AI辅助护士排班中的不确定性传递：可解释AI原型的专家协同设计与评估
Communicating Uncertainty in AI-Supported Nurse Scheduling: Expert Co-Design and Evaluation of an Explainable AI Prototype
AI总结：
本研究针对AI辅助护士排班，通过专家协同设计开发可解释AI原型，经混合方法用户研究发现，AI预测的不确定性易被误解且降低决策信心，解释获积极评价但未显著影响决策信心，最终提出8项设计启示。
摘要：
护士排班是一个复杂的优化问题，需将护士分配至各班次，以满足 staffing 需求，同时遵守工作时间限制、资质要求及其他运营和监管约束。人工智能正越来越多地用于支持这一过程，通过预测未来 staffing 需求并提供改进班次排班的建议。护士排班中有效的人机协作要求用户理解、解读并合理依据AI的预测和建议采取行动，但目前对医疗保健专业人员在做出 staffing 决策时如何运用不确定性传递、AI建议解释等透明度机制的了解仍不充分。本研究采用以人为本的设计方法，迭代开发了护士排班的决策支持系统原型，并通过与专家用户开展的混合方法用户研究对其进行评估。我们运用主题分析、问卷数据和眼动追踪技术，研究医疗保健专业人员与该系统的交互方式，以及不确定性传递和AI建议解释如何影响理解、决策信心、信任度和可用性。研究发现，AI预测中传递的不确定性常被误解，并会降低决策信心；解释总体上获得积极评价，但未显著影响决策信心或建议接受度。我们从研究结果中总结出8项设计启示，为医疗保健领域更透明、有效的AI辅助决策支持工具的设计提供见解。
英文摘要：
Nurse scheduling is a complex optimization problem in which nurses are assigned to shifts to meet staffing demands while complying with working-time limits, qualification requirements, and other operational and regulatory constraints. Artificial intelligence is increasingly being used to support this process by forecasting future staffing needs and providing advice on how to improve shift scheduling. Effective human-AI collaboration in nurse scheduling requires users to understand, interpret, and appropriately act on AI forecasts and advice. However, it remains insufficiently understood how healthcare professionals use transparency mechanisms like uncertainty communication and AI advice explanations when making staffing decisions. Applying a human-centered design approach, we iteratively developed a prototype decision support system for nurse scheduling and evaluated it through a mixed-methods user study with expert users. Using thematic analysis, questionnaire data, and eye tracking, we examine how healthcare professionals interact with the system and how uncertainty communication and explanations of AI advice influence understanding, decision confidence, trust, and usability. We found that the uncertainty communicated in AI forecasts was frequently misinterpreted and reduced decision confidence. Explanations were generally perceived positively, but did not significantly affect decision confidence or advice acceptance. We conclude with eight design implications derived from our findings, providing insights for the design of more transparent and effective AI-supported decision support tools in healthcare.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
