构建支持多方协作的医疗健康系统，实现角色感知的数据共享与可验证流程。
JEEVHITAA -- An HCAI Ecosystem to Support Collective Care
- 基于角色权限和时间限制的细粒度访问控制，支持多角色协同护理。
- 集成大模型生成结构化摘要与带可信度评分的诊疗建议，支持证据溯源。
- 适用于需要多方协作的慢性病管理、家庭照护等场景，适合医护团队使用。
当前移动健康平台多以个体为中心，缺乏对多角色协同、可审计工作流的支持。在许多地区，健康决策依赖于多方协作而非个人独立行为。我们提出 JEEVHITAA，一个跨平台移动系统，支持在受控照护圈内实现角色感知的数据共享与可验证的信息流动。该系统整合平台与设备数据，通过传感器与分层注册构建分层用户画像，并在照护图谱上实施细粒度、时限性的访问控制。数据在应用内与云端均保持安全。集成的检索增强型大语言模型生成结构化、角色定制的摘要与行动方案，提供基于证据的验证，附带出处与置信度评分，并支持报告的深度洞察。我们阐述了系统架构、连接器抽象与安全原语，并通过合成的、基于本体的数据进行鲁棒性评估，以及为期9-14周的真实照护圈可行性研究。未来计划开展多中心评估，聚焦操作一致性、长期信任与素养影响，以及关系摩擦与日常基础设施融合问题。
原文摘要 · Abstract (English)
Current mobile health platforms are predominantly individual-centric and lack the support for coordinated, auditable multi-actor workflows. However, in many settings worldwide, health decisions are enacted through multi-actor coordination rather than individual users. We present JEEVHITAA, a cross-platform mobile system enabling role-aware sharing and verifiable information flows within permissioned care circles. JEEVHITAA ingests platform and device data, builds layered profiles from sensors and tiered onboarding, and enforces fine-grained, time-bounded access control across care graphs. Data stays secure both within the application and the cloud. Integrated retrieval-augmented Large Language Models produce structured, role-targeted summaries and action plans, offer evidence-grounded verification with provenance and confidence scores, and support advanced insights on reports. We describe the architecture, connector abstractions, and security primitives, and report robustness evaluations using synthetic, ontology-driven data and findings from a feasibility study with real-life care circles across 9-14 weeks. We outline plans for larger multi-site evaluations focusing on operational alignment, longitudinal trust & literacy impact, and relational friction & efforts to sink into the daily infrastructure.
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