Genomic and Health Data as Fuel to Advance a Health Data Economy for Artificial Intelligence

Cloud and distributed computing, code repositories, and large language models are democratizing the less computationally intensive use cases of artificial intelligence (AI) in medicine. The convergence and democratization of these powerful tools promises to mobilize and utilize humanity’s knowledge and data, at least the knowledge bases and data that are readily available in the public commons. Healthcare represents a challenge due to fragmentation of the data fabric and governance mechanisms intrinsic to that sector of the economy. Privacy laws, stewardship practices, and the fragmented nature of the patient data journey (medical record silos) create cumbersome impediments to health data sharing, particularly longitudinal patient-level data. Consequently, obtaining the data necessary to train and operationalize AI in many healthcare and clinical genomics use cases limits the promise of these new technologies in addressing complexities in healthcare. We posit that trust, provenance, and fitness of health data and transaction costs represent challenges that blockchain ledgers and smart digital contracts might address. Here, we present frameworks from some of the great economic thinkers that might help address some of the stewardship and agency issues inherent to health data sharing. Our goal is to promote a more equitable and patient-centric healthcare data fabric to address current challenges of healthcare.