J Am Med Inform Assoc - A simple heuristic for blindfolded record linkage.

Tópicos

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Resumo

JECTIVES: To address the challenge of balancing privacy with the need to create cross-site research registry records on individual patients, while matching the data for a given patient as he or she moves between participating sites. To evaluate the strategy of generating anonymous identifiers based on real identifiers in such a way that the chances of a shared patient being accurately identified were maximized, and the chances of incorrectly joining two records belonging to different people were minimized.METHODS: Our hypothesis was that most variation in names occurs after the first two letters, and that date of birth is highly reliable, so a single match variable consisting of a hashed string built from the first two letters of the patient's first and last names plus their date of birth would have the desired characteristics. We compared and contrasted the match algorithm characteristics (rate of false positive v. rate of false negative) for our chosen variable against both Social Security Numbers and full names.RESULTS: In a data set of 19 000 records, a derived match variable consisting of a 2-character prefix from both first and last names combined with date of birth has a 97% sensitivity; by contrast, an anonymized identifier based on the patient's full names and date of birth has a sensitivity of only 87% and SSN has sensitivity 86%.CONCLUSION: The approach we describe is most useful in situations where privacy policies preclude the full exchange of the identifiers required by more sophisticated and sensitive linkage algorithms. For data sets of sufficiently high quality this effective approach, while producing a lower rate of matching than more complex algorithms, has the merit of being easy to explain to institutional review boards, adheres to the minimum necessary rule of the HIPAA privacy rule, and is faster and less cumbersome to implement than a full probabilistic linkage.

Resumo Limpo

jectiv address challeng balanc privaci need creat crosssit research registri record individu patient match data given patient move particip site evalu strategi generat anonym identifi base real identifi way chanc share patient accur identifi maxim chanc incorrect join two record belong differ peopl minimizedmethod hypothesi variat name occur first two letter date birth high reliabl singl match variabl consist hash string built first two letter patient first last name plus date birth desir characterist compar contrast match algorithm characterist rate fals posit v rate fals negat chosen variabl social secur number full namesresult data set record deriv match variabl consist charact prefix first last name combin date birth sensit contrast anonym identifi base patient full name date birth sensit ssn sensit conclus approach describ use situat privaci polici preclud full exchang identifi requir sophist sensit linkag algorithm data set suffici high qualiti effect approach produc lower rate match complex algorithm merit easi explain institut review board adher minimum necessari rule hipaa privaci rule faster less cumbersom implement full probabilist linkag

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