Suppr超能文献

可计算生物医学知识库调查。

A survey of computable biomedical knowledge repositories.

作者信息

Platt Jodyn E, Solomonides Anthony E, Walker Philip D, Amara Philip S, Richardson Joshua E, Middleton Blackford

机构信息

University of Michigan Medical School Department of Learning Health Sciences Ann Arbor Michigan USA.

Research Institute, NorthShore University HealthSystem Evanston Illinois USA.

出版信息

Learn Health Syst. 2022 Jun 3;7(1):e10314. doi: 10.1002/lrh2.10314. eCollection 2023 Jan.

Abstract

INTRODUCTION

While data repositories are well-established in clinical and research enterprises, knowledge repositories with shareable computable biomedical knowledge (CBK) are relatively new entities to the digital health ecosystem. Trustworthy knowledge repositories are necessary for learning health systems, but the policies, standards, and practices to promote trustworthy CBK artifacts and methods to share, and safely and effectively use them are not well studied.

METHODS

We conducted an online survey of 24 organizations in the United States known to be involved in the development or deployment of CBK. The aim of the survey was to assess the current policies and practices governing these repositories and to identify best practices. Descriptive statistics methods were applied to data from 13 responding organizations, to identify common practices and policies instantiating the TRUST principles of Transparency, Responsibility, User Focus, Sustainability, and Technology.

RESULTS

All 13 respondents indicated to different degrees adherence to policies that convey TRUST. is conveyed by having policies pertaining to provenance, credentialed contributors, and provision of metadata. Repositories provide knowledge in machine-readable formats, include implementation guidelines, and adhere to standards to convey . Repositories report having functions that enable end-users to verify, search, and filter for knowledge products. Less common TRUST practices are procedures that enable consumers to know about user licensing requirements or query the use of knowledge artifacts. Related to , less than a majority post describe their sustainability plans. Few organizations publicly describe whether patients play any role in their decision-making.

CONCLUSION

It is essential that knowledge repositories identify and apply a baseline set of criteria to lay a robust foundation for their trustworthiness leading to optimum uptake, and safe, reliable, and effective use to promote sharing of CBK. Identifying current practices suggests a set of desiderata for the CBK ecosystem in its continued evolution.

摘要

引言

虽然数据存储库在临床和研究机构中已得到广泛应用,但具有可共享的可计算生物医学知识(CBK)的知识存储库对于数字健康生态系统来说还是相对较新的实体。值得信赖的知识存储库对于学习型健康系统至关重要,但促进可信赖的CBK工件以及共享、安全有效使用这些工件的方法的政策、标准和实践尚未得到充分研究。

方法

我们对美国已知参与CBK开发或部署的24个组织进行了在线调查。该调查的目的是评估管理这些存储库的现行政策和实践,并确定最佳实践。描述性统计方法应用于来自13个回复组织的数据,以确定体现透明度、责任、用户关注、可持续性和技术等TRUST原则的常见实践和政策。

结果

所有13位受访者均表示在不同程度上遵守传达TRUST的政策。通过制定有关出处、有资质的贡献者和元数据提供的政策来传达透明度。存储库以机器可读格式提供知识,包括实施指南,并遵守标准以传达责任。存储库报告具有使最终用户能够验证、搜索和筛选知识产品的功能。不太常见的TRUST实践是使消费者能够了解用户许可要求或查询知识工件使用情况的程序。与可持续性相关的是,不到一半的组织描述了他们的可持续发展计划。很少有组织公开描述患者在其决策中是否发挥任何作用。

结论

知识存储库必须确定并应用一组基线标准,为其可信度奠定坚实基础,以实现最佳采用率,并安全、可靠且有效地用于促进CBK的共享。确定当前实践为CBK生态系统在其持续发展中提出了一系列 desiderata。

相似文献

1
A survey of computable biomedical knowledge repositories.
Learn Health Syst. 2022 Jun 3;7(1):e10314. doi: 10.1002/lrh2.10314. eCollection 2023 Jan.
2
Guiding principles for technical infrastructure to support computable biomedical knowledge.
Learn Health Syst. 2022 Nov 1;7(3):e10352. doi: 10.1002/lrh2.10352. eCollection 2023 Jul.
3
Summary of fourth annual MCBK public meeting: Mobilizing computable biomedical knowledge-metadata and trust.
Learn Health Syst. 2021 Dec 22;6(1):e10301. doi: 10.1002/lrh2.10301. eCollection 2022 Jan.
4
Categorizing metadata to help mobilize computable biomedical knowledge.
Learn Health Syst. 2021 May 9;6(1):e10271. doi: 10.1002/lrh2.10271. eCollection 2022 Jan.
5
Summary of fifth annual public MCBK meeting: Mobilizing computable biomedical knowledge (CBK) around the world.
Learn Health Syst. 2023 Jan 12;7(1):e10357. doi: 10.1002/lrh2.10357. eCollection 2023 Jan.
6
Summary of third annual MCBK public meeting: Mobilizing computable biomedical knowledge-Accelerating the second knowledge revolution.
Learn Health Syst. 2020 Dec 23;5(1):e10255. doi: 10.1002/lrh2.10255. eCollection 2021 Jan.
9
Transforming health and well-being through publishing computable biomedical knowledge (CBK).
Learn Health Syst. 2023 Oct 5;7(4):e10396. doi: 10.1002/lrh2.10396. eCollection 2023 Oct.
10
The future of Cochrane Neonatal.
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.

引用本文的文献

1
Working in biocuration: contemporary experiences and perspectives.
Database (Oxford). 2025 Feb 12;2025. doi: 10.1093/database/baaf003.

本文引用的文献

1
Knowledge repositories. In digital knowledge we trust.
Med Health Care Philos. 2020 Dec;23(4):543-547. doi: 10.1007/s11019-020-09978-9.
2
Fair trade in building digital knowledge repositories: the knowledge economy as if researchers mattered.
Med Health Care Philos. 2020 Dec;23(4):549-563. doi: 10.1007/s11019-020-09966-z.
3
Openness and trust in data-intensive science: the case of biocuration.
Med Health Care Philos. 2020 Sep;23(3):497-504. doi: 10.1007/s11019-020-09960-5.
4
Silencing trust: confidence and familiarity in re-engineering knowledge infrastructures.
Med Health Care Philos. 2020 Sep;23(3):471-484. doi: 10.1007/s11019-020-09957-0.
5
The TRUST Principles for digital repositories.
Sci Data. 2020 May 14;7(1):144. doi: 10.1038/s41597-020-0486-7.
6
Building and maintaining trust in clinical decision support: Recommendations from the Patient-Centered CDS Learning Network.
Learn Health Syst. 2019 Dec 11;4(2):e10208. doi: 10.1002/lrh2.10208. eCollection 2020 Apr.
7
Trust in Centralized Large-Scale Data Repository: A Qualitative Analysis.
J Empir Res Hum Res Ethics. 2020 Oct;15(4):365-378. doi: 10.1177/1556264619888365. Epub 2019 Nov 18.
8
The FAIR Guiding Principles for scientific data management and stewardship.
Sci Data. 2016 Mar 15;3:160018. doi: 10.1038/sdata.2016.18.
9
Data Safe Havens in health research and healthcare.
Bioinformatics. 2015 Oct 15;31(20):3241-8. doi: 10.1093/bioinformatics/btv279. Epub 2015 Jun 25.
10
A multi-layered framework for disseminating knowledge for computer-based decision support.
J Am Med Inform Assoc. 2011 Dec;18 Suppl 1(Suppl 1):i132-9. doi: 10.1136/amiajnl-2011-000334. Epub 2011 Nov 3.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验