# Consumer Data is Key to Artificial Intelligence Value: Welcome to the Health Care Future

## TL;DR

The paper argues that consumer-managed health data is essential for advancing AI in healthcare, enabling better treatment and understanding of diseases.

## Contribution

The paper introduces the concept of consumers as primary custodians of longitudinal health records to enable AI-driven healthcare transformation.

## Key findings

- Current healthcare systems lack incentives to maintain complete longitudinal health records.
- Consumer-driven data aggregation is critical for AI to deliver transformative healthcare insights.
- Rare disease communities demonstrate the potential of consumer-led health data management.

## Abstract

Humanity stands at the threshold of a new era in biological understanding, disease treatment, and overall wellness. The convergence of evolving patient and caregiver (consumer) behaviors, increased data collection, advancements in health technology and standards, federal policies, and the rise of artificial intelligence (AI) is driving one of the most significant transformations in human history. To achieve transformative health care insights, AI must have access to comprehensive longitudinal health records (LHRs) that span clinical, genomic, nonclinical, wearable, and patient-generated data. Despite the extensive use of electronic medical records and widespread interoperability efforts, current health care organizations, electronic medical record vendors, and public agencies are not incentivized to develop and maintain complete LHRs. This paper explores the new paradigm of consumers as the common provenance and singular custodian of LHRs. With fully aligned intentions and ample time to dedicate to optimizing their health outcomes, patients and caregivers must assume the sole responsibility to manage or delegate aggregation of complete, accurate, and real-time LHRs. Significant gaps persist in empowering consumers to act as primary custodians of their health data and to aggregate their complete LHRs, a foundational requirement for the effective application of AI. Rare disease communities, leaders in participatory care, offer a compelling model for demonstrating how consumer-driven data aggregation can be achieved and underscore the need for improved policy frameworks and technological tools. The convergence of AI and LHRs promises to transform medicine by enhancing clinical decision-making, accelerating accurate diagnoses, and dramatically advancing our ability to understand and treat disease at an unprecedented pace.