Background: Evidence-based medicine (EBM) has the potential to improve health outcomes, but EBM has not been widely integrated into the systems used for research or clinical decision-making. There has not been a scalable and reusable computer-readable standard for distributing research results and synthesized evidence among creators, implementers, and the ultimate users of that evidence. Evidence that is more rapidly updated, synthesized, disseminated, and implemented would improve both the delivery of EBM and evidence-based health care policy.
View Article and Find Full Text PDFObjective: Patients and clinicians rarely experience healthcare decisions as snapshots in time, but clinical decision support (CDS) systems often represent decisions as snapshots. This scoping review systematically maps challenges and facilitators to longitudinal CDS that are applied at two or more timepoints for the same decision made by the same patient or clinician.
Methods: We searched Embase, PubMed, and Medline databases for articles describing development, validation, or implementation of patient- or clinician-facing longitudinal CDS.
Inputs And Outputs: The Strike-a-Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria.
Purpose: Ultimately, such a system will play a "matchmaker" role in facilitating point-of-care recognition of patient-specific clinical decision support.
Background: Various formalisms have been developed to represent clinical practice guideline recommendations in a computer-interpretable way. However, none of the existing formalisms leverage the structured and computable information that emerge from the evidence-based guideline development process. Thus, we here propose a FHIR-based format that uses computer-interpretable representations of the knowledge artifacts that emerge during the process of evidence-based guideline development to directly serve as the basis of evidence-based recommendations.
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January 2023
Science advances at a slow pace but can be accelerated with a standard for computable expression of scientific knowledge, more precisely a technical standard for electronic data exchange of machine-interpretable data expressing scientific knowledge. Efforts to achieve this vision include Evidence-Based Medicine on Fast Healthcare Interoperability Resources (EBMonFHIR), COVID-19 Knowledge Accelerator (COKA), Computable Publishing LLC, Scientific Knowledge Accelerator Foundation, and the Fast Evidence Interoperability Resources (FEvIR) Platform. The vision for communicating scientific research results to be instantly found, viewed, sent, received, and incorporated into thousands of system is a Just-in-time Evidence Dissemination and Integration (JEDI) vision.
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