ObjectivesAcademic 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.MethodsWe 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.ResultsThe 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.ConclusionValue 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.