Sociodemographic and clinical factors, visit expectations and driving factors for emergency department attendance for uncomplicated upper respiratory tract infection.

BACKGROUND: Upper respiratory tract infections (URTIs) account for substantial non-urgent ED attendances. Hence, we explored the reasons for such attendances using a mixed-methods approach. METHODS: We interviewed adult patients with URTI who visited the second busiest adult ED in Singapore from June 2016 to November 2018 on their expectations and reasons for attendance. A structured questionnaire, with one open-ended question was used. Using the Andersen's Behavioural Model for Healthcare Utilisation, the topmost reasons for ED attendances were categorised into (1) contextual predisposing factors (referral by primary care physician, family, friends or coworkers), (2) contextual enabling factors (convenience, accessibility, employment requirements), (3) individual enablers (personal preference and trust in hospital-perceived care quality and efficiency) and (4) individual needs (perceived illness severity and non-improvement). Multivariable multinomial logistic regression was used to assess associations between sociodemographic and clinical factors, patient expectations for ED visits and the drivers for ED attendance. RESULTS: There were 717 patients in the cohort. The mean age of participants was 40.5 (SD 14.7) years, 61.2% were males, 66.5% without comorbidities and 40.7% were tertiary educated. Half had sought prior medical consultation (52.4%) and expected laboratory tests (55.7%) and radiological investigations (46.9%). Individual needs (32.8%) and enablers (25.1%) were the main drivers for ED attendance. Compared with ED attendances due to contextual enabling factors, attendances due to other drivers were more likely to be aged ≥45 years, had prior medical consultation and expected radiological investigations. Having a pre-existing medical condition (adjusted OR (aOR) 1.78, 95% CI 1.05 to 3.04) and an expectation for laboratory tests (aOR 1.64, 95% CI 1.01 to 2.64) were associated with individual needs while being non-tertiary educated (aOR 2.04, 95% CI 1.22 to 3.45) and having pre-existing comorbidities (aOR 1.79, 95% CI 1.04 to 3.10) were associated with individual enablers. CONCLUSIONS: Meeting individual needs of perceived illness severity or non-improvement was the topmost driver of ED visits for URTI, while contextual enabling factors such as convenience was the lowest. Patients' sociodemographic and clinical factors and visit expectations influence their motivations for ED attendances. Addressing these factors and expectations can alleviate the overutilisation of ED services.

Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study.

BACKGROUND: Effective contact tracing is labor intensive and time sensitive during the COVID-19 pandemic, but also essential in the absence of effective treatment and vaccines. Singapore launched the first Bluetooth-based contact tracing app-TraceTogether-in March 2020 to augment Singapore's contact tracing capabilities. OBJECTIVE: This study aims to compare the performance of the contact tracing app-TraceTogether-with that of a wearable tag-based real-time locating system (RTLS) and to validate them against the electronic medical records at the National Centre for Infectious Diseases (NCID), the national referral center for COVID-19 screening. METHODS: All patients and physicians in the NCID screening center were issued RTLS tags (CADI Scientific) for contact tracing. In total, 18 physicians were deployed to the NCID screening center from May 10 to May 20, 2020. The physicians activated the TraceTogether app (version 1.6; GovTech) on their smartphones during shifts and urged their patients to use the app. We compared patient contacts identified by TraceTogether and those identified by RTLS tags within the NCID vicinity during physicians' 10-day posting. We also validated both digital contact tracing tools by verifying the physician-patient contacts with the electronic medical records of 156 patients who attended the NCID screening center over a 24-hour time frame within the study period. RESULTS: RTLS tags had a high sensitivity of 95.3% for detecting patient contacts identified either by the system or TraceTogether while TraceTogether had an overall sensitivity of 6.5% and performed significantly better on Android phones than iPhones (Android: 9.7%, iPhone: 2.7%; P<.001). When validated against the electronic medical records, RTLS tags had a sensitivity of 96.9% and specificity of 83.1%, while TraceTogether only detected 2 patient contacts with physicians who did not attend to them. CONCLUSIONS: TraceTogether had a much lower sensitivity than RTLS tags for identifying patient contacts in a clinical setting. Although the tag-based RTLS performed well for contact tracing in a clinical setting, its implementation in the community would be more challenging than TraceTogether. Given the uncertainty of the adoption and capabilities of contact tracing apps, policy makers should be cautioned against overreliance on such apps for contact tracing. Nonetheless, leveraging technology to augment conventional manual contact tracing is a necessary move for returning some normalcy to life during the long haul of the COVID-19 pandemic.

Risk stratification and rapid geriatric screening in an emergency department - a quasi-randomised controlled trial.

BACKGROUND: To determine if risk stratification followed by rapid geriatric screening in an emergency department (ED) reduced functional decline, ED reattendance and hospitalisation. METHOD: This was a quasi-randomised controlled trial. Patients were randomised by the last digit of their national registration identity card (NRIC). Odd number controls received standard ED care; even number patients received geriatric screening, followed by intervention and/or onward referrals. Patients were followed up for 12 months. RESULTS: There were 500 and 280 patients in the control and intervention groups. The intervention group had higher Triage Risk Screening Tool (TRST) scores (34.3% vs 25.4% TRST ≥3, p = 0.01) and lower baseline Instrumental Activity of Daily Living (IADL) scores (22.84 vs 24.18, p < 0.01). 82.9% of the intervention group had unmet needs; 62.1% accepted our interventions. Common positive findings were fall risk (65.0%), vision (61.4%), and footwear (58.2%). 28.2% were referred to a geriatric clinic and 11.8% were admitted. 425 (85.0%) controls and 234 (83.6%) in the intervention group completed their follow-up. After adjusting for TRST and baseline IADL, the intervention group had significant preservation in function (Basic ADL -0.99 vs -0.24, p < 0.01; IADL -2.57 vs +0.45, p < 0.01) at 12 months. The reduction in ED reattendance (OR0.75, CI 0.55-1.03, p = 0.07) and hospitalization (OR0.77, CI0.57-1.04, p = 0.09) were not significant, however the real difference would have been wider as 21.2% of the control group received geriatric screening at the request of the ED doctor. A major limitation was that a large proportion of patients who were randomized to the intervention group either refused (18.8%) or left the ED before being approached (32.0%). These two groups were not followed up, and hence were excluded in our analysis. CONCLUSION: Risk stratification and focused geriatric screening in ED resulted in significant preservation of patients' function at 12 months. TRIAL REGISTRATION: National Healthcare Group (NHG) Domain Specific Review Board (DSRB) C/09/023. Registered 5th March 2009.