Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Randomized Controlled Trial
. 2021 Jan 4;4(1):e2032320.
doi: 10.1001/jamanetworkopen.2020.32320.

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

Stig Nikolaj Blomberg  1   2 , Helle Collatz Christensen  1   2   3 , Freddy Lippert  1   2 , Annette Kjær Ersbøll  4 , Christian Torp-Petersen  5 , Michael R Sayre  6 , Peter J Kudenchuk  7 , Fredrik Folke  1   2   8
Affiliations
Randomized Controlled Trial

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

Stig Nikolaj Blomberg et al. JAMA Netw Open. .

Abstract

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.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest Disclosures: Mr Blomberg reported receiving unrestricted research grants from TrygFoundation during the conduct of the study. Dr Lippert reported receiving unrestricted research grants from TrygFonden and Laerdal Foundation during the conduct of the study. Dr Torp-Petersen reported receiving grants from Bayer and Novo Nordisk outside the submitted work. Dr Sayre reported receiving a fellowship from Stryker Physio-Control outside the submitted work. Dr Folke reported receiving grants from Novo Nordisk Foundation and the Laerdal Foundation during the conduct of the study. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Study Flowchart
DCAR indicates Danish Cardiac Arrest Registry; EMS, emergency medical services; ML, machine learning; OHCA, out-of-hospital cardiac arrest. aCalls that had been on hold while dispatcher conferred with EMS physicians did not meet inclusion criteria if the alert appeared after the conference. bAll calls were linked to the DCAR by the personal identification number. If a call had a corresponding cardiac arrest in the register, it was included in further analysis. cCalls forwarded from out-of-hours services or calls from other authorities requesting help were excluded.
Figure 2.
Figure 2.. Timeline for Call to 112
CPR indicates cardiopulmonary resuscitation.
Figure 3.
Figure 3.. Failure Curves for Recognition of Out-of-Hospital Cardiac Arrest
The shaded areas indicate 95% CIs; crosses, censored calls, ie, those that ended without the dispatcher recognizing the condition.
See this image and copyright information in PMC

References

    1. Hasselqvist-Ax I, Riva G, Herlitz J, et al. . Early cardiopulmonary resuscitation in out-of-hospital cardiac arrest. N Engl J Med. 2015;372(24):2307-2315. doi:10.1056/NEJMoa1405796 - DOI - PubMed
    1. Takei Y, Nishi T, Matsubara H, Hashimoto M, Inaba H. Factors associated with quality of bystander CPR: the presence of multiple rescuers and bystander-initiated CPR without instruction. Resuscitation. 2014;85(4):492-498. doi:10.1016/j.resuscitation.2013.12.019 - DOI - PubMed
    1. Viereck S, Møller TP, Ersbøll AK, et al. . Recognising out-of-hospital cardiac arrest during emergency calls increases bystander cardiopulmonary resuscitation and survival. Resuscitation. 2017;115:141-147. doi:10.1016/j.resuscitation.2017.04.006 - DOI - PubMed
    1. Rea TD, Fahrenbruch C, Culley L, et al. . CPR with chest compression alone or with rescue breathing. N Engl J Med. 2010;363(5):423-433. doi:10.1056/NEJMoa0908993 - DOI - PubMed
    1. Svensson L, Bohm K, Castrèn M, et al. . Compression-only CPR or standard CPR in out-of-hospital cardiac arrest. N Engl J Med. 2010;363(5):434-442. doi:10.1056/NEJMoa0908991 - DOI - PubMed

Publication types

Associated data