Korábbi változatok

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D.1: UNVETTED DRAFT Notes on a Near-term Approach for Propagating Down the Knowledge Supply Chain Notifications about Impactful Updates  

Goal:

Illustrate how for a sample target targets (anticoagulation and/or COVID-19 testing/triage) we can signal to care teams (through 'living' CDS interventions) when a change in the evidence-review-guidance supply chain content for this target indicates a change in recommended care. Or this change indicates that the strength of evidence/guidance supporting a recommendation has changed. (The latter is important so this new information can be factored into patient-clinician shared decision making accordingly.)

• As part of this work, lay foundations for improving the evidence/guidance supply chain timeliness, efficiency and effectiveness by making its components (e.g., studies, reviews, guidelines) computable through standardized terminologies.

Approach:

• Explore having UMN/VA /UMN teams serve as a core for addressing the goal; they each manage all facets of this supply chain and also consume its results via CDS interventions that support care for their patient populations.
  • Cultivate synergies between these 'full cycle' efforts and related Collaborative participant efforts - e.g., NACHC (testing/triage in health centers), ACEP/EvidenceCare COVID-19 Severity Classification/Triage/Disposition tool (see here), Australia Living COVID-19 Guidelines (anticoagulation), U Melbourne COVID-CARE and related efforts, etc.
  • Have teams responsible in each of these organizations for evidence surveillance/synthesis, guidance development/updating, and CDS development/updating/deployment collaborate among themselves and with other organizations in the Collaborative on this 'update notification' process and tooling.
• Consider ways to coordinate/advance current efforts:  SRDR/COKA, C19HCC Digital Guideline WG, COVID-NMA, COVID-END, AU Living Guidelines, and related efforts collaborate to produce triggering demonstrate notification system that suggests to living CDS owners/implementers that updates should be considered.
  • see below sampling below of sources that could be leveraged
• Document how evidence/guidance changes are detected and addressed in current VA/UMN/other processes
  • exploring enhancing these approaches to include a scalable notification function (applicable across approaches) that propagates supply chain updates to all pertinent stakeholders throughout the chain, including those responsible for developing/maintaining CDS interventions.  
• Phased enhancement approach
  • Synthesize a largely manual update detection and notification mechanism that runs throughout the supply chain
  • Begin automating portions of the manual process (e.g., leverage pilot 'web difff diff tool' to detect changes on target web pages)
  • Fully automate approach leveraging computable, standards-based, interoperable information throughout the supply chain (build on COKA/SRDR explorations - see notes below about types of data that can be coded to accomplish this)

Sampling of External Sources to Check for Updated Evidence/Guidance on Targets

• NIH anticoagulation adaptive clinical trials announced 9/20
• COVID-NMA (search for heparin)
• VA COVID Reviews (search for triage, testing, anticoagulation)
• L*VE Epistimonikos
• COVID END Best Evidence Synthesis (see testing/triage here, and others/anticoagulants here)
• NIH Guideline on Antithrombotic therapy
• CDC Phone Triage Guidance
• ACEP/EvidenceCare computable guidance on ED triage (based on this guide)
• AU Living Guidelines - see 'definition of disease severity' and 'VTE' [leaders of this effort are engaged in this exploration and they will send their processes (e.g., search tools/strategies)]
• IDSA Guideline on Serologic Testing (frequently updated)
• [other resources listed on Collaborative's evidence/guidance processing CoP page]
• Note from J ens Jens Jap, SRDR Team: "my team is interested in ... the development of automated literature searches to assist in SR updates or at least signal an opportunity for one. A preliminary step to this effort was the development of a RCT classifier. I think this is similar to what you previously referred to as COKA enhanced tagging tool, at least in nature. Using these kinds of machine learning assisted tools can bring us a step closer to more automation and living SRs. " Response from David Tovey: "In terms of an RCT Classifier, you may be interested to know that a tool with exactly this name has been developed by James Thomas and his team at UCL in London. It is currently in use within Cochrane but it might be useful to reach out to James if you are interested to explore this. ... The tool is capable of assessing large bundles of citation and abstracts very quickly with an accuracy level that is at least as good as could be achieved manually."

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