ArticleJournal of medical Internet research2013
Outsourcing medical data analyses: can technology overcome legal, privacy, and confidentiality issues?
Article in Journal of medical Internet research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Using Minimum Local Distortion to Hide Decision Tree Rules.Entropy (Basel, Switzerland) · 2019Article
- European Hospitals' Transition Toward Fully Electronic-Based Systems: Do Information Technology Security and Privacy Practices Follow?JMIR medical informatics · 2019Article
- Enhancing Privacy Controls for Patients via a Selective Authentic Electronic Health Record Exchange Service: Qualitative Study of Perspectives by Medical Professionals and Patients.Journal of medical Internet research · 2018Article
- Privacy Policy and Technology in Biomedical Data Science.Annual review of biomedical data science · 2018Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedical data are gold mines for deriving the knowledge that could change the course of a single patient's life or even the health of the entire population. A data analyst needs to have full access to relevant data, but full access may be denied by privacy and confidentiality of medical data legal regulations, especially when the data analyst is not affiliated with the data owner.
objectiveOur first objective was to analyze the privacy and confidentiality issues and the associated regulations pertaining to medical data, and to identify technologies to properly address these issues. Our second objective was to develop a procedure to protect medical data in such a way that the outsourced analyst would be capable of doing analyses on protected data and the results would be comparable, if not the same, as if they had been done on the original data. Specifically, our hypothesis was there would not be a difference between the outsourced decision trees built on encrypted data and the ones built on original data.
methodsUsing formal definitions, we developed an algorithm to protect medical data for outsourced analyses. The algorithm was applied to publicly available datasets (N=30) from the medical and life sciences fields. The analyses were performed on the original and the protected datasets and the results of the analyses were compared. Bootstrapped paired t tests for 2 dependent samples were used to test whether the mean differences in size, number of leaves, and the accuracy of the original and the encrypted decision trees were significantly different.
resultsThe decision trees built on encrypted data were virtually the same as those built on original data. Out of 30 datasets, 100% of the trees had identical accuracy. The size of a tree and the number of leaves was different only once (1/30, 3%, P=.19).
conclusionsThe proposed algorithm encrypts a file with plain text medical data into an encrypted file with the data protected in such a way that external data analyses are still possible. The results show that the results of analyses on original and on protected data are identical or comparably similar. The approach addresses the privacy and confidentiality issues that arise with medical data and is adherent to strict legal rules in the United States and Europe regarding the processing of the medical data.
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