Evidence map›Paper›PMID 42584805›Full record

ArticleCJEM2026

Planning the development of an AI-driven decision support architecture for the recognition of sudden cardiac arrest by 9-1-1 telecommunicators: report of a community engagement and brainstorming meeting.

Christian Vaillancourt, Shannon Leduc, Sarika Naidoo, Manya Charette, J Phillip Nicholson, Matthew Church, Wojtek Michalowshi, Herna Viktor, Pouya Khodaee, Venkatesh Thiruganasambandamoorthy and 8 more

Abstract read
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In one paragraph

Article in CJEM, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Christian VaillancourtDepartment of Emergency Medicine, University of Ottawa, Ottawa, ON, Canada. cvaillancourt@ohri.ca.ORCID http://orcid.org/0000-0002-8563-4656
Shannon LeducSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0002-0277-2560
Sarika NaidooAcute Care Research Program, Ottawa Hospital Research Institute, Civic Campus, Ottawa, ON, Canada.
Manya CharetteAcute Care Research Program, Ottawa Hospital Research Institute, Civic Campus, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0003-4203-0155
J Phillip NicholsonCardiac arrest survivor and study partner, President, J. Phillip Nicholson Policy Consultants Inc., Ottawa, ON, Canada.
Matthew ChurchCardiac arrest survivor and study partner, Freelance Writer, Toronto, ON, Canada.
Wojtek MichalowshiHealth Informatics Institute, Telfer School of Management, University of Ottawa, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0002-9198-6439
Herna ViktorSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0003-1914-5077
Pouya KhodaeeSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0002-7777-7323
Venkatesh ThiruganasambandamoorthyDepartment of Emergency Medicine, University of Ottawa, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0003-4373-3688
Sean MooreLake of the Woods District Hospital, Ornge Transport, Northern Ontario School of Medicine University, Kenora, ON, Canada.ORCID http://orcid.org/0000-0001-7059-6936
Sheldon CheskesDepartment of Emergency Medicine, Family and Community Medicine, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0001-7835-3074
Greg FurlongOttawa Paramedic Service, Ottawa, ON, Canada.
Rick PowerInternational Business Machines (IBM) Canada, Ottawa, ON, Canada.
Randy GiffenInternational Business Machines (IBM) Canada, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0002-1801-7009
Donald PierceOntario Ministry of Health, Toronto, ON, Canada.
Michael FeldmanDepartment of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0002-5401-8551
Katie N DaintyResearch and Innovation, North York General Hospital, Toronto, ON, Canada.ORCID http://orcid.org/0000-0002-2906-8813

Funding

Strategy for Patient-Oriented Research 190955
6 · The paper itself

Abstract

objectivesSudden cardiac arrest is a leading cause of mortality in Canada (40,000 treatable deaths annually). Despite the benefits of 9-1-1 telecommunicator-assisted CPR, agonal (reflexive/ineffective) breathing often leads to misdiagnosis and delayed intervention. Artificial intelligence (AI), particularly large language models, may improve cardiac arrest recognition during 9-1-1 calls. This study aimed to identify community-informed requirements for integrating AI models into 9-1-1 ambulance communications centers to enhance rapid cardiac arrest recognition and timely response.

methodsWe hosted a CIHR-funded planning and dissemination meeting at the Ottawa Paramedic Service. The qualitatively driven study involved diverse community members including emergency physicians, telecommunicators, paramedics, cardiac arrest survivors, AI model developers, and policymakers. We used a structured Miro board exercise before, during, and after the meeting to facilitate participant input. Five pre-identified themes guided the exercise: (1) essential must-haves in a useful AI application, (2) considerations for integration with 9-1-1 system, (3) policy/ethical considerations, (4) burning questions, and (5) missing community members.

resultsParticipants emphasized the need for high cardiac arrest interpretation accuracy, low false activation rates, multilingual capabilities, and explainable AI model outputs. Integration concerns included compatibility with computer-assisted dispatch systems, platform independence, and redundancy during outages. Ethical considerations focused on privacy, data sovereignty, liability, and bias mitigation. Participants raised questions about AI model's ability to interpret voice cues, background audio, and caller distress. Gaps in community representation were identified, including accessibility experts and Ministry information technology specialists.

conclusionThis planning exercise identified critical technical, ethical, and operational requirements for AI model integration into 9-1-1 ambulance communications centers. Community member insights will inform future development and validation of an AI-driven decision support architecture aimed at improving cardiac arrest recognition, reducing time to CPR initiation, and enhancing equity in emergency response. The findings support a patient/telecommunicator-centered, responsible approach to AI model deployment in prehospital care.

Indexed as

9-1-1 telecommunicatorsArtificial intelligenceCardiac arrestCommunity engagement meeting

Identifiers

PMID42584805

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.