Methods Inf Med - A database de-identification framework to enable direct queries on medical data for secondary use.

Tópicos

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Resumo

JECTIVE: To qualify the use of patient clinical records as non-human-subject for research purpose, electronic medical record data must be de-identified so there is minimum risk to protected health information exposure. This study demonstrated a robust framework for structured data de-identification that can be applied to any relational data source that needs to be de-identified.METHODS: Using a real world clinical data warehouse, a pilot implementation of limited subject areas were used to demonstrate and evaluate this new de-identification process. Query results and performances are compared between source and target system to validate data accuracy and usability.RESULTS: The combination of hashing, pseudonyms, and session dependent randomizer provides a rigorous de-identification framework to guard against 1) source identifier exposure; 2) internal data analyst manually linking to source identifiers; and 3) identifier cross-link among different researchers or multiple query sessions by the same researcher. In addition, a query rejection option is provided to refuse queries resulting in less than preset numbers of subjects and total records to prevent users from accidental subject identification due to low volume of data. This framework does not prevent subject re-identification based on prior knowledge and sequence of events. Also, it does not deal with medical free text de-identification, although text de-identification using natural language processing can be included due its modular design.CONCLUSION: We demonstrated a framework resulting in HIPAA Compliant databases that can be directly queried by researchers. This technique can be augmented to facilitate inter-institutional research data sharing through existing middleware such as caGrid.

Resumo Limpo

jectiv qualifi use patient clinic record nonhumansubject research purpos electron medic record data must deidentifi minimum risk protect health inform exposur studi demonstr robust framework structur data deidentif can appli relat data sourc need deidentifiedmethod use real world clinic data warehous pilot implement limit subject area use demonstr evalu new deidentif process queri result perform compar sourc target system valid data accuraci usabilityresult combin hash pseudonym session depend random provid rigor deidentif framework guard sourc identifi exposur intern data analyst manual link sourc identifi identifi crosslink among differ research multipl queri session research addit queri reject option provid refus queri result less preset number subject total record prevent user accident subject identif due low volum data framework prevent subject reidentif base prior knowledg sequenc event also deal medic free text deidentif although text deidentif use natur languag process can includ due modular designconclus demonstr framework result hipaa compliant databas can direct queri research techniqu can augment facilit interinstitut research data share exist middlewar cagrid

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