Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge—A movement to accelerate translation of knowledge into action

Richesson, Rachel L.; Bray, Bruce E.; Dymek, Christine; Greenes, Robert A.; McIntosh, Leslie D.; Middleton, Blackford; Perry, Gerald; Platt, Jodyn; Shaffer, C.

doi:10.1002/lrh2.10222

Cited by 3 publications

(3 citation statements)

References 7 publications

(10 reference statements)

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“… “Computable Biomedical Knowledge (CBK) is the result of an analytic or deliberative process about or affecting human health, that is explicit, and therefore can be represented and reasoned upon using logic, formal standards, and mathematical approaches”—MCBK Manifesto. 4 CBK is related to, but distinctly different from, biomedical data. While CBK has a direct connection with data, managing and sharing it has its own unique needs.…”

Section: Cbk Technical Infrastructure—components and Key Termsmentioning

confidence: 99%

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Guiding principles for technical infrastructure to support computable biomedical knowledge

McCusker

McIntosh

Shaffer

et al. 2022

Learning Health Systems

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Over the past 4 years, the authors have participated as members of the Mobilizing Computable Biomedical Knowledge Technical Infrastructure working group and focused on conceptualizing the infrastructure required to use computable biomedical knowledge. Here, we summarize our thoughts and lay the foundation for future work in the development of CBK infrastructure, including: explaining the difference between computable knowledge and data, and contextualizing the conversation with the Learning Health Systems and the FAIR principles. Specifically, we provide three guiding principles to advance the development of CBK infrastructure: (a) Promote interoperable systems for data and knowledge to be findable, accessible, interoperable, and reusable. (b) Enable stable, trustworthy knowledge representations that are human and machine readable. (c) Computable knowledge resources should, when possible, be open. Standards supporting computable knowledge infrastructures must be open.

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Section: Cbk Technical Infrastructure—components and Key Termsmentioning

confidence: 99%

“…The community is open and has a listserv. The MCBK SI group has met in person three times at public meetings hosted by NLM in the summers of 2018, 3 2019, 4 2020, 5 and has had multiple phone meetings in between. In the summer 2020 meeting, the SI workgroup continued work on this article.…”

Section: Introductionmentioning

confidence: 99%

Guiding principles for technical infrastructure to support computable biomedical knowledge

McCusker

McIntosh

Shaffer

et al. 2022

Learning Health Systems

Self Cite

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“…The push for formal representation of knowledge, including computational and executable models that can be integrated into health information systems to enable application of knowledge to practice [ 67 68 ], has gained momentum during COVID-19. The COVID-19 Disease Map project [ 69 70 ] captured and made available molecular interaction information for the SARS-CoV-2 virus, based on manual curation, supported by weekly videoconferences.…”

Section: A Global Learning Covid-19 Systemmentioning

confidence: 99%

The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System

Verspoor

2021

Yearb Med Inform

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Summary Objectives: We examine the knowledge ecosystem of COVID-19, focusing on clinical knowledge and the role of health informatics as enabling technology. We argue for commitment to the model of a global learning health system to facilitate rapid knowledge translation supporting health care decision making in the face of emerging diseases. Methods and Results: We frame the evolution of knowledge in the COVID-19 crisis in terms of learning theory, and present a view of what has occurred during the pandemic to rapidly derive and share knowledge as an (underdeveloped) instance of a global learning health system. We identify the key role of information technologies for electronic data capture and data sharing, computational modelling, evidence synthesis, and knowledge dissemination. We further highlight gaps in the system and barriers to full realisation of an efficient and effective global learning health system. Conclusions: The need for a global knowledge ecosystem supporting rapid learning from clinical practice has become more apparent than ever during the COVID-19 pandemic. Continued effort to realise the vision of a global learning health system, including establishing effective approaches to data governance and ethics to support the system, is imperative to enable continuous improvement in our clinical care.

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Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper

Petersen

Smith

Freimuth

et al. 2021

Journal of the American Medical Informatics Association

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The development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data—here referred to as Adaptive CDS—present unique challenges and considerations. Although Adaptive CDS represents an expected progression from earlier work, the activities needed to appropriately manage and support the establishment and evolution of Adaptive CDS require new, coordinated initiatives and oversight that do not currently exist. In this AMIA position paper, the authors describe current and emerging challenges to the safe use of Adaptive CDS and lay out recommendations for the effective management and monitoring of Adaptive CDS.

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Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge—A movement to accelerate translation of knowledge into action

Cited by 3 publications

References 7 publications

Guiding principles for technical infrastructure to support computable biomedical knowledge

Guiding principles for technical infrastructure to support computable biomedical knowledge

The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System

Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper

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