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    포스트 EHR시대를 대비한 임상연구 전략: 전자건강 기록을 이용한 관찰연구 = A clinical research strategy using longitudinal observational data in the post-electronic health records era

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    https://www.riss.kr/link?id=A103687041

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Adoption of electronic health records (EHRs) is increasing worldwide. The worldwide EHR adoption rate is estimated to be around 9% to 12%. Thus, the accumulation of medical records in electronic form is also sharply increasing and is expected to be a precious asset for clinical research. Longitudinal observational studies based on EHRs are also increasing.
    Observational studies covering more than a million people are not rare at present. However,much of the current EHR data are equivalent in form to those of paper records, but are just stored in electronic stor-age devices, rather than as electronic data that can be transferred and shared without loss of clinical semantics. Current EHR systems must be improved in many ways to be used for anal-yses to yield important clinical knowledge. These improvements, which are addressed in this review, include the adoption of clinical data warehouses, use of controlled vocabulary, avoidance of personal/departmental research databases, a standardized interface of many diagnostic devices with the EHR system, control of time-stamp granularity, preparedness for whole-genome sequencing of every patient, confederation or consolidation of multiinstitutional EHR data, protection of privacy and confidentiality, and an education system for clinical informaticians.
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    Adoption of electronic health records (EHRs) is increasing worldwide. The worldwide EHR adoption rate is estimated to be around 9% to 12%. Thus, the accumulation of medical records in electronic form is also sharply increasing and is expected to be a ...

    Adoption of electronic health records (EHRs) is increasing worldwide. The worldwide EHR adoption rate is estimated to be around 9% to 12%. Thus, the accumulation of medical records in electronic form is also sharply increasing and is expected to be a precious asset for clinical research. Longitudinal observational studies based on EHRs are also increasing.
    Observational studies covering more than a million people are not rare at present. However,much of the current EHR data are equivalent in form to those of paper records, but are just stored in electronic stor-age devices, rather than as electronic data that can be transferred and shared without loss of clinical semantics. Current EHR systems must be improved in many ways to be used for anal-yses to yield important clinical knowledge. These improvements, which are addressed in this review, include the adoption of clinical data warehouses, use of controlled vocabulary, avoidance of personal/departmental research databases, a standardized interface of many diagnostic devices with the EHR system, control of time-stamp granularity, preparedness for whole-genome sequencing of every patient, confederation or consolidation of multiinstitutional EHR data, protection of privacy and confidentiality, and an education system for clinical informaticians.

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    참고문헌 (Reference)

    1 이선미, "임상에서의 데이터 마이닝 개념과 원칙" 대한의료정보학회 15 (15): 175-189, 2009

    2 Jha AK, "Use of electronic health records in U.S. hospitals" 360 : 1628-1638, 2009

    3 Hripcsak G, "Unlocking clinical data from narrative reports: a study of natural language processing" 122 : 681-688, 1995

    4 Cios KJ, "Uniqueness of medical data mining" 26 : 1-24, 2002

    5 Dondorp WJ, "The ‘thousand-dollar genomeʼ: an ethical exploration" Centre for Ethics and Health 2010

    6 Platt R, "The U.S. Food and Drug Administrationʼs Mini-Sentinel program: status and direction" 21 (21): 1-8, 2012

    7 Trifiro G, "The EU-ADR project: preliminary results and perspective" 148 : 43-49, 2009

    8 U.S. Department of Health and Human Services, "Research repositories, databases, and the HIPAA privacy rule" National Institutes of Health

    9 Friedman C, "Representing information in patient reports using natural language processing and the extensible markup language" 6 : 76-87, 1999

    10 Meystre SM, "Randomized controlled trial of an automated problem list with improved sensitivity" 77 : 602-612, 2008

    1 이선미, "임상에서의 데이터 마이닝 개념과 원칙" 대한의료정보학회 15 (15): 175-189, 2009

    2 Jha AK, "Use of electronic health records in U.S. hospitals" 360 : 1628-1638, 2009

    3 Hripcsak G, "Unlocking clinical data from narrative reports: a study of natural language processing" 122 : 681-688, 1995

    4 Cios KJ, "Uniqueness of medical data mining" 26 : 1-24, 2002

    5 Dondorp WJ, "The ‘thousand-dollar genomeʼ: an ethical exploration" Centre for Ethics and Health 2010

    6 Platt R, "The U.S. Food and Drug Administrationʼs Mini-Sentinel program: status and direction" 21 (21): 1-8, 2012

    7 Trifiro G, "The EU-ADR project: preliminary results and perspective" 148 : 43-49, 2009

    8 U.S. Department of Health and Human Services, "Research repositories, databases, and the HIPAA privacy rule" National Institutes of Health

    9 Friedman C, "Representing information in patient reports using natural language processing and the extensible markup language" 6 : 76-87, 1999

    10 Meystre SM, "Randomized controlled trial of an automated problem list with improved sensitivity" 77 : 602-612, 2008

    11 "Overdose rate of drugs requiring renal dose adjustment: Data analysis of 4 years prescriptions at a tertiary teaching hospital" SPRINGER 23 (23): 423-428, 2008

    12 Prather JC, "Medical data mining: knowledge discovery in a clinical data warehouse" 101-105, 1997

    13 Xu H, "MedEx: a medication information extraction system for clinical narratives" 17 : 19-24, 2010

    14 Murphy SN, "Integration of clinical and genetic data in the i2b2 architecture" 1040-, 2006

    15 Hubner U, "IT adoption of clinical information systems in Austrian and German hospitals: results of a comparative survey with a focus on nursing" 10 : 8-, 2010

    16 Service RF, "Gene sequencing. The race for the $1000 genome" 311 : 1544-1546, 2006

    17 Haerian K, "Detection of pharmacovigilance-related adverse events using electronic health records and automated methods" 92 : 228-234, 2012

    18 "Detection of Adverse Drug Reaction Signals Using an Electronic Health Records Database: Comparison of the Laboratory Extreme Abnormality Ratio (CLEAR) Algorithm" NATURE PUBLISHING GROUP 91 (91): 467-474, 2012

    19 El Emam K, "De-identification methods for open health data: the case of the Heritage Health Prize claims dataset" 14 : e33-, 2012

    20 Han J, "Data warehouse and OLAP technology for data mining, In Data mining: concepts and techniques" Morgan Kaufmann Publishers 39-43, 2001

    21 Park MY, "Construction of an open-access QT database for detecting the proarrhythmia potential of marketed drugs: ECG-ViEW" 2012

    22 Yasunaga H, "Computerizing medical records in Japan" 77 : 708-713, 2008

    23 Park RW, "Computerized physician order entry and electronic medical record systems in Korean teaching and general hospitals: results of a 2004 survey" 12 : 642-647, 2005

    24 Ray WA, "Azithromycin and the risk of cardiovascular death" 366 : 1881-1890, 2012

    25 Yoon D, "Adoption of electronic health records in Korean tertiary teaching and general hospitals." ELSEVIER IRELAND LTD 81 (81): 196-203, 2012

    26 Park MY, "A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database" WILEY-BLACKWELL 20 (20): 598-607, 2011

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    학술지 이력

    학술지 이력
    연월일 이력구분 이력상세 등재구분
    2024 평가 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
    2021-01-01 등재 등재학술지 선정 (해외등재 학술지 평가) KCI등재
    2020-12-01 등재 등재 탈락 (해외등재 학술지 평가)
    2013-10-01 등재 등재학술지 선정 (기타) KCI등재
    2011-01-01 등재 등재후보학술지 유지 (기타) KCI등재후보
    2007-01-01 등재 SCOPUS 등재 (신규평가) KCI등재후보
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    기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
    2016 0.33 0.33 0.48
    KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
    0.5 0.57 0.815 0.12
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