JEEVHITAA -- An HCAI Ecosystem to Support Collective Care
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 69 references that are in the map. First posted on arXiv 6 Dec 2025.
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9 of 69 references.
References · 9
- 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
- 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
- 2023Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholderscited by 13
- 2019NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditionscited by 12
- 2024User Characteristics in Explainable AI: The Rabbit Hole of Personalization?cited by 12
- 2021Not Now, Ask Later: Users Weaken Their Behavior Change Regimen Over Time, But Expect To Re-Strengthen It Imminentlycited by 4
- 2023Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosiscited by 4
- 2021Towards Automated Fatigue Assessment using Wearable Sensing and Mixed-Effects Modelscited by 1
- 2025Unpacking "Personal" Health Informatics for Proactive Collective Carecited by 1
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