J. Med. Internet Res. - Outsourcing medical data analyses: can technology overcome legal, privacy, and confidentiality issues?

Tópicos

{ system(1050) medic(1026) inform(1018) }
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{ network(2748) neural(1063) input(814) }
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{ treatment(1704) effect(941) patient(846) }
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{ howev(809) still(633) remain(590) }
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{ ehr(2073) health(1662) electron(1139) }
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Resumo

CKGROUND: Medical 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.OBJECTIVE: Our 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.METHODS: Using 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.RESULTS: The 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).CONCLUSIONS: The 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.

Resumo Limpo

ckground medic data gold mine deriv knowledg chang cours singl patient life even health entir popul data analyst need full access relev data full access may deni privaci confidenti medic data legal regul especi data analyst affili data ownerobject first object analyz privaci confidenti issu associ regul pertain medic data identifi technolog proper address issu second object develop procedur protect medic data way outsourc analyst capabl analys protect data result compar done origin data specif hypothesi differ outsourc decis tree built encrypt data one built origin datamethod use formal definit develop algorithm protect medic data outsourc analys algorithm appli public avail dataset n medic life scienc field analys perform origin protect dataset result analys compar bootstrap pair t test depend sampl use test whether mean differ size number leav accuraci origin encrypt decis tree signific differentresult decis tree built encrypt data virtual built origin data dataset tree ident accuraci size tree number leav differ pconclus propos algorithm encrypt file plain text medic data encrypt file data protect way extern data analys still possibl result show result analys origin protect data ident compar similar approach address privaci confidenti issu aris medic data adher strict legal rule unit state europ regard process medic data

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