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A discussion paper released by the National Academy of Medicine, and covered by ASCO AI in Oncology on March 24, proposes a unified digital and data architecture for the U.S. health care system. The paper, authored by an expert working group convened under NAM's Commission on Investment Imperatives for a Healthy Nation, argues that the country's fragmented digital infrastructure is holding back meaningful progress in cost reduction, care coordination, and clinical innovation.
The authors call for moving well beyond electronic health records toward full data digitization and seamless interoperability. For managed care organizations, the implications are direct. The framework aligns with ongoing CMS initiatives, including the Health Technology Ecosystem and CMS Aligned Networks, and specifically incorporates FHIR-based standards that now underpin the Beneficiary Claims Data API, the Data at the Point of Care API, and the newly finalized Prior Authorization API.
At the center of the proposal is the concept of a Learning Health System, one in which science, incentives, informatics, and culture support continuous improvement and equity. The paper identifies four persistent barriers to progress: regulatory complexity, industry fragmentation, misaligned financial incentives, and resistance to innovation. To illustrate how better data architecture could change outcomes, the authors present use cases in cardiovascular disease, maternal health and maternal mortality, non-small cell lung cancer, and diabetes.
The paper also positions AI as essential to scalable improvement in care coordination, risk prediction, and clinical decision-making, while acknowledging the need for responsible oversight frameworks. For payers and health plans navigating value-based care models, the proposed infrastructure could serve as a foundation for more efficient operations and improved population health outcomes.
