Personal Care Utility: Health as Everyday Infrastructure

• URL: http://arxiv.org/abs/2606.14145v1
• Date: Fri, 12 Jun 2026 06:07:37 GMT
• Title: Personal Care Utility: Health as Everyday Infrastructure
• Abstract summary: Healthcare is essential, expert, and episodic by design - built around the roughly one hour per year a person spends with a clinician.<n>The 8,759 hours outside clinical settings, where eating, sleeping, movement, medication, and stress actually shape long-term health, have no comparable infrastructure.<n>This paper introduces the Personal Care Utility (PCU), a layered, event-driven architecture proposed as the missing utility for everyday health.
• Score: 0.21079694661943607
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Physiological World Models for Human State Transitions [16.270018313593514]
  We propose the Physiological World Model (PWM), an event-conditioned framework for learning changes at the level of the whole person.<n>We describe four capability levels, from state representation to bounded intervention planning, together with four data acquisition and validation protocols.<n>This framework provides a practical path towards personalized health management, behavioural intervention design and clinician-supervised decision support.
  arXiv  Detail & Related papers  (2026-08-15T16:26:10Z)
• mind_call: A Dataset for Mental Health Function Calling with Large Language Models [2.1155908599769764]
  This paper presents a synthetic function-calling dataset for mental health assistance grounded in wearable health signals.<n>The dataset maps diverse natural language queries to standardized API calls derived from a widely adopted health data schema.<n>This resource supports research on intent grounding, temporal reasoning, and reliable function invocation in mental health agents.
  arXiv  Detail & Related papers  (2026-01-11T14:52:57Z)
• Personal Care Utility (PCU): Building the Health Infrastructure for Everyday Insight and Guidance [0.0]
  We propose the Personal Care Utility (PCU) as a cybernetic system for lifelong health guidance.<n>PCU functions as an ambient, adaptive companion - observing, interpreting, and guiding health in real time across daily life.<n>We describe the architecture, design principles, and implementation challenges of this emerging paradigm.
  arXiv  Detail & Related papers  (2025-10-26T09:43:33Z)
• A Comprehensive Review of Datasets for Clinical Mental Health AI Systems [55.67299586253951]
  We present the first comprehensive survey of clinical mental health datasets relevant to the training and development of AI-powered clinical assistants.<n>Our survey identifies critical gaps such as a lack of longitudinal data, limited cultural and linguistic representation, inconsistent collection and annotation standards, and a lack of modalities in synthetic data.
  arXiv  Detail & Related papers  (2025-08-13T13:42:35Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• Measuring Outcomes in Healthcare Economics using Artificial Intelligence: with Application to Resource Management [0.0]
  In most cases, such events lead to critical uncertainties in decision making, as well as in multiple medical and economic aspects at a hospital. This manuscript presents three data-driven methods that help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing.
  arXiv  Detail & Related papers  (2021-11-15T02:39:39Z)
• Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data [74.60507696087966]
  Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. One promising data source to help monitor human behavior is daily smartphone usage. We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
  arXiv  Detail & Related papers  (2021-06-24T17:46:03Z)
• Health Status Prediction with Local-Global Heterogeneous Behavior Graph [69.99431339130105]
  Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. We propose to model the behavior-related multi-source data streams with a local-global graph. We take experiments on StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.
  arXiv  Detail & Related papers  (2021-03-23T11:10:04Z)
• Data Resource Profile: Egress Behavior from Select NYC COVID-19 Exposed Health Facilities March-May 2020 [0.0]
  A multi-week, multi-site observational study was undertaken outside of 19 New York City medical facilities during the peak of the city's initial COVID-19 wave. The aim was to capture perishable data of the touch, destination choice, and PPE usage behavior of individuals egressing hospitals and urgent care centers.
  arXiv  Detail & Related papers  (2021-01-18T19:21:16Z)
• Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation [75.3147962600095]
  We propose an automated framework for body language based emotion recognition starting from regular RGB videos. In collaboration with psychologists, we extend the framework for psychiatric symptom prediction. Because a specific application domain of the proposed framework may only supply a limited amount of data, the framework is designed to work on a small training set.
  arXiv  Detail & Related papers  (2020-10-30T18:45:16Z)
• Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
  We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
  arXiv  Detail & Related papers  (2020-09-21T03:45:14Z)
• Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health [0.0]
  MHealth interventions are designed to impact a near time, proximal outcome such as stress or physical activity. The mHealth intervention policies, often called just-in-time adaptive interventions, are decision rules that map an individual's current state. We provide an approach for conducting inference about the performance of one or more such policies using historical data collected under a possibly different policy.
  arXiv  Detail & Related papers  (2019-12-30T21:22:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.