API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research

Shorabuddin Syed; Mahanazuddin Syed; Hafsa Bareen Syeda; Maryam Garza; William Bennett; Jonathan Bona; Salma Begum; Ahmad Baghal; Meredith Zozus; Fred Prior

추천

검색

자료유형: 학술저널

저자정보: Shorabuddin Syed (University of Arkansas for Medical Sciences) Mahanazuddin Syed (University of Arkansas for Medical Sciences) Hafsa Bareen Syeda (University of Arkansas for Medical Sciences) Maryam Garza (University of Arkansas for Medical Sciences) William Bennett (University of Arkansas for Medical Sciences) Jonathan Bona (University of Arkansas for Medical Sciences) Salma Begum (University of Arkansas for Medical Sciences) Ahmad Baghal (University of Arkansas for Medical Sciences) Meredith Zozus (University of Texas Health Science Center at San Antonio) Fred Prior (University of Arkansas for Medical Sciences)

저널정보: 대한의료정보학회 Healthcare Informatics Research Healthcare Informatics Research 제27권 제1호

발행연도: 2021.1

수록면: 39 - 47 (9page)

이용수

📌

연구주제

📖

연구배경

🔬

연구방법

🏆

연구결과

초록· 키워드

오류제보하기

Objectives: To facilitate clinical and translational research, imaging and non-imaging clinical data from multiple disparatesystems must be aggregated for analysis. Study participant records from various sources are linked together and to patient recordswhen possible to address research questions while ensuring patient privacy. This paper presents a novel tool that pseudonymizesparticipant identifiers (PIDs) using a researcher-driven automated process that takes advantage of application-programminginterface (API) and the Perl Open-Source Digital Imaging and Communications in Medicine Archive (POSDA) tofurther de-identify PIDs. The tool, on-demand cohort and API participant identifier pseudonymization (O-CAPP), employsa pseudonymization method based on the type of incoming research data. Methods: For images, pseudonymization of PIDsis done using API calls that receive PIDs present in Digital Imaging and Communications in Medicine (DICOM) headersand returns the pseudonymized identifiers. For non-imaging clinical research data, PIDs provided by study principal investigators(PIs) are pseudonymized using a nightly automated process. The pseudonymized PIDs (P-PIDs) along with other protectedhealth information is further de-identified using POSDA. Results: A sample of 250 PIDs pseudonymized by O-CAPPwere selected and successfully validated. Of those, 125 PIDs that were pseudonymized by the nightly automated process werevalidated by multiple clinical trial investigators (CTIs). For the other 125, CTIs validated radiologic image pseudonymizationby API request based on the provided PID and P-PID mappings. Conclusions: We developed a novel approach of an ondemandpseudonymization process that will aide researchers in obtaining a comprehensive and holistic view of study participantdata without compromising patient privacy.

#Data Management #De-identification #Multimedia #PACS #Semantic Web

참고문헌 (30)

참고문헌 신청

Evans RS / 2016 / Electronic Health Records: then, now, and in the future / Yearb Med Inform Suppl 1(Suppl 1):S48~S61

Nordo AH / 2019 / Use of EHRs data for clinical research : historical progress and current applications / Learn Health Syst 3(1):e10076

Penning ML / 2020 / Near real time EHR data utilization in a clinical study / Stud Health Technol Inform 270:337~341

Gliklich RE / 2014 / Registries for evaluating patient outcomes: a user’s guide / Agency for Healthcare Research and Quality

Nelson E / 2011 / Entity resolution for longitudinal studies in education using OYSTER / Proceedings of 2011 Information and Knowledge Engineering Conference(IKE) :286~290

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!