in Singapore: A multi-centre analysis using artificial intelligence-powered large language models

Abstract

Background As Singapore adopts a population health approach under Healthier Singapore (Healthier SG), optimizing healthcare resources is crucial. We examined referral reasons (using large language models [LLM]), wait times, and analyse factors affecting referrals from primary to tertiary care. Methods In 2023, 1,063,646 patient visits from seven primary care clinics in Singapore were analysed. Patient demographics, clinic, physician characteristics, referral volumes and wait times were extracted. LLM Claude 3.5 Sonnet was utilized to identify and classify top referral reasons within the most frequently referred specialties based on referral notes. Chi-square tests identified differences in referral rates among categorical variables, while a generalised linear model (GLM) with an identity link (normal distribution) determined factors influencing referrals by physicians. Findings Around 1 in 5 visits resulted in a referral (n = 210,839, 19.8%), achieving 76.0% attendance rate. Referrals peaked among patients aged 60–70 years. Male (Odds ratio [OR] 0.88, 95% Confidence interval [CI] 0.87–0.89) and Malay (OR 0.71, 95% CI 0.70–0.72, compared with Chinese) patients were less likely to be referred. Significant variations were observed among clinics (p < 0.001). Ophthalmology (11.1%), orthopaedic surgery (10.3%), and emergency (10.0%) were the most referred specialties, with blurred vision (n = 7,461), abnormal diabetic retinopathy screening (n = 5,266) and pregnancy and antenatal care (n = 3,959) being the top referral reasons. 51.5% were routine referrals. Wait time averaged 52.7 days with 48.9% meeting targets, with long wait times for Gastroenterology & Hepatology, and Endocrinology. On average, each additional year of physician experience was associated with a reduction of 4.45 referrals per physician (95% CI: 1.40–7.58, p = 0.005). Interpretation Our study highlighted disparities in referrals rates, patterns, and wait times. Continuing education and support for primary care is paramount. Resource allocation should be tailored to meet the population needs, with further research needed to ensure timely and appropriate referrals.