[论文解读] Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis

Shao Zhang, Jianing Yu+9 more|arXiv (Cornell University)|Sep 17, 2023

Sepsis Diagnosis and Treatment被引用 7

一句话总结

简要结论：本文分析为何现有的 AI 败血症预测模块在临床实践中不被信任，并介绍 SepsisLab——一个以人为本的系统，支持中间决策阶段和未来风险预测，并提供可执行的实验室检测建议。

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.

研究动机与目标

• 理解为何临床医生在 EHR 系统中放弃当前基于 AI 的败血症模块。
• 设计一个以人为本的 AI 助手，支持败血症诊断中的中间决策阶段的医疗决策。
• 开发 SepsisLab 以预测当前和未来的败血症风险并进行不确定性可视化。
• 提供可执行的实验室检测建议以降低不确定性并改进最终决策。
• 评估 SepsisLab 是否提高临床医生对人机协作的感知。

提出的方法

• 使用 Epic Sepsis Module (ESM) 对六位临床医生进行探索性形成性研究，以识别设计差距。
• 设计并实现 SepsisLab，一款 AI 辅助系统，预测当前和未来的败血症风险。
• 结合不确定性可视化和反事实实验室检查建议，以降低决策不确定性。
• 实现对最有可能降低不确定性的实验室检验的排序，并通过反事实进行交互式试验。
• 采用以用户为中心的界面，符合临床工作流程，促进假设生成、数据收集和假设检验（中间决策阶段）。
• 以去标识化的 MIMIC-III 数据为原型基础，展示与现有 EHR 系统的集成。

Figure 1. Existing EPIC Sepsis Module and Our Proposed Sepsis Decision-Support Module in Medical Decision Making Workflow . Our work focuses on sepsis diagnosis, a high-uncertainty, high-stakes, time-sensitive medical decision-making process. Physicians usually take four steps: (1) generating hypoth

实验结果

研究问题

• RQ1 为什么现有的 AI 败血症模块被临床医生认为没用或令人生畏？
• RQ2 应如何设计一个 AI 系统来支持败血症诊断中的中间医学决策阶段（假设生成、数据收集、假设检验）？
• RQ3 是否有 AI 工具能够在不确定性可视化和可执行的数据收集建议的帮助下预测未来的败血症风险，从而改善最终决策？

主要发现

• 临床医生认为现有的 Epic Sepsis Module (ESM) 落后、不准确、难以解读且缺乏可执行的洞察。
• 当前的 AI 范式被体验为竞争而非协作，削弱信任和采用。
• SepsisLab 展现了有前景的人机协同：能够预测当前和近未来的败血症风险、可视化不确定性、并推荐可降低不确定性的实验室检验。
• 临床医生更偏好能够支持中间决策（假设生成和数据收集）的 AI，而不仅仅是最终预测。
• 用户评估表明临床医生将 SepsisLab 视为合作者而非令人生畏的预测工具。

Figure 2. Existing Human-AI Interaction and “Competition” Paradigm. The current sepsis module mainly focuses on supporting the final decision-making stage, yet physicians often find the AI predictions are too late and not helpful.