Research
Print page Print page
Switch language
The Capital Region of Denmark - a part of Copenhagen University Hospital
Published

Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial

Research output: Contribution to journalJournal articleResearchpeer-review

  1. Effect of Vasoactive Intestinal Polypeptide on Development of Migraine Headaches: A Randomized Clinical Trial

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Trends in Incidence of Intracerebral Hemorrhage and Association With Antithrombotic Drug Use in Denmark, 2005-2018

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Association of Childhood Fat Mass and Weight With Adult-Onset Type 2 Diabetes in Denmark

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Mortality Among Young Adults Born Preterm and Early Term in 4 Nordic Nations

    Research output: Contribution to journalJournal articleResearchpeer-review

  5. Assessment of Suicidal Behaviors Among Individuals With Autism Spectrum Disorder in Denmark

    Research output: Contribution to journalJournal articleResearchpeer-review

  1. Workforce Attachment after Ischemic Stroke – The Importance of Time to Thrombolytic Therapy

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Live video from bystanders' smartphones to improve cardiopulmonary resuscitation

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Risk of out-of-hospital cardiac arrest in patients with bipolar disorder or schizophrenia

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Incidence of COVID-19 Hospitalisation in Patients with Systemic Lupus Erythematosus: A Nationwide Cohort Study from Denmark

    Research output: Contribution to journalJournal articleResearchpeer-review

View graph of relations

Importance: Emergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation.

Objective: To examine how a machine learning model trained to identify OHCA and alert dispatchers during emergency calls affected OHCA recognition and response.

Design, Setting, and Participants: This double-masked, 2-group, randomized clinical trial analyzed all calls to emergency number 112 (equivalent to 911) in Denmark. Calls were processed by a machine learning model using speech recognition software. The machine learning model assessed ongoing calls, and calls in which the model identified OHCA were randomized. The trial was performed at Copenhagen Emergency Medical Services, Denmark, between September 1, 2018, and December 31, 2019.

Intervention: Dispatchers in the intervention group were alerted when the machine learning model identified out-of-hospital cardiac arrest, and those in the control group followed normal protocols without alert.

Main Outcomes and Measures: The primary end point was the rate of dispatcher recognition of subsequently confirmed OHCA.

Results: A total of 169 049 emergency calls were examined, of which the machine learning model identified 5242 as suspected OHCA. Calls were randomized to control (2661 [50.8%]) or intervention (2581 [49.2%]) groups. Of these, 336 (12.6%) and 318 (12.3%), respectively, had confirmed OHCA. The mean (SD) age among of these 654 patients was 70 (16.1) years, and 419 of 627 patients (67.8%) with known gender were men. Dispatchers in the intervention group recognized 296 confirmed OHCA cases (93.1%) with machine learning assistance compared with 304 confirmed OHCA cases (90.5%) using standard protocols without machine learning assistance (P = .15). Machine learning alerts alone had a significantly higher sensitivity than dispatchers without alerts for confirmed OHCA (85.0% vs 77.5%; P < .001) but lower specificity (97.4% vs 99.6%; P < .001) and positive predictive value (17.8% vs 55.8%; P < .001).

Conclusions and Relevance: This randomized clinical trial did not find any significant improvement in dispatchers' ability to recognize cardiac arrest when supported by machine learning even though artificial intelligence did surpass human recognition.

Trial Registration: ClinicalTrials.gov Identifier: NCT04219306.

Original languageEnglish
Article numbere2032320
JournalJAMA network open
Volume4
Issue number1
ISSN2574-3805
DOIs
Publication statusPublished - 6 Jan 2021

Bibliographical note

Copyright:
This record is sourced from MEDLINE/PubMed, a database of the U.S. National Library of Medicine

ID: 61785791