Objectives: Academic institutions have access to comprehensive sets of real-world data. However, their potential for secondary use-for example, in medical outcomes research or health care quality management-is often limited due to data privacy concerns. External partners could help achieve this potential, yet documented frameworks for such cooperation are lacking. Therefore, this work presents a pragmatic approach for enabling academic-industrial data partnerships in a health care environment.
Methods: We employ a value-swapping strategy to facilitate data sharing. Using tumor documentation and molecular pathology data, we define a data-altering process as well as rules for an organizational pipeline that includes the technical anonymization process.
Results: The resulting dataset was fully anonymized while still retaining the critical properties of the original data to allow for external development and the training of analytical algorithms.
Conclusion: Value swapping is a pragmatic, yet powerful method to balance data privacy and requirements for algorithm development; therefore, it is well suited to enable academic-industrial data partnerships.
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| http://dx.doi.org/10.1177/20552076231172120 | DOI Listing |
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