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This study introduces computer-readable policies for health data sharing, enhancing consumer control and enabling automated consent. This approach improves interoperability and policy adoption in digital health.

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Area of Science:

  • Health Informatics
  • Computer Science
  • Information Governance

Background:

  • Digital health transformation necessitates robust consent mechanisms for sensitive data.
  • Existing data sharing policies often lack machine readability, hindering automation.
  • Consumer preferences and privacy are paramount in health information exchange.

Purpose of the Study:

  • To propose computer-readable policy expressions for governing health data sharing and use.
  • To enable consent automation within digital health ecosystems.
  • To enhance interoperability and policy adaptability.

Main Methods:

  • Utilizing the HL7 FHIR standard for health information exchange.
  • Applying ISO ODP Enterprise Language (ODP-EL) concepts for obligations and responsibilities.
  • Developing a domain-specific language (DSL) for policy expression and code generation.

Main Results:

  • Demonstrated a framework for computer-readable policies in consent-based health data exchange.
  • Facilitated integration with digital health applications through DSL-generated code.
  • Accommodated generative AI for policy alignment and provenance tracking.

Conclusions:

  • The proposed approach enhances consumer control over sensitive health data.
  • It improves interoperability by integrating consent, privacy, and security requirements.
  • The framework supports adaptability to new business, regulatory, and jurisdictional policies.