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Big Data: Are Biomedical and Health Informatics Training Programs Ready? Contribution of the IMIA Working Group for Health and Medical Informatics Education.大数据:生物医学与健康信息学培训项目准备好了吗?国际医学信息学协会健康与医学信息学教育工作组的贡献。
Yearb Med Inform. 2014 Aug 15;9(1):177-81. doi: 10.15265/IY-2014-0007.
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Recommendations of the International Medical Informatics Association (IMIA) on education in health and medical informatics.国际医学信息学协会(IMIA)关于健康与医学信息学教育的建议。
Stud Health Technol Inform. 2004;109:226-43.
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JAMIA Open. 2022 Aug 27;5(3):ooac073. doi: 10.1093/jamiaopen/ooac073. eCollection 2022 Oct.
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[Not Available].[无可用内容]
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Annu Rev Public Health. 2018 Apr 1;39:95-112. doi: 10.1146/annurev-publhealth-040617-014208. Epub 2017 Dec 20.
8
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本文引用的文献

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Educating the Next Generation of Data Scientists.培养下一代数据科学家。
Big Data. 2013 Mar;1(1):21-7. doi: 10.1089/big.2013.1510.
2
Recommendations for the use of operational electronic health record data in comparative effectiveness research.关于在比较效果研究中使用电子健康记录操作数据的建议。
EGEMS (Wash DC). 2013 Oct 8;1(1):1018. doi: 10.13063/2327-9214.1018. eCollection 2013.
3
What Big Data means to me.大数据对我的意义。
J Am Med Inform Assoc. 2014 Mar-Apr;21(2):194. doi: 10.1136/amiajnl-2014-002651.
4
Health information exchange, system size and information silos.健康信息交换、系统规模与信息孤岛
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Potentiality of big data in the medical sector: focus on how to reshape the healthcare system.大数据在医疗领域的潜力:聚焦于如何重塑医疗体系。
Healthc Inform Res. 2013 Jun;19(2):79-85. doi: 10.4258/hir.2013.19.2.79. Epub 2013 Jun 30.
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Building trust in the power of "big data" research to serve the public good.建立对“大数据”研究造福公众利益力量的信任。
JAMA. 2013 Jun 19;309(23):2443-4. doi: 10.1001/jama.2013.5914.
7
Caveats for the use of operational electronic health record data in comparative effectiveness research.使用操作性电子健康记录数据进行比较有效性研究的注意事项。
Med Care. 2013 Aug;51(8 Suppl 3):S30-7. doi: 10.1097/MLR.0b013e31829b1dbd.
8
The inevitable application of big data to health care.大数据在医疗保健领域的必然应用。
JAMA. 2013 Apr 3;309(13):1351-2. doi: 10.1001/jama.2013.393.
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Data scientist: the sexiest job of the 21st century.数据科学家:21 世纪最性感的工作。
Harv Bus Rev. 2012 Oct;90(10):70-6, 128.
10
Implementing the learning health system: from concept to action.实施学习型卫生系统:从理念到行动。
Ann Intern Med. 2012 Aug 7;157(3):207-10. doi: 10.7326/0003-4819-157-3-201208070-00012.

大数据:生物医学与健康信息学培训项目准备好了吗?国际医学信息学协会健康与医学信息学教育工作组的贡献。

Big Data: Are Biomedical and Health Informatics Training Programs Ready? Contribution of the IMIA Working Group for Health and Medical Informatics Education.

作者信息

Otero P, Hersh W, Jai Ganesh A U

机构信息

Dra. Paula Otero, Department of Health Informatics, Hospital Italiano de Buenos Aires, Peron 4190, (1199) Ciudad Autonoma de Buenos, Argentina, E-mail:

出版信息

Yearb Med Inform. 2014 Aug 15;9(1):177-81. doi: 10.15265/IY-2014-0007.

DOI:10.15265/IY-2014-0007
PMID:25123740
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4287071/
Abstract

OBJECTIVE

The growing volume and diversity of health and biomedical data indicate that the era of Big Data has arrived for healthcare. This has many implications for informatics, not only in terms of implementing and evaluating information systems, but also for the work and training of informatics researchers and professionals. This article addresses the question: What do biomedical and health informaticians working in analytics and Big Data need to know?

METHODS

We hypothesize a set of skills that we hope will be discussed among academic and other informaticians.

RESULTS

The set of skills includes: Programming - especially with data-oriented tools, such as SQL and statistical programming languages; Statistics - working knowledge to apply tools and techniques; Domain knowledge - depending on one's area of work, bioscience or health care; and Communication - being able to understand needs of people and organizations, and articulate results back to them.

CONCLUSION

Biomedical and health informatics educational programs must introduce concepts of analytics, Big Data, and the underlying skills to use and apply them into their curricula. The development of new coursework should focus on those who will become experts, with training aiming to provide skills in "deep analytical talent" as well as those who need knowledge to support such individuals.

摘要

目的

健康和生物医学数据的数量不断增长且种类日益多样,这表明医疗保健领域已迎来大数据时代。这对信息学有诸多影响,不仅体现在信息系统的实施和评估方面,还涉及信息学研究人员和专业人员的工作与培训。本文探讨的问题是:从事分析和大数据工作的生物医学与健康信息学专家需要了解什么?

方法

我们假设了一系列技能,希望能在学术及其他信息学专家之间展开讨论。

结果

这些技能包括:编程——特别是使用面向数据的工具,如SQL和统计编程语言;统计学——应用工具和技术的实用知识;领域知识——取决于个人工作领域,如生物科学或医疗保健;以及沟通能力——能够理解人员和组织的需求,并向他们清晰阐述结果。

结论

生物医学与健康信息学教育项目必须将分析、大数据的概念以及使用和应用这些概念的基础技能引入课程。新课程的开发应聚焦于那些将成为专家的人,培训旨在提供“深度分析人才”所需技能,同时也满足那些需要知识来支持此类人员的需求。