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The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System.
Verspoor, Karin.
  • Verspoor K; School of Computing Technologies, RMIT University, Melbourne VIC 3000 Australia.
Yearb Med Inform ; 30(1): 176-184, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1392942
ABSTRACT

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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Medical Informatics / Knowledge Management / Learning Health System / COVID-19 Type of study: Prognostic study / Reviews Limits: Humans Language: English Journal: Yearb Med Inform Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Medical Informatics / Knowledge Management / Learning Health System / COVID-19 Type of study: Prognostic study / Reviews Limits: Humans Language: English Journal: Yearb Med Inform Year: 2021 Document Type: Article