ACTS COVID-19 Collaborative Learning Community

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changes.mady.by.user Jerry Osheroff

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changes.mady.by.user Jerry Osheroff

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Illustrate how for a sample target (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.)

Approach:

  • Explore having VA and UMN /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. Explore having them serve as a core for addressing the goal.
    • 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 mutually beneficial ways to apply coordinate/advance current efforts:  SRDR/COKA, C19HCC Digital Guideline WG, COVID-NMA, COVID-END, AU Living Guidelines, and related efforts to produce a a triggering system that suggests to living CDS owners/implementers that updates should be considered.
    • see sampling below that could be leveraged
  • Start by documenting Document how evidence/guidance changes are detected and addressed in current VA/UMN/other processes, and
    • exploring enhancing these approaches to include a scalable notification function 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, then semi-automated approaches
    • Begin automating portions of the manual process (e.g., that leverage pilot 'web difff tool' to detect changes on target web pages), and ultimately fully automated approaches that leverage computable
    • Fully automate approach leveraging computable, standards-based, interoperable information throughout the supply chain .
  • Important foundational work for this has been laid in SRDR/COKA collaborations, and we should explore how to fully leverage and build on this in developing an 'update notification' approach. Especially since SRDR is an important component of AHRQ's current digital knowledge platform.
  • Note from Jens
    • (build on COKA/SRDR explorations)


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

  • Note from 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."

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' 
  • IDSA Guideline on Serologic Testing (frequently updated)
  • [other resources listed on Collaborative's evidence/guidance processing CoP page]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."


D.2: Notes on a More Comprehensive Proof of Concept Software Toolset 

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