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Abstract Background Primary needle and syringe programs (NSPs) have been integral for the prevention of blood-borne virus (BBV) transmission among people who inject drugs. Despite this, many people who inject drugs face barriers accessing these services, particularly after-hours when most services are closed. To our knowledge, the St Kilda NSP, in Melbourne, Victoria, is the only primary NSP providing 24/7 dedicated stand-alone face-to-face services for people who inject drugs in Australia. We conducted an evaluation of the St Kilda NSP to assess its role and effectiveness in meeting client needs. Methods Mixed research methods were used to conduct the evaluation. We analysed four quantitative data sets including the Victorian Needle and Syringe Program Information System data; NSP ‘snapshot’ survey data; and St Kilda NSP records of after-hours contacts and naloxone training events. Qualitative interviews were conducted with 20 purposively selected NSP clients, which were focused on individual needs, expectations and experiences accessing the service. Interviews were audio recorded and transcribed, and data were analysed thematically. A convergent research design was used to merge the five data sets. Results St Kilda NSP had 39,898 service contacts in 2018; 72% of contacts occurred outside business hours. Similarly, of 1,185,000 sterile needles and syringes dispatched, 71% were distributed outside business hours. Participants described valuing the after-hours service because drug use patterns did not always align with standard NSP opening hours and after-hours access afforded anonymity when collecting injecting equipment. Narratives highlighted several additional benefits of the 24/7 service, including: access to safer sex equipment; material support; naloxone training; referrals to specialist services; face-to-face emotional and social support from a non-judging worker; and for women involved in sex work in particular, being able to seek refuge when feeling unsafe on the streets. Conclusions Our study provides evidence of the social and health benefits (beyond that of preventing BBV transmission) that can be gained through the provision of 24/7 primary NSP services. Findings support the need for the establishment of after-hours primary NSPs in other areas of Australia where active street-based drug markets operate outside business hours and concentrated numbers of people who inject drugs live and spend time.

Rationale and objectivesRadiology turnaround time is an important quality measure that can impact hospital workflow and patient outcomes. We aimed to develop a machine learning model to predict delayed turnaround time during non-business hours and identify factors that contribute to this delay.Materials and methodsThis retrospective study consisted of 15,117 CT cases from May 2018 to May 2019 during non-business hours at two hospital campuses after applying exclusion criteria. Of these 15,177 cases, 7,532 were inpatient cases and 7,585 were emergency cases. Order time, scan time, first communication by radiologist, free-text indications, and other clinical metadata were extracted. A combined XGBoost classifier and Random Forest natural language processing model was trained with 85% of the data and tested with 15% of the data. The model predicted two measures of delay: when the exam was ordered to first communication (total time) and when the scan was completed to first communication (interpretation time). The model was analyzed with the area under the curve (AUC) of receiver operating characteristic (ROC) and feature importance. Source code: https://bit.ly/2UrLiVJ RESULTS: The algorithm reached an AUC of 0.85, with a 95% confidence interval [0.83, 0.87], when predicting delays greater than 245 minutes for "total time" and 0.71, with a 95% confidence interval [0.68, 0.73], when predicting delays greater than 57 minutes for "interpretation time". At our institution, CT scan description (e.g. "CTA chest pulmonary embolism protocol"), time of day, and year in training were more predictive features compared to body part, inpatient status, and hospital campus for both interpretation and total time delay.ConclusionThis algorithm can be applied clinically when a physician is ordering the scan to reasonably predict delayed turnaround time. Such a model can be leveraged to identify factors associated with delays and emphasize areas for improvement to patient outcomes.

Purpose: To investigate the differences in endovascular thrombectomy (EVT) outcomes of patients treated for acute ischaemic stroke (AIS) during business versus off-business hours. Methods: A single-centre retrospective cohort study of patients with AIS treated with EVT from February 1, 2015, to May 31, 2021, was performed at a comprehensive stroke centre (CSC). Patients were divided into business (Monday to Friday, 8 AM-5 PM) versus off-business hours groups. The primary outcome was functional neurological disability, scored using the modified Rankin Scale (mRS) at 90 days. Secondary outcomes included the rate of successful reperfusion and procedural workflow time delays. Differences in proportions were assessed using Fisher's exact and Chi-Square tests as appropriate. For continuous variables, differences in medians between groups were assessed using Mann-Whitney U tests. Results: A total of 676 patients were included, with 399 patients (59%) comprising the off-business-hour group. No significant differences were seen in age, sex, ASPECTS score, or NIHSS at arrival. Off-business hours strokes had a longer delay between CSC arrival to groin puncture (minutes: 81 vs 44, P < .0001) and between imaging to groin puncture (minutes: 67 vs 32, P < .0001) compared to the business hours strokes. There were no differences in the rate of successful reperfusion (mTICI ≥2b) between groups (82% vs 83%, P = .61). At 90 days, 65% of patients in both groups had an mRS ≤2 ( P = .91). Conclusion: Despite workflow delays in initiating EVT during off-business hours, there were no differences in the rate of successful reperfusion or functional outcomes., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.