Ugrás a metaadatok végére
Ugrás a metaadatok elejére
Overview
- This page is a resource to help stakeholders working along the COVID-19 evidence-to guidance- to action - to data - to evidence cycle improve their work processes and results (see diagram at the top of this page). The ultimately goal is to a soon as possible, improve COVID-19 care delivery and outcomes.
- The tables below are designed to aggregate Collaborative participant recommendations for addressing steps in this COVID-19 knowledge ecosystem. The recommended tools and approaches are being gathered and will evolve over time as stakeholder input is received and community consensus around best practices - and the pandemic itself - evolve.
Recommendation Table Listing
- Identify Studies
- Synthesize Evidence
- Produce Guidance
- Make Guidance Computable
- Implement Guidance
- Analyze Care Results
- Leverage Results Analysis (e.g., for Quality Improvement, Reporting, Evidence Generation)
Process for Populating Recommendation Tables
- Identify 'leads' for each table who are currently doing extensive, collaborative work around the ecosystem step.
- Leads provide pointers to what their collaborative communities to be high value resources for table cells
- Other Collaborative participants likewise add comments and suggestions about this emerging information
- Formal processes/criteria will be developed by the Collaborative for adding/vetting information in the tables to optimize their value and use
Recommendation Tables for Knowledge Ecosystem Steps
Identify Studies
| General Recommendations | Anticoagulation | Testing/Triage | Other (Long COVID, Vaccine, Steroids) |
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| | - For steroids systematic meta-review, 8 sources have been identified these are (MEDLINE, CORD-19, L-OVE/Episteminokos, NIH iSearch COVID-19, EuropePMC, WHO COVID-19 Database, EMBASE + Prospero)
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| Search Strategies |
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| Output Repositories |
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| Current Standards |
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| Emerging Standards |
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| Initiatives | - Librarian Reserve Corps
- COVID-Evidence Project - building a database to gather all the evidence around a drug (University of Basil - currently focused on hydroxychloroquine, but model could be adaptable to other targets)
- Identification of sources to identify primary studies - validation study of specialized COVID-19 databases (systematic reviewers are going to different sources - this effort is to identify best practices)
- Advocate for librarian representation in searches and reviews - leverage skillsets/best practices in this work
[SLMC]: close gaps between needs that clinicians are seeing on the front line and topics covered in reviews guidelines
- COKA Evidence/ Tools WG [Project Google Drive](work in process)
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Synthesize Evidence
| General Recommendations | Anticoagulation | Testing/Triage | Other (Long COVID, Vaccine, Steroids) |
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| Other Best Practices | - COVID-NMA has initiated communication with all trialists to try to ensure consistent approaches e.g. selection of outcomes, reduction of risk of bias, and to invite them to contribute missing data.
- Framework slides: Applying Standards to the Evidence Domain (from the COKA Evidence Ecosystem Liaison WG)
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Produce Guidance
| General Recommendations | Anticoagulation | Testing/Triage | Other (Long COVID, Vaccine, Steroids) |
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| Input Sources | - See Output Repositories from Synthesize Evidence table
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Make Guidance Computable
| General Recommendations | Anticoagulation | Testing/Triage | Other (Long COVID, Vaccine, Steroids) |
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| Input Sources | - See output repositories under Produce Guidance
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| Other Best Practices | From C19HCC Digital Guideline WG: - Using CPG-on-FHIR standard for representing/ expressing the full intent of the Guidance in computer-interpretable artifacts (part of HL7 CPG-IG)
- Using the Agile Approach to CPG Development (inclusive of Integrated Process) to concurrently Produce Guidance and Make Guidance Computable (part of HL7 CPG-IG)
- Use Agile Knowledge Engineering methods, principles, and tools
- Cross-functional Integrated team (Agile CPG Team)
- Leverage composite nature of CPGs (e.g. can develop logic for inferences on patient information- CPG_CaseFeatures) to build incrementally and iteratively with rapid feedback
- Pull knowledge engineers into Content design/reviews; pull domain SMEs into knowledge representation design/reviews
- Leverage CPG-on-FHIR as a faithful expression of Guidance and its ability to create computationally derived CDS and Cognitive Support, patient-specific, practice-level digital Quality Measures/Metrics, eCaseReports, etc. to create computable artifacts used downstream in the Learning Health System and to provide closed-loop feedback/feedforward.
- Leverage established tools and capabilities (e.g. BPM+ process and tooling, Clinical Ontology) to author computable Guidance and Open Source tooling to translated into HL7 CPG-on-FHIR to leverage derivative and native compute
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- Use established standards and work with standards community (to understand and evolve as needed)
- Engage consumers/ users early and often
- Engage downstream vendors (e.g Terminology vendor USED in the EHRs) early
- Just because everyone everyone is using the same terminology systems doesn't mean they're agreeing how to use the actual terms- this needs to be considered and addressed to make ecosystem/supply chain work properly (feedforward from Evidence, but also feedback of data semantics back into evidence)
- Learn from related communities of practice (e.g. Agile Software Engineering)
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Implement Guidance (e.g., as CDS, eCQMs)
| General Recommendations | Anticoagulation | Testing/Triage | Other (Long COVID, Vaccine, Steroids) |
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| Input Sources | - See output repositories for Make Guidance Computable
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| Initiatives | - C19 Digital Guidelines WG developing and implementation guide for COVID-19 interventions
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Analyze Care Results
| General Recommendations | Anticoagulation | Testing/Triage | Other (Long COVID, Vaccine, Steroids) |
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Leverage Results Analysis (e.g., for Quality Improvement, Reporting, and Evidence Generation)
| General Recommendations | Anticoagulation | Testing/Triage | Other (Long COVID, Vaccine, Steroids) |
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