Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis

Zhang, Shao; Yu, Jianing; Xu, Xuhai; Yin, Changchang; Lu, Yuxuan; Yao, Bingsheng; Tory, Melanie; Padilla, Lace M.; Caterino, Jeffrey; Zhang, Ping; Wang, Dakuo

doi:10.1145/3613904.3642343

Cited by 99Artificial Intelligence in Healthcare and EducationMachine Learning in HealthcareElectronic Health Records Systems

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis

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Zhang, Shao, Yu, Jianing, Xu, Xuhai, Yin, Changchang, Lu, Yuxuan, Yao, Bingsheng, Tory, Melanie, Padilla, Lace M. +3 more·2024·Proceedings of the CHI Conference on Human Factors in Computing Systems

DOI 10.1145/3613904.3642343PMC PMC11149368

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Abstract

Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires an early diagnosis with high uncertainty from the clinician. Our aim is to explore the design requirements for AI systems that can support clinical experts in making better decisions for the early diagnosis of sepsis. The study begins with a formative study investigating why clinical experts abandon an existing AI-powered Sepsis predictive module in their electrical health record (EHR) system. We argue that a human-centered AI system needs to support human experts in the intermediate stages of a medical decision-making process (e.g., generating hypotheses or gathering data), instead of focusing only on the final decision. Therefore, we build SepsisLab based on a state-of-the-art AI algorithm and extend it to predict the future projection of sepsis development, visualize the prediction uncertainty, and propose actionable suggestions (i.e., which additional laboratory tests can be collected) to reduce such uncertainty. Through heuristic evaluation with six clinicians using our prototype system, we demonstrate that SepsisLab enables a promising human-AI collaboration paradigm for the future of AI-assisted sepsis diagnosis and other high-stakes medical decision making.

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Information-driven support may provide users with more agency and autonomy than recommendation-driven support.
“In addition to supporting decision making, information-driven support may provide users with more agency and autonomy than recommendation-driven support [82, 83].”
From To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events (2025) · cites this paper as [82]
Section 3.2.5 (page unavailable) says AI output that suggests a final diagnosis can challenge clinicians’ authority. Section 6.1 (page unavailable) argues that information and support at intermediate stages can preserve clinicians’ role as final decision-makers. But the proposed system also gives laboratory-test recommendations, so the paper does not support a general contrast between information-driven and recommendation-driven support. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Information-driven support could help providers evaluate potential diagnoses while avoiding issues commonly found with recommendations, such as over-reliance and reduced agency.
“Information-driven support could help providers evaluate potential diagnoses, while avoiding issues commonly found with recommendations, such as over-reliance and reduced agency [45, 82, 83].”
From To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events (2025) · cites this paper as [82]
Sections 5.2.2 and 6.1 say that information supporting hypothesis testing can help clinicians make decisions while preserving their role as final decision-makers. But Section 6.3 says over-dependence remains a potential risk of the proposed approach and that avoiding undue reliance is an open design question; the paper does not establish that information-driven support avoids it. Page numbers were unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Recent research in human-AI interaction has proposed a paradigm shift from recommendation-driven support to information-driven support.
“However, recent research in human-AI interaction has proposed a paradigm shift from recommendation-driven support to information-driven support, which provides users with information synthesis to facilitate hypothesis testing [27, 45, 82, 83].”
From To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events (2025) · cites this paper as [82]
Some intensive care and emergency medicine physicians preferred receiving information synthesis from AI-enabled systems to help them generate and test hypotheses instead of risk prediction scores that could challenge their authority.
“In one study, some intensive care and emergency medicine physicians preferred receiving information synthesis from AI-enabled systems to help them generate and test hypotheses instead of risk prediction scores that could challenge their authority [82].”
From To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events (2025) · cites this paper as [82]
Prompting critical thinking or "provocation" without challenging provider expertise is a challenge in designing AI support for clinical settings.
“Another challenge in designing AI support for clinical settings is prompting critical thinking or “provocation” without challenging provider expertise [82].”
From To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events (2025) · cites this paper as [82]

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Authors: Zhang, S., Yu, J., Xu, X., Yin, C., Lu, Y., Yao, B., Tory, M., Padilla, L. M., Caterino, J., Zhang, P., & Wang, D.
Affiliations: Northeastern University (US)
University of Hong Kong (HK)
Massachusetts Institute of Technology (US)
The Ohio State University (US)
Rensselaer Polytechnic Institute (US)
Corresponding author: Zhang, Ping
Wang, Dakuo
ORCID: Zhang, Shao
Yu, Jianing
Xu, Xuhai
Yin, Changchang
Lu, Yuxuan
Yao, Bingsheng
Tory, Melanie
Padilla, Lace M.
Caterino, Jeffrey
Zhang, Ping
Wang, Dakuo
Venue: Proceedings of the CHI Conference on Human Factors in Computing Systems
Publisher: ACM
Published: 2024-05-11
Volume: 2024
Pages: 1-18
DOI: 10.1145/3613904.3642343
PMC: PMC11149368
OpenAlex: W4396832329
Cited by: 99
References: 96
Citations by year: 2026: 43 · 2025: 47 · 2024: 8 · 2023: 1
FWCI: 26.35
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion, cc-by)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
arXiv (Cornell University) (cc-by)
Funders: National Science Foundation (IIS-2145625)
National Institutes of Health (R01GM141279)
NIH (National Institutes of Health) (1R01MD018424-01)
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords: human-AI collaboration, sepsis diagnosis, clinical decision-making, clinical decision support, early diagnosis, electronic health records, heuristic evaluation

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Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis

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