Rethinking human-AI collaboration in complex medical decision making: a case study in sepsis diagnosis

The image shows a workflow diagram where SepsisLab adds an additional step that provides clinicians with information about predicted outcomes and uncertainty.
The Clinician's Medical Decision-Making Workflow with Support from SepsisLab. SepsisLab focuses on providing support to the intermediate steps of the clinical experts' decision-making process, as opposed to existing AI modules that focus only on the final decision-making stage. SepsisLab can generate predictions for the patient's sepsis onset possibility (as the risk score) now and in the future, as shown in Step 1; It can further suggest additional lab tests by their impact on model uncertainty, and the interactive visualization can help clinicians select the most valuable lab tests to support their decision, as shown in Step 2; Once new data are collected, the prediction visualization will be updated (Step 3), helping clinicians test hypotheses. Then clinicians can generate new hypotheses or reach final decisions (Step 4).
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.
PDF | Preprint | BibTeX
Shao Zhang
Jianing Yu
Xuhai Xu
Changchang Yin
Yuxuan Lu
Bingsheng Yao
Jeffrey Caterino
Ping Zhang
Dakuo Wang
Thumbnail image for publication titled: Rethinking human-AI collaboration in complex medical decision making: a case study in sepsis diagnosis
Rethinking human-AI collaboration in complex medical decision making: a case study in sepsis diagnosis

Shao Zhang, Jianing Yu, Xuhai Xu, Changchang Yin, Yuxuan Lu, Bingsheng Yao, Melanie Tory, Lace M. Padilla, Jeffrey Caterino, Ping Zhang, and Dakuo Wang. Proc. CHI Conference on Human Factors in Computing Systems—CHI. 2024.

PDF | Preprint | BibTeX

Khoury Vis Lab — Northeastern University
* West Village H, Room 302, 440 Huntington Ave, Boston, MA 02115, USA
* 100 Fore Street, Portland, ME 04101, USA
* Carnegie Hall, 201, 5000 MacArthur Blvd, Oakland, CA 94613, USA