Article type
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Abstract
Background: Major advances in standards, systems and technological platforms for evidence production and dissemination may together reduce waste and increase value in medical research, reduce information overload and result in better decisions at the point of care. Innovative technological platforms can connect people doing primary research, systematic reviews, guidelines, those creating computerized decision support systems and those involved in quality improvement. Such platforms – which we call an evidence ecosystem – can interact to create, disseminate and implement trustworthy research evidence in clinical practice.
Objectives: In this first part of our project to create an evidence ecosystem we developed a conceptual framework and demonstrated its feasibility and relevance through a real life example.
Methods: The framework for the Ecosystem uses a PICO (patient, intervention, comparator, outcome)-based shared health data model developed in collaboration with several partners - including Cochrane – that adheres to updated and internationally accepted standards and systems (e.g. GRADE) for trustworthiness. The data-model is implemented in a web-based authoring and publication platform (MAGICapp) used to create, disseminate and dynamically update evidence summaries, recommendations and decision aids. This is integrated with other innovative electronic platforms, e.g. Covidence for key steps in systematic reviews production, and EBMeDS for decision support systems. We applied the ecosystem to address the issue of overtreatment with surgery for meniscal tears.
Results: The figure illustrates the ecosystem conceptual framework. We will, at the Colloquium, demonstrate how such an ecosystem can facilitate the processing of high quality evidence from primary research (nationwide observational study and randomized trials of meniscectomies) into systematic reviews, guidelines and decision support tools, followed by quality performance measures and observational studies to document change in practice and outcomes, using overtreatment of meniscal tears as an example.
Conclusions: A living evidence ecosystem could improve diagnosis and treatment of patients.
Objectives: In this first part of our project to create an evidence ecosystem we developed a conceptual framework and demonstrated its feasibility and relevance through a real life example.
Methods: The framework for the Ecosystem uses a PICO (patient, intervention, comparator, outcome)-based shared health data model developed in collaboration with several partners - including Cochrane – that adheres to updated and internationally accepted standards and systems (e.g. GRADE) for trustworthiness. The data-model is implemented in a web-based authoring and publication platform (MAGICapp) used to create, disseminate and dynamically update evidence summaries, recommendations and decision aids. This is integrated with other innovative electronic platforms, e.g. Covidence for key steps in systematic reviews production, and EBMeDS for decision support systems. We applied the ecosystem to address the issue of overtreatment with surgery for meniscal tears.
Results: The figure illustrates the ecosystem conceptual framework. We will, at the Colloquium, demonstrate how such an ecosystem can facilitate the processing of high quality evidence from primary research (nationwide observational study and randomized trials of meniscectomies) into systematic reviews, guidelines and decision support tools, followed by quality performance measures and observational studies to document change in practice and outcomes, using overtreatment of meniscal tears as an example.
Conclusions: A living evidence ecosystem could improve diagnosis and treatment of patients.