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Background: Urinary tract infection (UTI) is particularly common in young women and the elderly. The Emergency Medical Services (EMS) in Copenhagen, Denmark can be reached by calling either of two dedicated telephone lines: 1–1-2 in case of an emergency and 1813 during general practitioner’s (GP) out-of-office hours (OOH). This study investigated characteristics of patients with symptoms of UTI calling the Copenhagen EMS and the response they received. Methods: A retrospective observational cohort study was conducted in which 7.5 years of telephone data on UTI from the EMS in Copenhagen were analyzed. Descriptive statistics and multinomial logistic regression were used to analyze patient characteristics, the timing of the incident and response. Patients’ age and gender were assessed and the use of urinary catheters, the timing of the incident, and the impact on the response were evaluated. Results: A total of 278.961 calls were included (78% female, mean age 47), with an average of 120 patients with UTI symptoms calling each day. Most people contacted the 1813-medical helpline (98%) and of those, the majority were referred to the emergency department (ED)(37%). Patients were more likely to be referred to the ED during the weekend compared to a weekday and less likely during OOH compared to in-office hours (IH). Patients with a urinary catheter were more likely to receive specialized care referred to as ‘other’. For the smaller proportion of patients calling 1–1-2, most people got a B (urgent) response (1.5%). The most likely response to be given was an A (emergency) or F (non-emergency) response during OOH compared to IH and on weekends compared to weekdays. Patients with a urinary catheter were more likely to receive a D (unmonitored transport) response. Conclusions: Since 2015, there was a decrease in 1813 antibiotic prescription rates and a subsequent increase in referral to the ED of UTI patients. Patients were referred less to the ED during OOH as they were

Background and purpose: Stroke recognition at the Emergency Medical Services (EMS) impacts the stroke treatment and thus the related health outcome. At the EMS Copenhagen 66.2% of strokes are detected by the Emergency Medical Dispatcher (EMD) and in Denmark approximately 50% of stroke patients arrive at the hospital within the time-to-treatment. An automatic speech recognition software (ASR) can increase the recognition of Out-of-Hospital cardiac arrest (OHCA) at the EMS by 16%. This research aims to analyse the potential impact an ASR could have on stroke recognition at the EMS Copenhagen and the related treatment. Methods: Stroke patient data (n = 9049) from the years 2016–2018 were analysed retrospectively, regarding correlations between stroke detection at the EMS and stroke specific, as well as personal characteristics such as stroke type, sex, age, weekday, time of day, year, EMS number contacted, and treatment. The possible increase in stroke detection through an ASR and the effect on stroke treatment was calculated based on the impact of an existing ASR to detect OHCA from CORTI AI. Results: The Chi-Square test with the respective post-hoc test identified a negative correlation between stroke detection and females, the 1813-Medical Helpline, as well as weekends, and a positive correlation between stroke detection and treatment and thrombolysis. While the association analysis showed a moderate correlation between stroke detection and treatment the correlation to the other treatment options was weak or very weak. A potential increase in stroke detection to 61.19% with an ASR and hence an increase of thrombolysis by 5% in stroke patients calling within time-to-treatment was predicted. Conclusions: An ASR can potentially improve stroke recognition by EMDs and subsequent stroke treatment at the EMS Copenhagen. Based on the analysis results improvement of stroke recognition is particularly relevant for females, younger stroke patients, calls received through th