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      KCI등재 SCI SCIE SCOPUS

      Perceived Risk of Re-Identification in OMOP-CDM Database: A Cross-Sectional Survey

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      https://www.riss.kr/link?id=A108185207

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      다국어 초록 (Multilingual Abstract)

      Background: The advancement of information technology has immensely increased the quality and volume of health data. This has led to an increase in observational study, as well as to the threat of privacy invasion. Recently, a distributed research net...

      Background: The advancement of information technology has immensely increased the quality and volume of health data. This has led to an increase in observational study, as well as to the threat of privacy invasion. Recently, a distributed research network based on the common data model (CDM) has emerged, enabling collaborative international medical research without sharing patient-level data. Although the CDM database for each institution is built inside a firewall, the risk of re-identification requires management. Hence, this study aims to elucidate the perceptions CDM users have towards CDM and risk management for re-identification.
      Methods: The survey, targeted to answer specific in-depth questions on CDM, was conducted from October to November 2020. We targeted well-experienced researchers who actively use CDM. Basic statistics (total number and percent) were computed for all covariates.
      Results: There were 33 valid respondents. Of these, 43.8% suggested additional anonymization was unnecessary beyond, “minimum cell count” policy, which obscures a cell with a value lower than certain number (usually 5) in shared results to minimize the liability of re-identification due to rare conditions. During extract-transform-load processes, 81.8% of respondents assumed structured data is under control from the risk of re-identification.
      However, respondents noted that date of birth and death were highly re-identifiable information. The majority of respondents (n = 22, 66.7%) conceded the possibility of identifier-contained unstructured data in the NOTE table.
      Conclusion: Overall, CDM users generally attributed high reliability for privacy protection to the intrinsic nature of CDM. There was little demand for additional de-identification methods.
      However, unstructured data in the CDM were suspected to have risks. The necessity for a coordinating consortium to define and manage the re-identification risk of CDM was urged.

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      참고문헌 (Reference)

      1 Overhage JM, "Validation of a common data model for active safety surveillance research" 19 (19): 54-60, 2012

      2 Keshta I, "Security and privacy of electronic health records : concerns and challenges" 22 (22): 177-183, 2021

      3 Jeon S, "Proposal and assessment of a de-identification strategy to enhance anonymity of the observational medical outcomes partnership common data model(OMOPCDM)in a public cloud-computing environment : anonymization of medical data using privacy models" 22 (22): e19597-, 2020

      4 International Organization for Standardization, "Privacy Enhancing Data De-identification Terminology and Classification of Techniques"

      5 Hripcsak G, "Observational Health Data Sciences and Informatics(OHDSI) : opportunities for observational researchers" 216 : 574-578, 2015

      6 O’Keefe CM, "Individual privacy versus public good : protecting confidentiality in health research" 34 (34): 3081-3103, 2015

      7 International Organization for Standardization, "Health Informatics — Pseudonymization"

      8 Garza M, "Evaluating common data models for use with a longitudinal community registry" 64 : 333-341, 2016

      9 PfaffER, "Ensuring a safe(r) harbor:excising personally identifiable information from structured electronic health record data" 6 (6): e10-, 2021

      10 Khare R, "Design and refinement of a data quality assessment workflow for a large pediatric research network" 7 (7): 36-, 2019

      1 Overhage JM, "Validation of a common data model for active safety surveillance research" 19 (19): 54-60, 2012

      2 Keshta I, "Security and privacy of electronic health records : concerns and challenges" 22 (22): 177-183, 2021

      3 Jeon S, "Proposal and assessment of a de-identification strategy to enhance anonymity of the observational medical outcomes partnership common data model(OMOPCDM)in a public cloud-computing environment : anonymization of medical data using privacy models" 22 (22): e19597-, 2020

      4 International Organization for Standardization, "Privacy Enhancing Data De-identification Terminology and Classification of Techniques"

      5 Hripcsak G, "Observational Health Data Sciences and Informatics(OHDSI) : opportunities for observational researchers" 216 : 574-578, 2015

      6 O’Keefe CM, "Individual privacy versus public good : protecting confidentiality in health research" 34 (34): 3081-3103, 2015

      7 International Organization for Standardization, "Health Informatics — Pseudonymization"

      8 Garza M, "Evaluating common data models for use with a longitudinal community registry" 64 : 333-341, 2016

      9 PfaffER, "Ensuring a safe(r) harbor:excising personally identifiable information from structured electronic health record data" 6 (6): e10-, 2021

      10 Khare R, "Design and refinement of a data quality assessment workflow for a large pediatric research network" 7 (7): 36-, 2019

      11 Malenfant JM, "Cross-Network Directory Service : Infrastructure to enable collaborations across distributed research networks" 3 (3): e10187-, 2019

      12 sjdjSchneeweiss S, "Choosing among common data models for realworld data analyses fit for making decisions about the effectiveness of medical products" 107 (107): 827-833, 2020

      13 You SC, "Association of ticagrelor vs clopidogrel with net adverse clinical events in patients with acute coronary syndrome undergoing percutaneous coronary intervention" 324 (324): 1640-1650, 2020

      14 Observational Health Data Sciences and Informatics, "ATLAS – a unified interface for the OHDSI tools"

      15 Benson K, "A comparison of observational studies and randomized, controlled trials" 342 (342): 1878-1886, 2000

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 SCI 등재 (등재유지) KCI등재
      2002-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1999-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.48 0.37 1.06
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.85 0.75 0.691 0.11
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