Difference between revisions of "Mobilizing Computable Biomedical Knowledge"

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Submitted by Steven Chin
 
Submitted by Steven Chin
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Latest revision as of 17:53, 10 May 2022

Overview

Timely, widely available access to the best available evidence is a cornerstone component of high-quality healthcare (1). However, it is widely recognized that on average, over a decade may elapse before new, validated evidence is integrated into practice (1). Due to exponential increases in knowledge generation and scientific discovery, this “evidence to practice” gap in healthcare will continue to widen until more agile, rapidly deployable and scalable forms of knowledge representation compared to human readable forms (printed words, static images) are developed and disseminated (2). Advances in digital technology enable healthcare knowledge to be represented in standardized, machine-executable code, or computable biomedical knowledge. Computable Biomedical Knowledge unlocks the potential of modern information technology to generate and deliver decision-specific advice to individuals and organizations with greater speed, at a world-wide scale, enabling a healthcare system that rapidly and continuously integrates new knowledge and evidence at scale and learns, or a Learning Health System (LHS) (See Learning Health Systems) (2). Mobilizing Computable Biomedical Knowledge (MBCK.org) is a nascent movement to promote and operationalize the “…curation, dissemination and application of computable biomedical knowledge at a global scale” and it’s integration into health information systems and applications (3).

Background

Since the initial envisioning of the LHS in the Institute of Medicine (IOM) report Crossing the Quality Chasm (2001), the follow decade of research and development of the LHS model has demonstrated that new infrastructure would be necessary to support a shift towards real-time evidence sharing and more rapid application of evidence to change practice and improve care (2). The importance of this shift towards augmenting standard knowledge representations such as text, tables and figures with standardized, computable representations is reflected in the National Library of Medicine Long Range Plan 2017-2027 (4). Joint meetings sponsored by the University of Michigan and National Library of Medicine in 2018-19 have laid the foundation to develop and disseminate the concept of machine-executable biomedical knowledge as a key strategy to improve the health and address the evidence to practice gap


What is Computable Biomedical Knowledge

Evidence of any type – guidelines, recommendations, rules, predictive models can be packaged in a standardized format as a digital knowledge object (DKO). Important metadata containing essential information (analytic results used the generate the DKO, strength of evidence, pertinent population) is attached to the DKO. These DKO’s can be curated and managed in digital libraries and made available to users. The modular nature of these digital knowledge representations allows integration of locally produced evidence and published external evidence and enables customization of DKO’s to match the specific population and system at hand. The use of existing open interoperable data standards will enable rapid integration into health information systems and applications (5)


Important Elements of Computable Biomedical Knowledge (6)

The CBK Concept

•Sustain the Computable Biomedical Knowledge ecosystem through public-private partnerships

•Establish broadly-based participatory governance of the ecosystem

•Explore the sciences of Computable Biomedical Knowledge collaboratively

•Ensure the Computable Biomedical Knowledge ecosystem is FAIR – easily findable, universally accessible, highly interoperable, and readily reusable


The CBK Technical System

•Enable the ecosystem with open standards

•Build and uphold trust in Computable Biomedical Knowledge through the ecosystem

•Ensure robust and unbiased methods to support transparency

•Implement the highest standards of privacy and security for all stakeholders

•Enable a pipeline that transitions knowledge from human-readable to fully computable through successive stages


The CBK User System

•Ensure the safe and effective use of Computable Biomedical Knowledge through the ecosystem

•Generate value for the creators of the knowledge, the users of the knowledge and the general public

•Engender equity in health and in knowledge accessibility


Examples of CBK

Examples of Computable Biomedical Knowledge can be found in several existing software products such as the Semedy Knowledge Management System, and Knowledge Grid platform developed by the University of Michigan. The journal Learning Health Systems has established “Computable Knowledge” as a new peer-reviewed publication type. Publications will include computable versions of the knowledge that can be accessed general use, through an appropriate open source license. Instructions for authors can be found: https://onlinelibrary.wiley.com/page/journal/23796146/homepage/forauthors.html

References

1.Institute of Medicine (US) Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington (DC): National Academies Press (US); 2001. PMID: 25057539

2.Institute of Medicine (US). Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary. Grossmann C, Powers B, McGinnis JM, editors. Washington (DC): National Academies Press (US); 2011. PMID: 22379651

3.https://mobilizecbk.med.umich.edu/about/manifesto

4.US National Library of Medicine. A Platform for Biomedical Discovery and Data-Powered Health – Strategic Plan 2017-2027/report of the NLM Board of Regents. December 2017. NLM Z 675.M4

5.Guise JM, Savitz LA, Friedman CP. Mind the Gap: Putting Evidence into Practice in the Era of Learning Health Systems. J Gen Intern Med. 2018 Dec;33(12):2237-2239. doi: 10.1007/s11606-018-4633-1.Epub 2018 Aug 28. PMID: 30155611; PMCID: PMC6258636

6.https://mobilizecbk.med.umich.edu/home

Submitted by Steven Chin