Exploring the patterns of real-world data governance in the context of special healthcare policy_Chinese Journal of Evidence-Based Medicine

Authors：

JIA Yulong ^1,2,3 , YAO Minghong ^1,2,3 , LIU Yanmei ^1,2,3 , REN Yan ^1,2,3 , ZOU Kang ^1,2,3 ,  LI Yaohua ⁴ ,  SUN Xin ^1,2,3

1. Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;
2. NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, P.R.China;
3. Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, P.R.China;
4. Hainan Provincial Drug and Medical Device Evaluation Service Center, Haikou 570216, P.R.China;

Corresponding?author：

LI Yaohua, Email: sunx79@hotmail.com; SUN Xin, Email: sunxin@wchscu.cn

Keywords：

Licensed medical care; Boao hope city; Real-world study; Data management; Data governance

DOI：

10.7507/1672-2531.202108147

Video：

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With the increasing improvement of real-world evidence as a research system and guideline specification for pre-market registration and post-market regulatory decision support of clinically urgent drug and mechanical products, identifying an approach to ensure the high quality and standards of real-world data and establishing a basis for the generation of real-world evidence is receiving increasing attention and concern from regulatory authorities. Based on the experience of Boao hope city real-world data research pattern and ophthalmic data platform construction, this paper discussed the "source data-database-evidence chain" generation process, data management, and data governance in real-world study from the special features and necessity of multiple sources and heterogeneity of data, multiple research designs, and standardized regulatory requirements, and provided references for further construction of comprehensive research data platforms in the future.

Citation： JIA Yulong, YAO Minghong, LIU Yanmei, REN Yan, ZOU Kang, LI Yaohua, SUN Xin. Exploring the patterns of real-world data governance in the context of special healthcare policy. Chinese Journal of Evidence-Based Medicine, 2021, 21(12): 1373-1380. doi: 10.7507/1672-2531.202108147 Copy

1.	王雯, 譚婧, 任燕, 等. 重新認識真實世界數據研究: 更新與展望. 中國循證醫學雜志, 2020, 20(11): 1241-1246.
2.	孫鑫, 譚婧, 唐立, 等. 重新認識真實世界研究. 中國循證醫學雜志, 2017, 17(2): 126-130.
3.	國家藥品監督管理局藥品審評中心. 用于產生真實世界證據的真實世界數據指導原則(試行), 2021. Available at: http://www.cde.org.cn/news.do?method=largeInfo&id=eaed86b800e8d9d9.
4.	國家發展改革委, 國家衛生健康委, 國家中醫藥局, 等. 關于支持建設博鰲樂城國際醫療旅游先行區的實施方案, 2019. Available at:http://www.gov.cn/xinwen/2019-09/17/content_5430452.htm.
5.	任燕, 姚明宏, 姚晨, 等. 特許創新藥械在博鰲樂城開展真實世界數據研究的模式探索. 中國食品藥品監管, 2020, (11): 14-20.
6.	任燕, 梁遠波, 劉密密, 等. 構建博鰲樂城創新藥械真實世界數據研究體系: 基于我國首個使用境內真實世界數據獲批上市的醫療器械研究的探討. 中國循證醫學雜志, 2020, 20(10): 1117-1123.
7.	姚明宏, 賈玉龍, 任燕, 等. 特許醫療政策下的藥械真實世界數據研究常見設計及其關鍵考慮. 中華流行病學雜志, 2021, 42(7): 1306-1311.
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10.	國家藥品監督管理局. 臨床試驗數據管理工作技術指南, 2016. Available at: https://wenku.baidu.com/view/3e468e1f5ef7ba0d4b733bc0.html.
11.	U. S. FDA. Guidance for industry computerized systems used in clinical investigations, 2007. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/computerized-systems-used-clinical-investigations.
12.	European Medicines Agency. Reflection paper on expectations for electronic source data and data transcribed to electronic data collection tools in clinical trials, 2010. Available at: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/reflection-paper-expectations-electronic-source-data-data-transcribed-electronic-data-collection_en.pdf.
13.	國家藥品監督管理局. 臨床試驗的電子數據采集技術指導原則, 2016. Available at: https://www.nmpa.gov.cn/yaopin/ypggtg/ypqtgg/20160729184001958.html.
14.	國家藥品監督管理局. 真實世界數據用于醫療器械臨床評價技術指導原則(試行), 2020. Available at: https://www.nmpa.gov.cn/xxgk/ggtg/qtggtg/20201126090030150.html.
15.	國家藥品監督管理局, 國家衛生健康委員會. 藥物臨床試驗質量管理規范, 2020. Available at:https://www.nmpa.gov.cn/xxgk/ggtg/qtggtg/20200426162401243.html.
16.	Society for Clinical Data Management. Good clinical data management practices, 2013. Available at: http://www.doctorama.com/event/Good-Clinical-Data-Management-Practices-GCDMP-28264.htm.
17.	國家藥品監督管理局藥品審評中心. 藥物臨床試驗數據遞交指導原則(試行), 2020. Available at: https://www.nmpa.gov.cn/yaopin/ypggtg/ypqtgg/20200720171201514.html.
18.	Andriole KP. Security of electronic medical information and patient privacy: what you need to know. J Am Coll Radiol, 2014, 11(12PtB): 1212-1216.
19.	譚婧, 程亮亮, 王雯, 等. 患者登記研究的策劃與患者登記數據庫構建: 基于觀察性設計的真實世界研究. 中國循證醫學雜志, 2017, 17(12): 1365-1372.
20.	Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng, 2018, 2(10): 719-731.
21.	張淑杰. 基于云計算技術的大數據分析平臺設計與開發. 電子測試, 2021, (2): 78-79,94.
22.	丁發明, 閆慧, 黃天嬌. 對臨床試驗中運用遠程智能技術的監管策略的科學建議. 中國食品藥品監管, 2020, (11): 125-133.
23.	Health Level Seven International. HL7 Standards, 2021. Available at:https://www.hl7.org/.
24.	Khvastova M, Witt M, Essenwanger A, et al. Towards interoperability in clinical research - enabling fhir on the open-source research platform XNAT. J Med Syst, 2020, 44(8): 137.
25.	李雪迎, 姚晨, 晉菲斐, 等. 面向真實世界數據的臨床研究數據治理模式選擇. 中國循證醫學雜志, 2020, 20(10): 1150-1156.
26.	國務院辦公廳. 關于促進和規范健康醫療大數據應用發展的指導意見, 2016. Available at: http://www.gov.cn/zhengce/content/2016-06/24/content_5085091.htm#.
27.	Foundation O. What is openEHR? 2021. Available at:https://www.openehr.org/about/what_is_openehr.
28.	Wulff A, Mast M, Hassler M, et al. Designing an openEHR-based pipeline for extracting and standardizing unstructured clinical data using natural language processing. Methods Inf Med, 2020, 59(S02): e64-e78.
29.	國家衛生計生委. 衛生信息共享文檔編制規范, 2016. Available at:http://www.nhc.gov.cn/wjw/s9497/201607/f1472d0f82bf48ebb8da4ffe5000e0aa.shtml.
30.	CDISC Submission Data Standards Team. Study data tabulation model implementation guide: human clinical trials version 3.3 (final), 2018. Available at: http://www.sastricks.com/cdisc/SDTMIG_v3.3_FINAL.pdf.
31.	Riepenhausen S, Mertens C, Dugas M. Comparing SDTM and FHIR? for real world data from electronic health records for clinical trial submissions. Stud Health Technol Inform, 2021, 281: 585-589.
32.	OMOP. Common data model, 2021. Available at: https://github.com/OHDSI/CommonDataModel.
33.	侯麗, 李露琪, 唐靈逸, 等. OHDSI通用數據模型及醫學術語標準國內應用現狀分析. 醫學信息學雜志, 2020, 41(2): 1-10.
34.	閻小妍, 董沖亞, 姚晨. 保障中國臨床試驗數據的準確性. 英國醫學雜志中文版, 2018, 21(11): 639-640.
35.	中國醫院協會信息專業委員會. 醫療機構醫療大數據平臺建設指南(征求意見稿). 北京: 中國工信出版集團, 2019.
36.	國家藥品監督管理局. 深刻認識和準確把握“十四五”時期我國發展面臨的外部環境, 2020. Available at: https://www.nmpa.gov.cn/zhuanti/bwchx/bwxxjl/20201218085425130.html.

1. 王雯, 譚婧, 任燕, 等. 重新認識真實世界數據研究: 更新與展望. 中國循證醫學雜志, 2020, 20(11): 1241-1246.
2. 孫鑫, 譚婧, 唐立, 等. 重新認識真實世界研究. 中國循證醫學雜志, 2017, 17(2): 126-130.
3. 國家藥品監督管理局藥品審評中心. 用于產生真實世界證據的真實世界數據指導原則(試行), 2021. Available at: http://www.cde.org.cn/news.do?method=largeInfo&id=eaed86b800e8d9d9.
4. 國家發展改革委, 國家衛生健康委, 國家中醫藥局, 等. 關于支持建設博鰲樂城國際醫療旅游先行區的實施方案, 2019. Available at:http://www.gov.cn/xinwen/2019-09/17/content_5430452.htm.
5. 任燕, 姚明宏, 姚晨, 等. 特許創新藥械在博鰲樂城開展真實世界數據研究的模式探索. 中國食品藥品監管, 2020, (11): 14-20.
6. 任燕, 梁遠波, 劉密密, 等. 構建博鰲樂城創新藥械真實世界數據研究體系: 基于我國首個使用境內真實世界數據獲批上市的醫療器械研究的探討. 中國循證醫學雜志, 2020, 20(10): 1117-1123.
7. 姚明宏, 賈玉龍, 任燕, 等. 特許醫療政策下的藥械真實世界數據研究常見設計及其關鍵考慮. 中華流行病學雜志, 2021, 42(7): 1306-1311.
8. 國家藥品監督管理局藥品審評中心. 真實世界證據支持藥物研發與審評的指導原則(試行), 2020. Available at: https://www.nmpa.gov.cn/yaopin/ypggtg/ypqtgg/20200107151901190.html.
9. U. S. FDA. Framework for FDA’s real world evidence program, 2018. Available at:https://www.fda.gov/drugs/webinar-framework-fdas-real-world-evidence-program-mar-15-2019.
10. 國家藥品監督管理局. 臨床試驗數據管理工作技術指南, 2016. Available at: https://wenku.baidu.com/view/3e468e1f5ef7ba0d4b733bc0.html.
11. U. S. FDA. Guidance for industry computerized systems used in clinical investigations, 2007. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/computerized-systems-used-clinical-investigations.
12. European Medicines Agency. Reflection paper on expectations for electronic source data and data transcribed to electronic data collection tools in clinical trials, 2010. Available at: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/reflection-paper-expectations-electronic-source-data-data-transcribed-electronic-data-collection_en.pdf.
13. 國家藥品監督管理局. 臨床試驗的電子數據采集技術指導原則, 2016. Available at: https://www.nmpa.gov.cn/yaopin/ypggtg/ypqtgg/20160729184001958.html.
14. 國家藥品監督管理局. 真實世界數據用于醫療器械臨床評價技術指導原則(試行), 2020. Available at: https://www.nmpa.gov.cn/xxgk/ggtg/qtggtg/20201126090030150.html.
15. 國家藥品監督管理局, 國家衛生健康委員會. 藥物臨床試驗質量管理規范, 2020. Available at:https://www.nmpa.gov.cn/xxgk/ggtg/qtggtg/20200426162401243.html.
16. Society for Clinical Data Management. Good clinical data management practices, 2013. Available at: http://www.doctorama.com/event/Good-Clinical-Data-Management-Practices-GCDMP-28264.htm.
17. 國家藥品監督管理局藥品審評中心. 藥物臨床試驗數據遞交指導原則(試行), 2020. Available at: https://www.nmpa.gov.cn/yaopin/ypggtg/ypqtgg/20200720171201514.html.
18. Andriole KP. Security of electronic medical information and patient privacy: what you need to know. J Am Coll Radiol, 2014, 11(12PtB): 1212-1216.
19. 譚婧, 程亮亮, 王雯, 等. 患者登記研究的策劃與患者登記數據庫構建: 基于觀察性設計的真實世界研究. 中國循證醫學雜志, 2017, 17(12): 1365-1372.
20. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng, 2018, 2(10): 719-731.
21. 張淑杰. 基于云計算技術的大數據分析平臺設計與開發. 電子測試, 2021, (2): 78-79,94.
22. 丁發明, 閆慧, 黃天嬌. 對臨床試驗中運用遠程智能技術的監管策略的科學建議. 中國食品藥品監管, 2020, (11): 125-133.
23. Health Level Seven International. HL7 Standards, 2021. Available at:https://www.hl7.org/.
24. Khvastova M, Witt M, Essenwanger A, et al. Towards interoperability in clinical research - enabling fhir on the open-source research platform XNAT. J Med Syst, 2020, 44(8): 137.
25. 李雪迎, 姚晨, 晉菲斐, 等. 面向真實世界數據的臨床研究數據治理模式選擇. 中國循證醫學雜志, 2020, 20(10): 1150-1156.
26. 國務院辦公廳. 關于促進和規范健康醫療大數據應用發展的指導意見, 2016. Available at: http://www.gov.cn/zhengce/content/2016-06/24/content_5085091.htm#.
27. Foundation O. What is openEHR? 2021. Available at:https://www.openehr.org/about/what_is_openehr.
28. Wulff A, Mast M, Hassler M, et al. Designing an openEHR-based pipeline for extracting and standardizing unstructured clinical data using natural language processing. Methods Inf Med, 2020, 59(S02): e64-e78.
29. 國家衛生計生委. 衛生信息共享文檔編制規范, 2016. Available at:http://www.nhc.gov.cn/wjw/s9497/201607/f1472d0f82bf48ebb8da4ffe5000e0aa.shtml.
30. CDISC Submission Data Standards Team. Study data tabulation model implementation guide: human clinical trials version 3.3 (final), 2018. Available at: http://www.sastricks.com/cdisc/SDTMIG_v3.3_FINAL.pdf.
31. Riepenhausen S, Mertens C, Dugas M. Comparing SDTM and FHIR? for real world data from electronic health records for clinical trial submissions. Stud Health Technol Inform, 2021, 281: 585-589.
32. OMOP. Common data model, 2021. Available at: https://github.com/OHDSI/CommonDataModel.
33. 侯麗, 李露琪, 唐靈逸, 等. OHDSI通用數據模型及醫學術語標準國內應用現狀分析. 醫學信息學雜志, 2020, 41(2): 1-10.
34. 閻小妍, 董沖亞, 姚晨. 保障中國臨床試驗數據的準確性. 英國醫學雜志中文版, 2018, 21(11): 639-640.
35. 中國醫院協會信息專業委員會. 醫療機構醫療大數據平臺建設指南(征求意見稿). 北京: 中國工信出版集團, 2019.
36. 國家藥品監督管理局. 深刻認識和準確把握“十四五”時期我國發展面臨的外部環境, 2020. Available at: https://www.nmpa.gov.cn/zhuanti/bwchx/bwxxjl/20201218085425130.html.

Chinese Journal of Evidence-Based Medicine

Exploring the patterns of real-world data governance in the context of special healthcare policy

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