Sequoia Project Encourages Automated Patient Consent to Boost Health Data Sharing

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The Sequoia Project's privacy and consent workgroup released guidance aimed at helping healthcare organizations move from manual patient consent procedures to automated, computable consent systems. The document addresses the fragmentation of current consent management practices and provides sample operational resources, model policies, and workflow templates. The project identified several 'chokepoints,' including a complex web of federal and state consent rules, inconsistent forms across organizations, and the tension between obtaining consent and avoiding information blocking. Computable consent is defined as a machine-readable, standards-based representation of data-sharing preferences that can be automatically enforced across all electronic systems. The guidance focused on a high-impact use case: sharing substance use disorder information under the updated 42 CFR Part 2 final rule. This scenario was chosen because it represents a legally significant exchange pattern required to align HIPAA and Part 2 requirements, allowing for the modeling of end-to-end operational, legal, technical, and governance steps. The Sequoia Project views this document as laying the foundation for operationalizing automated consent, though acknowledging that additional guidance is needed for other high-impact areas such as payment, public health, or research.