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. 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. 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. 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. This study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122).

中文翻译：

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紧急医疗服务调度中的人工智能：以丹麦首都地区为例，评估自动语音识别软件对中风检测的潜在影响

紧急医疗服务 (EMS) 的中风识别会影响中风治疗，从而影响相关的健康结果。在哥本哈根 EMS，66.2% 的中风由紧急医疗调度员 (EMD) 检测到，在丹麦，大约 50% 的中风患者在治疗时间内到达医院。自动语音识别软件 (ASR) 可以将 EMS 对院外心脏骤停 (OHCA) 的识别率提高 16%。本研究旨在分析 ASR 对哥本哈根 EMS 中风识别和相关治疗的潜在影响。回顾性分析了 2016-2018 年的中风患者数据 (n = 9049)，关于 EMS 中风检测与中风特异性之间的相关性，以及中风类型、性别、年龄、工作日、一天中的时间等个人特征，年，联系EMS号码，处理。根据现有 ASR 对 CORTI AI 检测 OHCA 的影响，计算了通过 ASR 检测中风的可能增加以及对中风治疗的影响。卡方检验和相应的事后检验确定了中风检测与女性、1813-Medical Helpline 以及周末之间的负相关，以及中风检测与治疗和溶栓之间的正相关。虽然关联分析显示中风检测和治疗之间存在中等相关性，但与其他治疗方案的相关性较弱或非常弱。预计使用 ASR 后中风检测的潜在增加至 61.19%，因此预计在治疗时间内呼叫的中风患者的溶栓增加 5%。ASR 可以潜在地改善 EMD 对中风的识别以及在 EMS 哥本哈根进行的后续中风治疗。根据分析结果，中风识别的改善与女性、年轻中风患者、通过 1813-Medical Helpline 接听的电话以及周末特别相关。该研究已在丹麦数据保护局 (PVH-2014-002) 和丹麦患者安全局 (R-21013122) 注册。