ArticleProceedings. IEEE International Conference on Healthcare Informatics2026
Promise and Caution: Mapping Opportunities for AI Decision Support in Emergency Medical Services.
Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Designing AI-enabled decision support for fast-paced medical teams remains underexplored. We use Emergency Medical Services (EMS) as a critical use case to examine where AI can, and should not, support time-critical decision-making. We conducted participatory design workshops and formative user evaluations with EMS providers to elicit, prototype, and critique candidate AI application areas for EMS work. Providers identified several promising uses of AI: (1) AI-enabled information retrieval to accelerate access to protocols and medication references; (2) speech-based documentation support to reduce charting burden and generate draft records during care; (3) AI-generated patient "snapshots" that summarize relevant history from prior encounters; and (4) AI-based medication recognition to identify home medications and surface key safety information. In contrast, participants were cautious about clinical reasoning support that could be interpreted as diagnostic assertions, ambient "always-on" monitoring to flag errors or workflow deviations, and voice-based exchanges with AI in front of patients. Across various concepts, providers emphasized key factors critical for AI adoption in EMS, including seamless workflow fit, robustness to noisy environments, transparency, appropriate trust calibration, and the preservation of professional autonomy. We conclude with design implications for responsibly advancing the use of AI technology for EMS decision support.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.