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Conference proceeding
To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events
Proceedings of the ACM on human-computer interaction, v 9(7), pp 1-33
16 Oct 2025
PMID: 41122221
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
AI-enabled decision-support systems aim to help medical providers rapidly make decisions with limited information during medical emergencies. A critical challenge in developing these systems is supporting providers in interpreting the system output to make optimal treatment decisions. In this study, we designed and evaluated an AI-enabled decision-support system to aid providers in treating patients with traumatic injuries. We first conducted user research with physicians to identify and design information types and AI outputs for a decision-support display. We then conducted an online experiment with 35 medical providers from six health systems to evaluate two human-AI interaction strategies: (1) AI information synthesis and (2) AI information and recommendations. We found that providers were more likely to make correct decisions when AI information and recommendations were provided compared to receiving no AI support. We also identified two socio-technical barriers to providing AI recommendations during time-critical medical events: (1) an accuracy-time trade-off in providing recommendations and (2) polarizing perceptions of recommendations between providers. We discuss three implications for developing AI-enabled decision support used in time-critical events, contributing to the limited research on human-AI interaction in this context.
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Details
- Title
- To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events
- Creators
- Angela Mastrianni (Corresponding Author) - Drexel University, Information ScienceMary Suhyun Kim - Children's NationalTravis M Sullivan - Children's NationalGenevieve Jayne Sippel - Children's NationalRandall S Burd - Children's NationalKrzysztof Z. Gajos - Harvard University, Allston, MA, USAAleksandra Sarcevic - Drexel University, Information Science
- Publication Details
- Proceedings of the ACM on human-computer interaction, v 9(7), pp 1-33
- Publisher
- Association for Computing Machinery
- Number of pages
- 33
- Grant note
- 204177 / National Science Foundation Graduation Research Fellowship IS-2107391 / National Science Foundation (https://doi.org/10.13039/100000001) R01LM011834-05 / National Institutes of Health (https://doi.org/10.13039/100000002)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001615107800001
- Scopus ID
- 2-s2.0-105019333053
- Other Identifier
- 991022122860304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics
- Computer Science, Information Systems