Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point

Mirjam Lisa Scholz, Helle Collatz-Christensen, Stig Nikolaj Fasmer Blomberg, Simone Boebel, Jeske Verhoeven, Thomas Krafft

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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).

Original languageEnglish
Article number36
Pages (from-to)36
Number of pages17
JournalScandinavian Journal of Trauma Resuscitation & Emergency Medicine
Volume30
Issue number1
DOIs
Publication statusPublished - 12 May 2022

Keywords

  • Artificial Intelligence
  • Cardiopulmonary Resuscitation/methods
  • Denmark/epidemiology
  • Emergency Medical Services
  • Female
  • Humans
  • Out-of-Hospital Cardiac Arrest/diagnosis
  • Retrospective Studies
  • Speech Recognition Software
  • Stroke/diagnosis
  • DECISION
  • MANAGEMENT
  • SIGNS
  • Stroke detection
  • ALGORITHM
  • TIME
  • SYMPTOMS
  • CARE
  • WINDOW
  • Automated speech recognition
  • HEALTH
  • Artificial intelligence
  • BRAIN

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