Foreign workers’ migrant status may hinder their utilisation of health services. This study describes the health-seeking behaviour and beliefs of a group of male migrant workers in Singapore and the barriers limiting their access to primary healthcare.

Background: Discharge letters are a critical component in the continuity of care between specialists and primary care providers. However, these letters are time-consuming to write, underprioritized in comparison to direct clinical care, and are often tasked to junior doctors. Prior studies assessing the quality of discharge summaries written for inpatient hospital admissions show inadequacies in many domains. Large language models such as GPT have the ability to summarize large volumes of unstructured free text such as electronic medical records and have the potential to automate such tasks, providing time savings and consistency in quality. Objective: The aim of this study was to assess the performance of GPT-4 in generating discharge letters written from urology specialist outpatient clinics to primary care providers and to compare their quality against letters written by junior clinicians. Methods: Fictional electronic records were written by physicians simulating 5 common urology outpatient cases with long-term follow-up. Records comprised simulated consultation notes, referral letters and replies, and relevant discharge summaries from inpatient admissions. GPT-4 was tasked to write discharge letters for these cases with a specified target audience of primary care providers who would be continuing the patient’s care. Prompts were written for safety, content, and style. Concurrently, junior clinicians were provided with the same case records and instructional prompts. GPT-4 output was assessed for instances of hallucination. A blinded panel of primary care physicians then evaluated the letters using a standardized questionnaire tool. Results: GPT-4 outperformed human counterparts in information provision (mean 4.32, SD 0.95 vs 3.70, SD 1.27; P=.03) and had no instances of hallucination. There were no statistically significant differences in the mean clarity (4.16, SD 0.95 vs 3.68, SD 1.24; P=.12), collegiality (4.36, SD 1.00 vs 3.84, SD 1.22; P=.05), conciseness (3.60, SD 1.12 vs 3.64, SD 1.27; P=.71), follow-up recommendations (4.16, SD 1.03 vs 3.72, SD 1.13; P=.08), and overall satisfaction (3.96, SD 1.14 vs 3.62, SD 1.34; P=.36) between the letters generated by GPT-4 and humans, respectively. Conclusions: Discharge letters written by GPT-4 had equivalent quality to those written by junior clinicians, without any hallucinations. This study provides a proof of concept that large language models can be useful and safe tools in clinical documentation.

Purpose: To describe the inception and structure of the SingHealth Diabetes Registry (SDR) as well as the methodology used to set up the registry. The SDR was established to facilitate systematic and standardized data collection for diabetes mellitus within Singapore Health Services (SingHealth), which is an Academic Medical Center (AMC) and Singapore’s largest group of healthcare institutions. The diabetes casemix and outcome variables within the registry cohort are also provided. Materials and Methods: The SDR is built from SingHealth’s electronic medical records (EMR) and clinical databases. It covers all individuals aged 18 and above with diabetes mellitus, excluding those with pre-diabetes. Cases are annually ascertained using criteria that include diagnosis codes, prescription records and laboratory test records. Data collection of casemix and outcome variables for the period 2013 to 2019 is complete. Results: The SDR stands at 208,102 ascertained individuals, distributed across 8 healthcare sites within the AMC. The cohort is broadly reflective of the local gender and ethnic compositions but has a high proportion of older individuals with a mean age of 65.8 ± 13.7 years. Majority (> 99%) have type 2 diabetes mellitus, with multiple other comorbidities (hypertension 84.1%, hyperlipidemia 86.2%, established cardiovascular disease 34.1%). At present, majority of individuals are able to meet key process indicators and 52.7% have a mean HbA1c of < 7% (53 mmol/mol). Areas of potential improvement include increasing eye and foot screening rates, as well as glycemic control for the 19.5% of individuals with mean HbA1c > 8% (64 mmol/mol). Conclusion: The SDR is a large-scale, comprehensive, and representative diabetes registry that incorporates EMR data across the primary and hospital-based care continuum, in a major AMC in Singapore. The SDR has identified areas of improvement in diabetes processes and outcomes. It will support future quality assessment and improvements in diabetes care.

Background: Socioeconomic status (SES) is an important determinant of health, and SES data is an important confounder to control for in epidemiology and health services research. Individual level SES measures are cumbersome to collect and susceptible to biases, while area level SES measures may have insufficient granularity. The ‘Singapore Housing Index’ (SHI) is a validated, building level SES measure that bridges individual and area level measures. However, determination of the SHI has previously required periodic data purchase and manual parsing. In this study, we describe a means of SHI determination for public housing buildings with open government data, and validate this against the previous SHI determination method. Methods: Government open data sources (e.g. data.gov.sg, Singapore Land Authority OneMAP API, Urban Redevelopment Authority API) were queried using custom Python scripts. Data on residential public housing block address and composition from the HDB Property Information dataset (data.gov.sg) was matched to postal code and geographical coordinates via OneMAP API calls. The SHI was calculated from open data, and compared to the original SHI dataset that was curated from non-open data sources in 2018. Results: Ten thousand seventy-seven unique residential buildings were identified from open data. OneMAP API calls generated valid geographical coordinates for all (100%) buildings, and valid postal code for 10,012 (99.36%) buildings. There was an overlap of 10,011 buildings between the open dataset and the original SHI dataset. Intraclass correlation coefficient was 0.999 for the two sources of SHI, indicating almost perfect agreement. A Bland-Altman plot analysis identified a small number of outliers, and this revealed 5 properties that had an incorrect SHI assigned by the original dataset. Information on recently transacted property prices was also obtained for 8599 (85.3%) of buildings. Conclusion: SHI, a useful tool for health services research, can be accurately reconstructed using open datasets at no cost. This method is a convenient means for future researchers to obtain updated building-level markers of socioeconomic status for policy and research.

COVID-19 patients with acute hypoxemic respiratory failure (AHRF) benefit from high flow nasal cannula (HFNC) oxygen therapy. However, delays in initiating invasive ventilation after HFNC failure are associated with poorer outcomes. The respiratory oxygenation (ROX) index, combining SpO₂/FiO₂ and respiratory rate, can predict HFNC failure. This meta-analysis evaluated the optimal ROX index cut-offs in predicting HFNC failure among COVID-19 patients at different measurement timings and clinical settings. Three databases were searched for eligible papers. From each study, we reconstructed the confusion matrices at different cut-offs, fitted linear mixed models to estimate the ROX index distribution function, and derived the area under the summary receiver operator characteristic curve (sAUC) and optimal cut-offs to predict HFNC failure. 24 studies containing 4790 patients were included. Overall sAUC was 0.771 (95% CI: 0.666–0.847) (optimal cut-off: 5.23, sensitivity: 0.732, specificity: 0.690). The cut-off values to achieve 80%, 90% sensitivity, 80%, 90% specificity were 5.70, 6.69, 4.45, 3.37, respectively. We stratified the analysis by ROX measurement time and estimated optimal cut-offs and cut-offs to achieve 80% sensitivity and specificity. For 2–6 h and 6–12 h post-HFNC initiation, we propose the use of 80% specific cut-offs to rule in HFNC failure of < 5.33 and < 3.69, respectively. For 12–24 h post-HFNC initiation, we propose the use of the 80% sensitive cut-off of > 6.07 to rule out HFNC failure. Our analysis confirms the overall utility of the ROX index in risk stratification of COVID-19 patients with AHRF receiving HFNC and provides potentially useful cut-offs for different times from HFNC initiation.