Preprint
To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events
ArXiv.org
17 May 2025
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 MastrianniMary Suhyun KimTravis M SullivanGenevieve Jayne SippelRandall S BurdKrzysztof Z GajosAleksandra Sarcevic
- Publication Details
- ArXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Other Identifier
- 991022053939904721