Personal Care Utility: Health as Everyday Infrastructure

2026-06-12

AI Summary

This work addresses the critical gap in effective health management infrastructure outside clinical settings—spanning 8,759 hours annually—hindering personalized health interventions. The authors propose a Personal Care Utility (PCU), a hierarchical, event-driven architecture that semantically interprets continuous physiological and behavioral signals into life events to dynamically estimate an individual’s health state. By decoupling clinical decision-making, behavioral policy, and natural language generation, PCU delivers context-aware guidance. Innovatively treating personalization as an intrinsic architectural property, the system integrates the Personicle data model, dynamic baselines, causal inference, and large language models. Demonstrated in a type 2 diabetes use case, it enables multimodal data fusion, individualized health assessment, causally interpretable insights, and safe, real-time interventions—ranging from gentle reminders to urgent alerts.

Abstract

Healthcare is essential, expert, and episodic by design - built around the roughly one hour per year a person spends with a clinician. The 8,759 hours outside clinical settings, where eating, sleeping, movement, medication, and stress actually shape long-term health, have no comparable infrastructure. The bottleneck for personalized health is not raw data or reasoning capability; it is the absence of that infrastructure layer. This paper introduces the Personal Care Utility (PCU): a layered, event-driven architecture proposed as the missing utility for everyday health, in the way that payments, networks, and power are utilities for their domains. PCU organizes continuous personal signals into semantically meaningful life events through a Personicle, estimates dynamic health state against personal baselines, reasons about cause and context, and routes guidance through an orchestrator that separates clinical decision logic, behavioral strategy selection, and natural-language expression. This separation lets large language models support reasoning and communication while keeping safety-critical clinical decisions grounded in validated evidence. We instantiate PCU for Type 2 Diabetes - turning CGM, meal, activity, medication, sleep, stress, and clinical data into glycemic events, individualized state estimates, causal explanations, and knowledge-grounded interventions. A day-in-the-life scenario shows the same infrastructure producing real-time nudges, weekly summaries, medication check-ins, silence, or deterministic safety alerts depending on context and risk. We close with how PCU generalizes to other chronic conditions and the governance questions any always-on personal health utility must address. The result is a blueprint that treats personalization not as a final messaging layer, but as an architectural property of everyday health guidance.

Problem

• personalized health
• health infrastructure
• chronic disease management
• everyday health
• continuous care

Innovation

• Personal Care Utility
• event-driven architecture
• health personalization
• causal reasoning
• clinical decision separation