Under Construction

The COKA Project Management Group created an Evidence Based Medicine-on-FHIR (EBMonFHIR) for HL7 Sep2020 WGM PowerPoint. This PPT can also serve as an introduction to COKA efforts.   The PPT series includes a 5-step answer to “What do we need to do in early developmental stages?” with:

1. Agree to standard format for expression – schema/model of FHIR Resources

FHIR Resources at Maturity Level 1

1. Evidence
2. EvidenceVariable
3. Statistic (Datatype)
4. OrderedDistribution (Datatype)

FHIR Resources at Maturity Level 0

1. Citation
2. EvidenceReport

2.Agree to common terminologies – codes systems, value sets

We created a Code System Development Protocol

We are working now on creating 4 Code Systems:

• Study Design Code System to describe methodology characteristics of scientific observations such as randomized assignment, crossover allocation to cohorts, and qualitative analysis.
• Statistic Type Code System to describe the statistics reported such as mean, median, proportion, relative risk, absolute risk difference, p value, confidence interval, and I-squared measure of heterogeneity.
• Statistic Model Code System to describe the model characteristics such as linear regression, random-effects analysis, and tau estimation method.
• Risk Of Bias Code System to describe the concerns with methods or reporting of scientific observations such as comparator selection bias, gaps in blinding, and selective outcome reporting.

You can join any of the Expert Working Groups.

• Read the Invitation to join Expert Working Group
• Complete the Code System Development Intake Form

3. Develop tools for initial implementation – demonstrating that data exchange can work

Discussion of work being done by

• Content Citation and Classification Tools Development Work Group
• Evidence Evaluation and Reporting Tools Development Work Group
• Systematic Meta-Review Project Group

4. Adapting tools for early adopter real-world implementation

5. Develop an Implementation Guide.

These are often the “early stages” for standard development in HL7, but we are a little earlier because we are applying the standard to a community that (1) has not been functioning with computable expression for data exchange and (2) has not established universal standards for computable expression.   We are making great progress with what it takes for the earliest stages of introducing the computable layer to digital data expression.   We sincerely expect to get to #4 by the end of the calendar year.

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From Earlier Discussions:

This is a draft list of the types of data that could be helpful to make the knowledge supply chain more efficient by making this data computable in ways that support automated update notification and evidence/guidance processing in the knowledge supply chain. COKA teams are working on ways to communicate these data in reusable interoperable standard form. If this ACTS exploration identifies a specific, high priority need for a standardized of version one or more of these items, the COKA teams developing these standards for data exchange and code systems can explore addressing this need as a priority in their work. The interplay in this area between the COKA and ACTS work are discussed in the COVID-19 Knowledge Accelerator (COKA) Knowledge Ecosystem Liaison Work Group, which meets Wednesday 8A ET (see here for WG details). 

• Citations that a new article exists -- Citation Resource
• Study design classifiers - Citation.classifier and Evidence.studyType but we are working on the Study Design Code System
• Population and Intervention classifiers -- Group Resource (used for Populations to harmonize with CDS artifacts from CPGonFHIR) and EvidenceVariable Resource
• Outcome classifiers - EvidenceVariable Resource
• Risk of Bias assessments -- Evidence.certainty.certaintySubcomponent but we are working on the Risk of Bias Code System
• Study Results -- Evidence Resource
• Synthesis Results -- Evidence Resource
• Summary of Findings (synthesis results plus certainty judgments) -- Evidence Resource and EvidenceReport Resource

Earlier 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."