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 (including defining explicit criteria for what belongs in each row), e.g.,
- Input Sources:
- Search Strategies:
- Output Repositories:
- Standards:
- Initiatives:
- Tools/Platforms:
- Other Best Practices:
Recommendation Tables for Knowledge Ecosystem Steps
Identify Studies
| General Recommendations | Anticoagulation | Testing/Triage | Other (Long COVID, Vaccine, Steroids) |
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| Input Sources | |
| | - 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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| 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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| Key Definitions and Frameworks | - [CPG on FHIR and BPM+ IGs do this separately - need to be combined and simplified (e.g., with helpful graphics); e.g., leverage L1-L4]
- [Approach section from CPG on FHIR IG might be helpful in framing scope]
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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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| Key Definitions and Frameworks | - Implement Guidance includes integrating the computable guidance into organizational information system infrastructure, and deploying the intervention to users, and maintaining the interventions over time. Includes looking at 'leading' indicators (e.g., process changes) regarding intervention use and results. (as opposed to the next "Analyze Care Results" table below that addresses 'lagging indicators' (e.g., clinical outcomes))
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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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| Key Definitions and Frameworks |
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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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| Current Standards | |
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