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Secure Multi-Party Computation Based Distributed Feasibility Queries - A HiGHmed Use Case.

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Summary
This summary is machine-generated.

Researchers developed a novel, automated, decentralized feasibility query process for multi-institutional medical data. This method enables cohort size calculations across organizations without central data aggregation or trusted third parties, enhancing rare disease research.

Keywords:
BPMNFHIRFeasibility queriesdistributed processesmedical informaticsprivacysecure multi-party computation

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Area of Science:

  • Medical Informatics
  • Health Data Science
  • Computational Biology

Background:

  • Integrating routine medical data into research is valuable but faces technical and legal barriers.
  • The German Medical Informatics Initiative (MII) aims to consolidate clinical data, but cross-organizational data is crucial for many research questions, especially in rare diseases.
  • Feasibility queries for cohort size calculation across institutions are essential for multi-centric study designs.

Purpose of the Study:

  • To introduce a fully automated, decentralized feasibility query process.
  • To overcome limitations of existing solutions where centralized repositories or Trusted Third Parties are not feasible.
  • To enable cross-organizational cohort size calculations without central data aggregation.

Main Methods:

  • Utilized open standards like BPMN 2.0 and HL7 FHIR R4.
  • Employed cryptographic techniques of secure Multi-Party Computation.
  • Developed a decentralized process without central components or Trusted Third Parties.

Main Results:

  • A fully automated, decentralized feasibility query process was successfully introduced.
  • The solution operates without requiring centralized data repositories or Trusted Third Parties.
  • The open-source implementation is designed as a plugin for the HiGHmed Data Sharing Framework.

Conclusions:

  • The proposed decentralized process offers a novel approach for multi-institutional data analysis.
  • This method enhances feasibility queries, particularly for rare disease research requiring cross-organizational data.
  • The underlying concept and algorithms are adaptable for independent use beyond the HiGHmed consortium.