Emergency Medical Services, Capital Region of Denmark, Telegrafvej 5, 2750, Ballerup, Denmark.
Department of Health, Ethics and Society, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, Netherlands.
Scand J Trauma Resusc Emerg Med. 2022 May 12;30(1):36. doi: 10.1186/s13049-022-01020-6.
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).
在急救医疗服务（EMS）中识别中风会影响中风的治疗，从而影响相关的健康结果。在哥本哈根 EMS，66.2%的中风是由急救医疗调度员（EMD）识别的，而在丹麦，约有 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 中心的院外心脏骤停（OHCA）识别率提高 16%。本研究旨在分析 ASR 对哥本哈根 EMS 中心中风识别的潜在影响，以及相关的治疗效果。
回顾性分析了 2016-2018 年中风患者的数据（n=9049），分析了 EMS 中风检测与中风特定的、个人特征（如中风类型、性别、年龄、工作日、时间、年份、联系的 EMS 编号以及治疗方法）之间的相关性。根据 CORTI AI 对 OHCA 的检测效果，计算了通过 ASR 增加中风检测的可能性，以及对中风治疗的影响。
卡方检验（Chi-Square test）及其后续的 post-hoc 检验结果表明，中风检测与女性、1813 医疗热线以及周末之间呈负相关，与治疗和溶栓之间呈正相关。虽然关联分析显示中风检测与治疗之间存在中度相关性，但与其他治疗方法的相关性较弱或非常弱。预计通过 ASR 将中风检测率提高到 61.19%，从而使在治疗时间内呼叫的中风患者的溶栓治疗增加 5%。
ASR 可以提高哥本哈根 EMS 中心的 EMD 对中风的识别能力，从而改善中风的治疗效果。根据分析结果，对女性、年轻的中风患者、通过 1813 医疗热线呼叫的患者以及周末的患者，提高中风识别率尤为重要。
本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。
