AI Article Synopsis

  • The paper discusses the challenges of mobilizing computable biomedical knowledge for clinical decision support, emphasizing insights gained from a workshop with healthcare professionals.
  • Key difficulties include creating machine-readable content, maintaining standards, tracking the impact of knowledge on patient outcomes, and addressing issues like comorbidities and safety.
  • The author stresses the importance of collaboration among institutions, as the complexity of these challenges necessitates collective efforts for effective solutions.

Article Abstract

Introduction: This paper summarises a talk given at the first UK workshop on mobilising computable biomedical knowledge on 29 October 2019 in London. It examines challenges in mobilising computable biomedical knowledge for clinical decision support from the perspective of a medical knowledge provider.

Methods: We developed the themes outlined below after personally reflecting on the challenges that we have encountered in this field and after considering the barriers that knowledge providers face in ensuring that their content is accessed and used by healthcare professionals. We further developed the themes after discussing them with delegates at the workshop and listening to their feedback.

Discussion: There are many challenges in mobilising computable knowledge for clinical decision support from the perspective of a medical knowledge provider. These include the size of the task at hand, the challenge of creating machine interpretable content, the issue of standards, the need to do better in tracing how computable medical knowledge that is part of clinical decision support impacts patient outcomes, the challenge of comorbidities, the problem of adhering to safety standards and finally the challenge of integrating knowledge with problem solving and procedural skills, healthy attitudes and professional behaviours. Partnership is likely to be essential if we are to make progress in this field. The problems are too complex and interrelated to be solved by any one institution alone.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7388874PMC
http://dx.doi.org/10.1136/bmjhci-2019-100121DOI Listing

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