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 the 1813-Medical Helpline, and on weekends. Trial registration This study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122).

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 likely to be sent to their GP the next day. During the weekend, patients were referred more to the ED for the likely reason that their GP is closed.

Context: Artificial intelligence (AI) could be a key driver in different healthcare dossiers, ranging from preventive to diagnostic and treatment purposes. The establishment of the Artificial Intelligence High-Level Expert Group in the European Commission, as well as their White Paper, show first attempts of creating policies in the domain of artificial intelligence in the EU. Despite these policy approaches, there is a need for a coherent regulatory framework that enables the efficient use of AI in the field of health. The aim of this policy brief is to evaluate current legislative gaps in terms of the introduction of AI in healthcare, focusing on the domains of Data Protection, Liability & Transparency, as well as Robustness & Accuracy. Policy Options: This policy brief identified a high degree of eHealth infrastructure fragmentation on member state level and limited action towards a structured and coherent framework for AI in healthcare, under the domains of Data Protection, Liability & Transparency, and Robustness & Accuracy. Recommendations: A unified approach at EU-level, based on proposed recommendations and merged into the form of a Directive, is advised. The development of the Health-AI-Directive will bring progress and improvement to legal certainty in the European AI-landscape. The introduction of the Health-AI-Directive is recommended to ensure trust and excellence in the use of AI in healthcare.

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