Health+: Empowering Individuals via Unifying Health Data

Sujaya Maiyya; Shantanu Sharma; Avinash Kumar

cs.MMFeb 22, 2026

Health+: Empowering Individuals via Unifying Health Data

Authors: Sujaya Maiyya, Shantanu Sharma, Avinash Kumar

Organizations: University of Waterloo, Canada · New Jersey Institute of Technology, USA · Independent Researcher, USA · Google, Mountain View, USA

Abstract

Managing personal health data is a challenge in today's fragmented and institution-centric healthcare ecosystem. Individuals often lack meaningful control over their medical records, which are scattered across incompatible systems and formats. This vision paper presents Health+, a user-centric, multimodal health data management system that empowers individuals (including those with limited technical expertise) to upload, query, and share their data across modalities (e.g., text, images, reports). Rather than aiming for institutional overhaul, Health+ emphasizes individual agency by providing intuitive interfaces and intelligent recommendations for data access and sharing. At the system level, it tackles the complexity of storing, integrating, and securing heterogeneous health records, ensuring both efficiency and privacy. By unifying multimodal data and prioritizing patients, Health+ lays the foundation for a more connected, interpretable, and user-controlled health information ecosystem.

Figures & tables

Figure 1. Disc herniation treatment process.

Panel of Figure 2 — Figure 2. Views of two different frameworks an individual needs to access for visiting two different healthcare providers

Figure 4. Dataflow in Health + for a medical record with $\langle$ Date: 11/24/23, Heart Rate: 90, Cholesterol: 220 $\rangle$ and for an X-ray. Inputs and outputs of several components of the data processing module are shown here. The red color represents the output of the data ingester, which is also shown in Example 4 .6 , after the update over the corresponding tables shown in Table 1 .

Table 1. Three different tables. The green color represents a new row in Vital table and the resulting updates in the other two tables. Dataflow among different components of the data processing module due to this new row is shown in Figure 4 .

Healthcare

Healthcare AI develops and evaluates models and systems for clinical care, medical research, public health, and healthcare operations.

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Generative AI is reshaping healthcare, yet most existing advances rely on hospital-grade devices, which limits their accessibility and potential for health management outside clinical settings. With the proliferation of portable devices and telemedicine, healthcare is shifting toward home-based Diagnosis-It-Yourself (DIY) care. Despite this promise, several distinctive challenges remain: (i) home-collected data are heterogeneous, exacerbated by the absence of standardized large-scale datasets; (ii) models require adaptation to variable task demands and evolving individual conditions; (iii) the broad spectrum of home care tasks lacks a unified benchmark for systematic evaluation. In this paper, we present DIYHealth Suite, a comprehensive framework designed to address these challenges through a tailored dataset, model, and benchmark. We first curate DIYHealth-900K, a large-scale multimodal dataset capturing diverse real-world home care scenarios. Building on this, we propose DIYHealthGPT, an adaptive foundation model for home-based health management, powered by the novel Hybrid Hyper Low-Rank Adaptation technique. Finally, we establish DIYHealthBench, the first benchmark to evaluate foundation models on home care tasks. Extensive experiments demonstrate that DIYHealthGPT delivers state-of-the-art performance over both general-purpose and medical-specific baselines on 11 home care tasks in both open-QA and closed-QA settings, laying the groundwork for the next generation of personalized health management at home.

Abstract

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