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Related Experiment Videos

Privacy technology to support data sharing for comparative effectiveness research: a systematic review.

Xiaoqian Jiang1, Anand D Sarwate, Lucila Ohno-Machado

  • 1Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA. x1jiang@ucsd.edu

Medical Care
|June 19, 2013
PubMed
Summary

Effective data sharing for comparative effectiveness research (CER) is vital. This review explores privacy-preserving techniques for sharing patient data, focusing on practical applications and future scalability in CER studies.

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Area of Science:

  • Health Services Research
  • Biomedical Informatics
  • Data Science

Background:

  • Comparative Effectiveness Research (CER) relies heavily on data sharing.
  • Patient data privacy concerns hinder effective data sharing.
  • New technologies are emerging for privacy-preserving data sharing and mining.

Purpose of the Study:

  • To review existing and emerging privacy-preserving techniques for data sharing in CER.
  • To identify methods with practical impact relevant to CER data sharing scenarios.

Main Methods:

  • Systematic literature review methodology.
  • Searched 7 databases, with 3 stages of title, abstract, and full-text filtering.
  • Selected 97 articles for meta-analysis based on expert agreement.

Main Results:

  • Findings organized by data sharing types: institution-to-institution, institution-hosted, and public release.
  • Recommendations provided for specific CER data sharing scenarios.
  • Focus on practical impact, excluding purely theoretical privacy studies.

Conclusions:

  • State-of-the-art privacy-preserving technologies can advance future CER studies.
  • Challenges remain in practical evaluation and application to diverse data types.
  • The study focused on data tables, excluding complex data formats like genomics or network data.