CoPlan：一种基于角色可争议论证图的可信协同智能照护规划界面
CoPlan: A Trustworthy Co-Intelligence Interface for Care Planning through Role-Based Contestable Argument Graphs
- University of New Brunswick（新不伦瑞克大学）
- National Research Council Canada（加拿大国家研究委员会）
- Thompson Rivers University（汤普森河大学）
- ISB Corporation（ISB公司）
- University of Northern British Columbia（北英属哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出CoPlan界面，通过多智能体工作流结合协同智能与可争议性，实现人机协同照护规划，保留人类自主性与临床问责制，已在就地养老场景中验证其有效性。
AI中文摘要：
AI支持的照护规划可帮助临床医生、患者、照护人员及照护团队协调临床、功能、社会心理及环境需求等复杂决策。然而，许多AI系统将建议呈现为固定输出，限制了利益相关方在建议与临床判断、患者价值观或现实可行性冲突时检查、质疑和修订计划的能力。我们提出CoPlan——一种用于人机协同照护规划的协同智能可争议界面。CoPlan采用多智能体工作流，其中专门的AI智能体生成候选干预措施及支持或质疑的论证，人类照护规划人员可在最终计划生成前接受、拒绝、修改或添加论证。通过该设计，CoPlan结合了协同智能（人类与AI智能体贡献互补专业知识）与可争议性（建议始终可供检查、修订和论证）。我们在就地养老照护规划场景中演示了CoPlan，该系统支持适应性照护团队招募、基于角色的论证评审、最终照护计划生成及通过调度智能体实现的实际后续安排。本研究贡献了一种可争议照护规划界面及一种用于可信人机照护规划的设计框架，该框架保留了人类自主性与临床问责制。
英文摘要：
AI-supported care planning can help clinicians, patients, caregivers, and care teams coordinate complex decisions across clinical, functional, psychosocial, and environmental needs. However, many AI systems present recommendations as fixed outputs, limiting stakeholders' ability to inspect, challenge, and revise plans when they conflict with clinical judgment, patient values, or real-world feasibility. We present CoPlan - a Co-Intelligent and Contestable Interface for Human-AI Care Planning. CoPlan uses a multi-agent workflow in which specialized AI agents generate candidate interventions and supporting or challenging arguments, while human care planners can accept, reject, modify, or add arguments before final plan generation. Through this design, CoPlan combines co-intelligence, in which humans and AI agents contribute complementary expertise, with contestability, where recommendations remain open to inspection, revision, and justification. We demonstrate CoPlan in an aging-in-place care planning scenario. The system supports adaptive care team recruitment, role-based argument review, final care plan generation, and practical follow-up through scheduling agents. This work contributes a contestable care planning interface and a design framing for trustworthy human-AI care planning that preserves human agency and clinical accountability.
