Evidence map›Paper›PMID 39825293›Full record

ArticleBMC medical informatics and decision making2025

Enhancing prehospital decision-making: exploring user needs and design considerations for clinical decision support systems.

Enze Bai, Zhan Zhang, Yincao Xu, Xiao Luo, Kathleen Adelgais

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  5. Promise and Caution: Mapping Opportunities for AI Decision Support in Emergency Medical Services.Proceedings. IEEE International Conference on Healthcare Informatics · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Enze BaiSchool of Computer Science and Information Systems, Pace University, New York City, NY, USA.
Zhan ZhangSchool of Computer Science and Information Systems, Pace University, New York City, NY, USA. zzhang@pace.edu.
Yincao XuSchool of Computer Science and Information Systems, Pace University, New York City, NY, USA.
Xiao LuoSchool of Business, Oklahoma State University, Stillwater, OK, USA.
Kathleen AdelgaisSchool of Medicine, University of Colorado, Aurora, CO, USA.

Funding

Design and Development of an Evidence-based Cognitive Aid for Out-of-Hospital Treatment of Pediatric PatientsR15LM014556 · NLM · PACE UNIVERSITY NEW YORK · PI ZHANG, ZHAN · 2024 to 2024
$419k
National Science Foundation 2237097NIH HHS 1R15LM014556-01NLM NIH HHS R15 LM014556
6 · The paper itself

Abstract

backgroundIn prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain patient conditions. Effective Clinical Decision Support Systems (CDSS) have great potential to alleviate these challenges. However, such systems have not yet been widely adopted in real-world practice and have been found to cause workflow disruptions and usability issues. Therefore, it is critical to investigate how to design CDSS that meet the needs of prehospital providers while accounting for the unique characteristics of prehospital workflows.

methodsWe conducted semi-structured interviews with 20 prehospital providers recruited from four Emergency Medical Services (EMS) agencies in an urban area in the northeastern U.S. The interviews focused on the decision-making challenges faced by prehospital providers, their technological needs for decision support, and key considerations for the design and implementation of a CDSS that can seamlessly integrate into prehospital care workflows. The data were analyzed using content analysis to identify common themes.

resultsOur qualitative study identified several challenges in prehospital decision-making, including limited access to diagnostic tools, insufficient experience with certain critical patient conditions, and a lack of cognitive support. Participants highlighted several desired features to make CDSS more effective in the dynamic, hands-busy, and cognitively demanding prehospital context, such as automatic prompts for possible patient conditions and treatment options, alerts for critical patient safety events, AI-powered medication identification, and easy retrieval of protocols using hands-free methods (e.g., voice commands). Key considerations for successful CDSS adoption included balancing the frequency and urgency of alerts to reduce alarm fatigue and workflow disruptions, facilitating real-time data collection and documentation to enable decision generation, and ensuring trust and accountability while preventing over-reliance when using CDSS.

conclusionThis study provides empirical insights into the challenges and user needs in prehospital decision-making and offers practical and system design implications for addressing these issues.

Indexed as

Clinical Decision-MakingDecision Support Systems, ClinicalEmergency Medical ServicesAdultFemaleHumansMaleQualitative ResearchWorkflowClinical decision support systemEmergency medical servicesPrehospital workflowUser-centered design

Identifiers

PMID39825293
PMCPMC11742207

What OpenQuestion holds

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LicenceCC BY
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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.