Korábbi változatok

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• The figure in section A. is an overview of potential enhanced information flow around the Evidence/Quality Ecosystem Cycle - i.e., driven by more computable and standards-based evidence and guidance
• The table in section B. outlines ecosystem enhancement needs and opportunities, as well as the notes on a potential concept demo toolkit (in the 4th table column) for addressing those needs 
• The diagram in section C. illustrates where and how a concept demo toolkit that makes evidence and guidance more computable and standards-based could enhance the Evidence/Quality Ecosystem (and Learning Health System) Cycle
• The outline in section D. provides more details on a potential concept demo toolkit
• The email excerpt in section DE. provides considerations related to using computable/standards-based evidence descriptions to make developing and updating computable clinical recommendations more efficient and effective. 

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C. Overview Diagram for Proof of Concept Demo Toolkit (computable evidence slide.pptx)

D. More Details on Proof of Concept Toolkit Software tool development 

The 4 proof of concept tools and related repository outlined below can be placed on an open source developmental website for public dissemination. Content development for these tools will be driven by Collaborative participant current efforts, e.g., focused on COVID-19 testing/triage in ambulatory and ED settings and anticoagulation in inpatient settings.

Tool 1: Create/Store/Access Computable Study Results Representation   

• Develop tools that enable users (e.g., via web interfaces) and systems (e.g., via APIs) to input to/output from a proof of concept repository. The repository will be an AHRQ development website created for purposes of the concept demo. This tool will enter/store/retrieve standards-based, computable data about a study related to COVID-19 ambulatory triage/testing. Input sources can include medRxiv, journals, and article repositories, e.g., CORD-19. The data input tool user could be a study author/publisher, or someone else (e.g., in an EPC) for studies already published. Data output tool use could include systematic review developers and other individuals and applications requesting the study results. These proof of concept capabilities will be designed to align with SRDR+ functionality enhancements under consideration/development. 
  • This proof of concept tool demonstrates an open mechanism for capturing and presenting (e.g., via web-based viewer or API) summary results of individual studies in a manner that is re-usable by many different systems. This reduces redundant work currently required to extract key study variables that aren’t standardized and therefore aren’t interoperable or re-usable. 
  • The tool will also demonstrate linkages with clinicaltrials.gov, e.g., for visibility into what trials on the topic of interest (COVID-19 testing/triage) are underway and completed.
  • It also demonstrates a ‘notification feature’ that notifies ‘subscribers’ to the repository (e.g., systematic reviewers) that a new study pertinent to the topic of interest (COVID-19 ambulatory triage/testing) is entered. 

Tool 2: Create/Store/Access Computable Systematic Review Representation

• Develop tools that enable users (e.g., via web interfaces) and systems (e.g., via APIs) to input to/output from a repository (i.e., an AHRQ development website created for proof of concept purposes) systematic review information related to COVID-19 ambulatory triage/testing in a computable, standardized format.
• Includes notification tool/function to notify ‘subscribers’ (e.g., guideline developers) that there’s a new systematic review on the topic of interest.

Tool 3: Create/Store/Access Computable Rationale for Guidance

• Develop a proof of concept web interface and an API that use the FHIR standard for data exchange (i.e., EvidenceReport and PlanDefinition Resources maintained by the COKA/EBM on FHIR/CPG on FHIR HL7 projects) to encode a recommendation's evidence basis, i.e., pertinent studies and systematic reviews encoded by tools 1 (computable study results) and 2 (computable systematic reviews) above. This computable guidance rationale tool will also encode the rationale and confidence for the recommendation.  
• This enhanced, automated evidence processing for recommendations will be demonstrated to support data entry into the 'Evidence Supporting Recommendation' section of the C19HCC knowledge elicitation tool (see excerpt in diagram below). 
• Two functions that will be demonstrated are support for: 1) gathering and synthesizing the evolving evidence base pertinent to a recommendation and 2) assigning standard codes for the rationale behind and confidence in the recommendation.

Excerpt from Knowledge DRAFT Elicitation Tool:

Image Added

[intervening portions omitted]

• Likewise, the proof concept tool will also demonstrate web-based connection to other guideline development/presentation software to facilitate use of standards-based, computable evidence in guideline development. For example, MAGICapp, used in the Australia National COVID-19 Living Guidelines initiative - see disease severity, section 4, pertinent to triage.
• The tool will contain a feature that notifies CDS and guideline developers/implementers that new systematic reviews on the topic of interest (testing/triage) is available.
• This tool will use EBM on FHIR and CPG on FHIR: leads for these efforts are closely engaged in the ACTS COVID Collaborative and would be involved in executing work outlined in this proposal (see project team above).
• Outputs from the use of the C19HCC knowledge elicitation tool are provided to the C19HCC Agile Knowledge Engineering teams, who create L3 guideline representations (i.e., in CQL and BPMN) and CDS/eCQMs/eCaseReports that are deployed in clinical settings.
• The resulting CDS interventions will be placed in CDS Connect; explore synergies between living computable evidence/ guidance as demonstrated in the proof of concept toolkit and the CDS Connect Authoring Tool. 

Tool 4: Identify/Store/Access Terminology for Computable Recommendation Definition

• Develop a proof of concept tool that enables guideline and CDS developers to identify/gather (e.g., via web interfaces, APIs) appropriate standard coded concepts and/or value sets for the required data elements needed for guideline execution (to address testing/triage). 
• For example, exposures, symptoms, high risk conditions, and special circumstances listed in the CDC Phone Line Advice triage algorithm (see page 6) that underlies part of the NACHC CDS intervention. 
• Proof of concept tool addresses challenges that knowledge engineers currently face in identifying code sets and value sets needed to make concepts and data elements in clinical guidelines computable. In a leading effort to put the CPG on FHIR implementation guide for computable guidance into practice, members of the C19HCC Digital Guideline WG are using a knowledge elicitation tool (see figure below) to elicit these concepts/terms from SMEs and then manually searching code sets/value sets to obtain codes needed to make recommendations computable. This part of the concept demo toolkit provides an automated lookup function that maps concepts/terms to potentially matching items from pertinent value/code sets. These codes/value are needed to access pertinent data from EHRs to execute the recommendation. 
• For example, the new tool leverages the data dictionary being developed under the NACHC project to help computable guidance developers search code set repositories (e.g., from the COVID-19 Interoperability Alliance and others) to express guideline-related clinical data elements (e.g., body temperature) using specific, standard code sets (e.g., LOINC code, ICD-10, SNOMED, etc.).

Excerpts from Knowledge DRAFT Elicitation Tool: 

E.  Excerpts from 9/4/20 email exchange about using computable/standards-based evidence descriptions to make developing and updating computable, evidence-based clinical recommendations more efficient/effective.

Adapted version of note from Jerry Osheroff:

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