API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research
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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.
Abstract
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.
참고문헌 (30)
- Evans RS, "Electronic Health Records: then, now, and in the future", Yearb Med Inform, Suppl 1(Suppl 1), pp.S48~S61 (2016)
- Nordo AH, "Use of EHRs data for clinical research : historical progress and current applications", Learn Health Syst, 3(1), pp.e10076~ (2019)
- Penning ML, "Near real time EHR data utilization in a clinical study", Stud Health Technol Inform, 270, pp.337~341 (2020)
- Gliklich RE, "Registries for evaluating patient outcomes: a user’s guide" (2014), Agency for Healthcare Research and Quality
- Nelson E, "Entity resolution for longitudinal studies in education using OYSTER", Proceedings of 2011 Information and Knowledge Engineering Conference(IKE), pp.286~290 (2011)
- Talburt JR, "Handbook of data quality", pp.235~270 (2013), Springer
- Erickson BJ, "Imaging in clinical trials", Cancer Inform, 4, pp.13~18 (2007)
- Grant JB, "Informatics, imaging, and healthcare quality management : imaging quality improvement opportunities and lessons learned form HCFA's Health Care Quality Improvement Program", Clin Perform Qual Health Care, 5(3), pp.133~139 (1997)
- Strickland NH, "PACS(picture archiving and communication systems) : filmless radiology", Arch Dis Child, 83(1), pp.82~86 (2000)
- "Digital Imaging and Communications in Medicine" (2020)[URL]
- Nass SJ, "Beyond the HIPAA Privacy Rule : enhancing privacy, improving health through research" (2009), National Academies Press
- Linden T, "The privacy policy landscape after the GDPR", Proc Priv Enhanc Technol, (1), pp.47~64 (2020)
- Nelson GS, "Practical implications of sharing data: a primer on data privacy, anonymization, and de-identification", Proceedings of SAS Global Forum, pp.1~23 (2015)
- Kushida CA, "Strategies for de-identification and anonymization of electronic health record data for use in multicenter research studies", Med Care, 50(Suppl), pp.S82~S101 (2012)
- Chevrier R, "Use and understanding of anonymization and de-identification in the biomedical literature : scoping review", J Med Internet Res, 21(5), pp.e13484~ (2019)
- Kayaalp M, "Modes of de-identification", AMIA Annu Symp Proc, 2017, pp.1044~1050 (2018)
- Riedl B, "A secure architecture for the pseudonymization of medical data", Proceedings of the 2nd International Conference on Availability, Reliability and Security (ARES), pp.318~324 (2007)
- Aryanto KY, "Free DICOM de-identification tools in clinical research : functioning and safety of patient privacy", Eur Radiol, 25(12), pp.3685~3695 (2015)
- Bennett W, "Reengineering workflow for curation of DICOM datasets", J Digit Imaging, 31(6), pp.783~791 (2018)
- "Perl Open Source Digital Imaging and Communications in Medicine Archive (POSDA)" (2019)[URL]
- Bruland P, "Connecting healthcare and clinical research : workflow optimizations through seamless integration of EHR, pseudonymization services and EDC systems", Int J Med Inform, 119, pp.103~108 (2018)
- Meystre SM, "Clinical data reuse or secondary use : current status and potential future progress", Yearb Med Inform, 26(1), pp.38~52 (2017)
- Architectural styles and the design of network-based software architectures, "Fielding RT", University of California, ~ (2000)
- Baghal A, "Factors associated with increased adoption of a research data warehouse", Stud Health Technol Inform, 257, pp.31~35 (2019)
- Smith B, "The OBO Foundry : coordinated evolution of ontologies to support biomedical data integration", Nat Biotechnol, 25(11), pp.1251~1255 (2007)
- Syed H, "Developing and refining matching rules for entity resolution", Proceedings of the International Conference on Information and Knowledge Engineering (IKE) (2012)
- Foran DJ, "Roadmap to a comprehensive clinical data warehouse for precision medicine applications in oncology", Cancer Inform, 16 (2017)
- "Chest imaging with clinical and genomic correlates representing a rural COVID-19 positive population (COVID-19-AR)" (2020)[URL]
- Sood HS, "Has the time come for a unique patient identifier for the U.S.?", NEJM Catal, 4(1), pp.1~4 (2018)
- "House votes to overturn ban on national patient identifier" (2019)[URL]