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"Making Science Computable"

Help Us Overcome COVID-19 Impact at the Speed of Thought

To accelerate identifying, processing, and disseminating knowledge about COVID-19, we develop standards and support tools for computable expression of evidence and guidance

To accelerate our knowledge about COVID-19 we need to share what we learn and coordinate among many working groups 



Get Started With COKA

Google Drive folder for COKA

Click on the images below to be redirected to your page of interest!

Weekly Meeting Schedule 

Day

Time (Eastern)

Team


Monday 

8-9 am 

Project Management Working Group


Monday 

9-10 am

Usability Research Project Working Group


Monday

1-2 pm

Eligibility Criteria Working Group


Tuesday

11am-12 pm

Common Metadata Framework Working Group


Tuesday

2-3 pm 

Research Design Working Group


Wednesday

8-9 am 

Knowledge Ecosystem Liaison Working Group


Wednesday

9-10 am

Statistic Standard and Terminology Working Group


Thursday

 9-10 am

Computable EBM Tools Development Working Group


Thursday

10-11 am

EBM Decision Map Working Group


Thursday

4-5 pm

Project Management Working Group


Friday

9-10 am

Risk of Bias Terminology and Tooling Working Group


Friday

10-11 am

Communications (Awareness, Scholarly Publications) WG






You are welcome to join us!

Please email balper@computablepublishing.com with any questions.

For all of the above meetings: Join Microsoft Teams Meeting

+1 929-346-7156   United States, New York City (Toll)

Conference ID: 324 918 025#

Local numbers

Meeting support by Computable Publishing LLC

Agenda for the coming week:

Monday, May 9:

  • 8 am Eastern: Project Management – review progress on FHIR Trackers and FEvIR Platform refactoring to match current FHIR specification
  • 9 am Eastern: Usability Research Working Group – continue to develop instructions for the Risk of Bias Assessment Tool (RoBAT)
  • 1 pm Eastern: Terminology and Ontology Working Group – terminology-related technical developments for the Common Metadata Framework (the ‘SchemaElement Resource’)

 

Tuesday, May 10:

  • 9 am Eastern: Scientific Knowledge Accelerator Foundation Board of Directors
  • 11 am Eastern: Common Metadata Framework Working Group – review progress of adding Mapping to ‘SchemaElement Resources’ to support “Specifying Metadata to Help Mobilize Computable Biomedical Knowledge”; attention to mapping RIS to FHIR Citation
  • 2 pm Eastern: Research Design Working Group – continue review of related terms for the Scientific Evidence Code System


Wednesday, May 11: (typical COKA meetings canceled due to HL7 Work Group Meeting [WGM])

  • 9 am Eastern: HL7 WGM BRR Q1 – EBMonFHIR and Eligibility Criteria Connectathon Track Report Out
  • 11 am Eastern: HL7 WGM BRR Q2 – ResearchStudy and ClinicalTrials.gov efforts
  • 12:30 pm Eastern: HL7 WGM Sponsored Session - Converting Structured Data to FHIR


Thursday, May 12:

  • 9 am Eastern: Computable EBM Tools Development Working Group – review progress on tools development (EvidenceVariable Builder) and mapping Evidence and EvidenceVariable expressions for meta-analysis results (the measured variable)
  • 10 am Eastern: EBM Decision Map Profile Working Group (HL7 CDS EBM sub-WG) – development of EvidenceVariable Resource and related Profiles
  • 4 pm Eastern: Project Management – prepare weekly agenda


Friday, May 13:

  • 9 am Eastern: Risk of Bias Terminology and Tooling Working Group – continue review and addition of terms and definitions to the Scientific Evidence Code System
  • 10 am Eastern: Communications Working Group – review progress on publications, presentations, and website
  • 11 am Eastern: HL7 WGM CDS Q2 – EBMonFHIR session



 


Participation Opportunities

See an Example of a JSON FHIR Resource


Working Groups 

Click on the images below to be redirected to your page of interest!



HL7 FHIR Connectathon Information

The Next Connectathon will be held in person in Baltimore, Maryland USA Sept 2022:

Past HL7 Connectathons:

Connectathon 30 2022-05 Eligibility Criteria (Evidence-Based Medicine/EBMonFHIR) Track. Track Leads: Dr. Brian Alper, Gustav Vella and Khalid Shahin

 Eligibility Criteria specification with EvidenceVariable project.

Connectathon 29 2022-01 Evidence Based Medicine (COVID-19 Knowledge Accelerator) (January 11-12, 2022).

Connectathon 28 Sept 13-15,2021 2021-09 Evidence Based Medicine (COVID-19 Knowledge Accelerator)

Connectathon 27 2021-05-17 Evidence Based Medicine (COVID-19 Knowledge Accelerator)

Working Group Specific Information

SEVCO Progress Update and All-Group Discussion: Thank you to those who joined us Thursday April 14 to learn the latest progress for the Scientific Evidence Code System development and have an open discussion (‘town hall meeting’). You are helping us define science (helping us know what we are talking about) as we make science computable. 

The Town Hall Meeting included a presentation by Joanne Dehnbostel and open discussion with Brian Alper.  The PowerPoint can be viewed here and the meeting recording can be viewed here.

COKA Weekly Update



Computable Evidence and Guidance Framework Updates:

 

The EBM Decision Map Profile Working Group is considering an Implementation Guide for an ‘Evidence And Guidance Linked Expression’ (EAGLE) view of a Recommendation Justification.


We re-modeled the approach to a “Recommendation” Profile in FHIR with several considerations including:

  1. The guideline development community often uses the “Evidence-to-Decision Framework” to describe the multiple factors or determinants in making a recommendation (i.e., making the decision for what to recommend), and documenting the deliberation (recording the judgments made regarding each of these factors) is the data best represented in ArtifactAssessment Resource structure.   The PlanDefinition Resource structure is established for the description of what is recommended in a form more suitable for implementation (interoperable interaction with health records data).
  2. The shared decision making community often focuses on a process to facilitate the individual’s understanding and autonomy regarding factors or determinants (like those noted above) to make a personal decision. The data structure for “sharing the evidence including evidence for all factors or determinants” can be similar to #1.
  3. Whether referring to the “Recommendation” or the “Decision”, the core concept is similar. Whether referring to it for a target population or target individual, the core concept is similar. The core concept we are trying to model is the coordination of the end-result (“Recommendation” or “Decision”), the judgments or justifications for the related factors or determinants, and the expression of variables defining the “what” and “who” the recommendation or decision is about.



New FHIR Profiles in consideration include:


  • Profile of PlanDefinition:
    • RecommendationPlan – description of a recommended intervention in a guideline
  • Profile of ActivityDefinition:
    • RecommendationAction – description of an action or activity that is part of a recommended intervention in a guideline
  • Profiles of EvidenceVariable:
    • RecommendationEligibilityCriteria – description of the population for which the recommendation applies
    • StudyEligibilityCriteria – description of the patient group for which evidence is generated; description of the inclusion and exclusion criteria for a study population
    • InterventionDefinition – definition of an intervention or comparator (I or C in PICO)
    • OutcomeDefinition – definition of the outcome used as the observed or intended measured variable for a single study regarding a clinical (PICO) question, or the intended measured variable for an evidence synthesis
    • EvidenceDataset – definition of the outcome used as the observed measured variable for an evidence synthesis; each observation of an analysis result is defined by reference to an Evidence, so the collection of observations (combination of characteristics) is the dataset
    • NetEffect – description of the combination of outcomes considered for the net effect estimation
  • Profiles of Evidence:
    • OutcomeEvidenceSynthesis – evidence statistics generated from a meta-analysis or systematic review regarding a clinical (PICO) question
    • StudyOutcomeEvidence – evidence statistics generated from a single study regarding a clinical (PICO) question
    • NetEffectEstimate – the net effect estimate, a quantified estimate of the balance of benefits and harms
  • Profiles of Group:
    • StudyGroup – the actual group observed in a study, may include quantity and time period
    • StudyGroupGroup – the actual group of Groups observed in an evidence synthesis, wherein each Group is a StudyGroup
  • Profiles of ArtifactAssessment:
    • RecommendationJustification – rating of the recommendation and evidence-to-guidance framework judgments
    • CertaintyOfEvidence – rating of the evidence quality
  • Profiles of Citation:
    • StudyCitation – citation of a study or systematic review
    • RecommendationCitation – citation of an individual recommendation
    • GuidelineCitation – citation of a guideline
  • Profiles of Composition:
    • EvidenceReport
  • Profile of ImplementationGuide
    • ClinicalPracticeGuideline

 


An example created to demonstrate an “EBM Decision Map” includes:


The Computable EBM Tools Development Working Group continued to revise the Evidence Resource (Mean difference in HbA1c effect of bariatric surgery in 2016 meta-analysis) and included:


  1. A Population variableDefinition with:
    1. A description (Patients with type 2 diabetes)
    2. An observed population with a Group Resource (Study samples included in Mean difference in HbA1c effect of bariatric surgery in 2016 meta-analysis)
    3. An intended population with a Group Resource (Type 2 diabetes and elevated BMI in 2016 meta-analysis)
  2. An Exposure variableDefinition with an EvidenceVariable Resource of Bariatric Surgery (RYGB, VSG, LAGB, BPD))
  3. A Reference Exposure variableDefinition with:
    1. A description (Medical/lifestyle interventions)
    2. A note (As the control group (observed reference exposure) was defined differently in each trial, the intended reference exposure is described as the absence of the intended exposure (Bariatric Surgery).)
    3. An intended evidence variable with an Evidence Variable Resource (NOT Bariatric Surgery (RYGB, VSG, LAGB, BPD))
  4. An Outcome variableDefinition with:
    1. A description (Mean difference in HbA1c between Surgery and Medical/Lifestyle groups at end of follow-up for HbA1c end point)
    2. An observed evidence variable (TBD) to represent the “dataset” of Evidence Resources used in the meta-analysis
    3. An intended evidence variable with an Evidence Variable Resource (Mean difference in HbA1c between Surgery and Medical/Lifestyle groups at end of follow-up)


We also added an additional Citation (2022 Systematic Review of bariatric surgery mortality effect 35243488) to the Bariatric Surgery Decision Example of FHIR Resources Project.


Immediate next attention will focus on improvements to the EvidenceVariable Resource StructureDefinition and materials to describe its use in an Implementation Guide.

The HL7 Biomedical Research & Regulation (BRR) Work Group meeting (with 18 participants) was the Kickoff discussion for the Eligibility Criteria (Evidence-Based Medicine/EBMonFHIR) Track for the 30th HL7 FHIR Connectathon.  A recording can be viewed here.


In the Connectathon we created 2 FHIR change requests to improve the EvidenceVariable Resource:



 

Common Metadata Framework Updates:

 

The Common Metadata Framework Working Group has advanced a project for Specifying metadata to help mobilizing computable biomedical knowledge.

  1. A working group within the Mobilizing Computable Biomedical Knowledge (MCBK) Standards Working Group completed a year of effort and published Categorizing metadata to help mobilize computable biomedical knowledge which identified 13 metadata categories to communicate the Findability, Accessibility, Interoperability, Reusability, and Trustability (FAIR+T) of knowledge artifacts.
  2. The COKA Common Metadata Framework Working Group started February 2, 2021 and included some of the MCBK authors and some of the COKA participants.
  3. We spent 4 months mapping FHIR Resource StructureDefinitions to FAIR+T principles and the 13 metadata categories to inform initial thinking about a common metadata framework.
  4. We changed our approach in June 2021 and spent 5 months specifying elements for each of the 13 metadata categories (for a total of 134 elements).  The details of this “first draft” of “Specifying metadata to help mobilize computable biomedical knowledge” can be found at Specifying metadata to MCBK Spreadsheet.
  5. Through February 2022 we mapped the ‘Specifying metadata to MCBK’ concepts to:
    1. an extensive mapping (crosswalk) across 17 metadata schemas found at Ojsteršek. (2021). Crosswalk of most used metadata schemes and guidelines for metadata interoperability (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4420116 -- we created a “column T” to map the “COKA MCBK Common Metadata Framework” (Specifying metadata to MCBK Spreadsheet) to this Crosswalk – details in 772 rows at https://docs.google.com/spreadsheets/d/1uls9P7pRnj10tBLnEB7I7JEqxdPanZ51/edit#gid=434554867
    2. 52 rows in a crosswalk of 20 metadata schemes for research data: https://docs.google.com/spreadsheets/d/1eYvvYWsxjMrBsh0dDwja_0LLzhx0Ijzj/#gid=1989930257
  6. We reached out to 2 additional cross-cutting efforts to address common metadata for wide-scale sharing of research data: the Common Fund Data Ecosystem (CFDE) and JATS for Reuse (JATS4R).
  7. A project page for the Common Metadata Framework project is now at https://fevir.net/resources/Project/29201
  8. On March 8, 2022 we started drafting an article for “Specifying Metadata to Help Mobilize Computable Biomedical Knowledge” with a key Research Question of “Can a common framework for specification of metadata map to common metadata schemes used to represent CBK?”.
  9. The Terminology and Ontology Working Group developed a structure (SchemaElement Resource) to describe an element in any schema and how it can map to elements in other schema.  The basic structure is described on the Common Metadata Framework project page.
  10. We created 3,927 SchemaElement Resource instances by mapping 16 FHIR Datatypes (Address, Annotation, Attachment, CodeableConcept, Coding, ContactDetail, ContactPoint, HumanName, Identifier, Period, Quantity, Range, Reference, RelatedArtifact, SimpleQuantity, UsageContext) and 9 FHIR Resources (Citation, Evidence, EvidenceReport, EvidenceVariable, Group, Organization, Practitioner, PractitionerRole, ResearchStudy) in the 5.0.0-snapshot1 version of FHIR.
  11. We created 4,154 SchemaElement Resource instances by mapping 17 FHIR Datatypes (Address, Annotation, Attachment, CodeableConcept, CodeableReference, Coding, ContactDetail, ContactPoint, HumanName, Identifier, Period, Quantity, Range, Reference, RelatedArtifact, SimpleQuantity, UsageContext) and 10 FHIR Resources (ArtifactAssessment, Citation, Evidence, EvidenceReport, EvidenceVariable, Group, Organization, Practitioner, PractitionerRole, ResearchStudy) in the Build version of FHIR.
  12. We identified 238 SchemaElement Resource instances in the 5.0.0-snapshot1 version of FHIR that are not matched in the Build version of FHIR, and added mapping data to suggest how to convert data from the 5.0.0-snapshot1 version of FHIR to the Build version of FHIR.
  1. We created 509 SchemaElement Resource instances to represent the ClinicalTrials.gov schema as used in 2021.
    1. We created 400+ map element instances in 300+ of these SchemaElement Resources to document mapping the ClinicalTrials.gov schema to the current FHIR Build specification.
    2. Automated reporting function to share this mapping as a spreadsheet is in progress.
  2. We created 40 SchemaElement Resource instances to represent the Research Information Systems (RIS) schema.
    1. We created map element instances in 15 of these SchemaElement Resources to document mapping the RIS schema to the current FHIR Build specification.
  3. Next developments:
    1. Document (as map elements in SchemaElement Resources) the process to convert data from the RIS schema to the FHIR Build Citation schema.
    2. Create SchemaElement Resource instances to represent the Common Metadata Framework.
    3. Convert the Crosswalks to SchemaElement Resources.


 

Code System Development Updates:


  • All are welcome to Help Shape the Scientific Evidence Code System (SEVCO) – an open effort to define terms for the expression of study design, statistics, and risk of bias used across the communication of science. We are following a Scientific Evidence Code System Development Protocol.
  • The SEVCO Progress Update and All-Group Discussion included a presentation by Joanne Dehnbostel and open discussion with Brian Alper on April 14, 2022.  The PowerPoint can be viewed here and the meeting recording can be viewed here.
  • To join the Scientific Evidence Code System Expert Working Group to vote on the terms and definitions in this code system, just go to the Scientific Evidence Code System (SEVCO) Project Page and click the Join the Group button. There are currently 35 people in the Scientific Evidence Code System Expert Working Group.
  • The code system (as it is developed) can be viewed at Scientific Evidence Code System (SEVCO) -- DRAFT ONLY (Not published for current use), and anyone is welcome to comment on any term by finding the term in the Term Detail view and clicking the Comment button.
  • Members of the Expert Working Group can vote on any term that is open for voting in the Term Detail view and clicking the Vote button, or by viewing all terms open for vote at Computable Publishing®: My Ballot.
  • Current progress on term development includes:
    • 53 of 58 (91%) Study Design terms approved
    • 104 of 239 (44%) Bias terms approved
    • 0 of 26 Rating of Bias terms approved
    • 34 of 137 (25%) Statistic terms approved
    • 0 of 98 Statistical Model terms approved
    • 191 of 558 (34%) TOTAL terms approved
  • Terms that are currently open for voting include:


Study Design-related terms:

Term

Definition

Alternative Terms

Comment for application

Multisite data collection

A study design process in which data are collected from two or more geographic locations.


For studies conducted across multiple contexts (administrative or logistical) that are distinct from geographic locations, potentially introducing greater variability beyond multisite data collection, use the term Multicentric.

Clinical outcome

A study design feature in which one or more outcomes are direct measures of quantity or quality of life.

  • Patient-oriented outcome
  • Patient-important outcome
  • Patient-relevant outcome
  • Patient-centered outcome
  • Includes clinical outcomes

In healthcare research, outcomes are effects on patients or populations, including changes to health status, behavior, or knowledge as well as patient satisfaction and quality of life. A clinical outcome qualifies the type of outcome as that which patients directly care about, i.e. outcomes that are directly related to patients' experience of their life. Examples of clinical outcomes include mortality, morbidity, symptoms, and quality of life.

Surrogate outcome

A study design feature in which one or more outcomes are indirect measures of quantity or quality of life, presumed or believed to have an effect on clinical outcomes.

  • Disease-oriented outcome

In healthcare research, outcomes are effects on patients or populations, including changes to health status, behavior, or knowledge as well as patient satisfaction and quality of life. A clinical outcome qualifies the type of outcome as that which patients directly care about. Examples of clinical outcomes include mortality, morbidity, symptoms, and quality of life. A surrogate outcome qualifies the type of outcome as that which patients do not directly care about. Examples of surrogate outcomes include laboratory test measurements, imaging study findings, and calculated risk estimates.


Bias-related terms:

Term

Definition

Alternative Terms

Comment for application

Bias related to selection of the data for analysis

An analysis bias due to inappropriate choice of data included in the analysis before the analysis is applied.


An analysis selection after the analysis is applied would be considered a Selective Analysis Reporting Bias.

Bias due to post-baseline factors influencing selection of the data for analysis

A bias related to selection of the data analysis based on participant characteristics observed after study enrollment.



Inadequate intention-to-treat analysis

A bias related to selection of the data analysis in which data are not completely analyzed according to the original assignment to comparison groups in an interventional study.

  • Inadequate as-randomized analysis


Inadequate per-protocol analysis

A bias related to selection of the data analysis in which data are not completely analyzed according to the assignment to comparison groups according to the interventions received.

  • Inadequate as-treated analysis

Bias related to selection of the variables for analysis

An analysis bias due to inappropriate choice of variables included in the analysis before the analysis is applied.


An analysis selection after the analysis is applied would be considered a Selective Analysis Reporting Bias.

Bias related to selection of the variables for adjustment for confounding

An analysis bias due to inappropriate choice of the variables for adjustment for confounding before the analysis is applied.


An analysis selection after the analysis is applied would be considered a Selective Analysis Reporting Bias.

Bias controlling for time-varying confounding

A bias related to selection of the variables for adjustment for confounding in which the confounding is time-dependent.


An analysis selection after the analysis is applied would be considered a Selective Analysis Reporting Bias.

Inadequate adherence effect analysis

A bias related to selection of the variables for adjustment for confounding by adherence.

  • Bias controlling for adherence effect
  • Bias controlling for confounding by adherence

An analysis selection after the analysis is applied would be considered a Selective Analysis Reporting Bias.

Bias related to selection of the statistical model

An analysis bias due to inappropriate choice of the analytic model before the analysis is applied.

  • Bias related to selection of the analytic framework



Statistic-related terms:

Term

Definition

Alternative Terms

Comment for application

Rate

A ratio in which the numerator represents any quantity and the denominator represents an interval of time.


When the numerator represents a count, the rate is an Event Rate.

Incidence Rate

A rate in which the numerator represents an incidence and the denominator represents an interval of time.

  • Incidence density
  • Average hazard rate

Incidence is defined as a proportion in which the numerator represents new events. Rate is defined as a ratio in which the numerator represents any quantity and the denominator represents an interval of time. The interval of time used for the denominator may be data-dependent when the duration of observation varies across the observations.


In the method for calculating incidence rate (described at https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section2.html), the numerator is the "Number of new cases of disease or injury during the specified period" and the denominator is the "Time each person was observed, totaled for all persons"

Hazard Rate

A conditional instantaneous rate in which the numerator represents an incidence conditioned on survival to a specified time, and the denominator represents a time interval with a duration approaching zero.

  • Hazard
  • Hazard Function
  • Instantaneous hazard rate

In the definition of Hazard Rate, the term "survival" is not literally about life and death but is used to represent existence without experiencing the event. "Hazard" as a statistical term is not specific to "bad" or "dangerous" events.

A hazard rate is expressed as a unitless numerator per unit of time, occurring at a specified time, and conditioned on survival to that time.

A hazard rate is mathematically the negative derivative of the log of the survival function. The survival function is the probability of surviving past a specified point in time, expressed as Pr{ T >= t }.

A hazard rate is also mathematically defined as lim(dt -> 0) [ Pr{ ( t <= T < t + dt ) | ( T >= t ) } / dt ].

Event Rate

The number of occurrences per unit of time.


An event rate is a ratio in which the numerator represents a count and the denominator represents an interval of time.

When the numerator represents a count:

--If the denominator includes an interval of time, the type of ratio is an Event Rate.

--If the denominator includes a count without an interval of time, the type of ratio is an Event Frequency.

--If the denominator includes a count and an interval of time, the type of ratio is an Event Frequency Rate.

--If the denominator includes an interval of space, the type of ratio is a Number Density

Event Frequency Rate

A ratio in which the numerator represents an event frequency and the denominator represents an interval of time.


When the numerator represents a count:

--If the denominator includes an interval of time, the type of ratio is an Event Rate.

--If the denominator includes a count without an interval of time, the type of ratio is an Event Frequency.

--If the denominator includes a count and an interval of time, the type of ratio is an Event Frequency Rate.

--If the denominator includes an interval of space, the type of ratio is a Number Density

Event Frequency

A ratio in which the numerator represents a count and the denominator represents a count (without involving an interval of time).

  • Frequentist Probability

When the numerator represents a count:

--If the denominator includes an interval of time, the type of ratio is an Event Rate.

--If the denominator includes a count without an interval of time, the type of ratio is an Event Frequency.

--If the denominator includes a count and an interval of time, the type of ratio is an Event Frequency Rate.

--If the denominator includes an interval of space, the type of ratio is a Number Density

Number Density

A ratio in which the numerator represents a count and the denominator represents an interval of space (distance, area, or volume).


When the numerator represents a count:

--If the denominator includes an interval of time, the type of ratio is an Event Rate.

--If the denominator includes a count without an interval of time, the type of ratio is an Event Frequency.

--If the denominator includes a count and an interval of time, the type of ratio is an Event Frequency Rate.

--If the denominator includes an interval of space, the type of ratio is a Number Density


To participate you can join the Scientific Evidence Code System Expert Working Group at https://fevir.net/resources/Project/27845.


 

Tools Development Updates:

  • The Fast Evidence Interoperability Resources (FEvIR) Platform is set up to support a hub for data exchange using the FHIR® standard. (FHIR is the registered trademark of Health Level Seven International (HL7) and the use does not constitute endorsement by HL7.)
    • Resources (in FHIR JSON) are currently available at https://fevir.net/content/resources including ActivityDefinition, ArtifactAssessment, Bundle, Citation, CodeSystem, Consent, Evidence, EvidenceReport, EvidenceVariable, Group, Organization, PlanDefinition, Practitioner, PractitionerRole, Questionnaire, QuestionnaireResponse, ResearchStudy, ResearchSubject, StructureDefinition, and ValueSet Resources using the FHIR® standard (current build).
    • The FEvIR Platform is available for use now, but is “pre-release”.  The current version is 0.48.0 (April 25, 2022). Viewing resources is open without login.  Signing in is free and required to create content (which can then only be edited by the person who created the content).
    • Release notes can be found at https://fevir.net/resources/Project/29394.


  • The FEvIR Platform has 9 Builder Tools that are available when logged in, and 13 Viewer Tools that are available without logging in.

 

    • Builder Tools enable creation of a FHIR Resource without any working knowledge of FHIR or JSON.
    • Viewer Tools enable human-friendly viewing of a FHIR Resource. Views may include outline representation of the JSON and/or specialized views based on the resource type.

 



 

    • Converter Tools transform data between known structured forms and the FHIR standard form.


    • Computable Publishing®: MEDLINE-to-FEvIR Converter version 1.9.0 (April 14, 2022) converts PubMed MEDLINE XML to FHIR JSON.
      • Enter a PubMed Identifier (PMID) and click Submit.
      • A complete Citation Resource will be created from the PubMed Medline XML for this record.



    • Computable Publishing®: FEvIR-to-ClinicalTrials.gov Converter version 1.2.0 (May 2, 2022) is available without logging in.
      • You can access this tool when viewing a ResearchStudy Resource by clicking on the “Create CT.gov JSON” button.
      • The tool provides a complete transformation from FHIR Resources on the FEvIR Platform (which were created by the ClinicalTrials.gov-to-FEvIR Converter) back to the ClinicalTrials.gov format.
      • Release 1.2.0 (May 2, 2022) updates all components to match the current FHIR Build specification and to interpret contained resources.



    • Computable Publishing®: My Ballot version 0.4.1 (March 25, 2022) is available when logged in.
      • The tool will identify all Code Systems for which you are signed up for a voting group, identify all terms open for voting, present you all the concepts being voted upon, present all your current active votes, allow you to set or change your votes for all terms on one screen, then submit all desired votes at once.

 

 

    • Computable Publishing®: Recommendations Table Viewer version 0.9.0 (March 21, 2022) is a concept demonstration.
      • This concept demonstration shows a collection of computable recommendations derived from manual review of online guideline publications AND review of structured data from GRADEpro and MAGICapp software systems.

 


    • Computable Publishing®: Risk of Bias Assessment Tool (RoBAT) version 0.13.0 (May 2, 2022) creates a report for a complete risk of bias assessment.
      • You can access the RoBAT by opening any Citation, Evidence, or EvidenceReport Resource (see the Resource List), then click the Rate button in the left navigation menu.
      • If you want to rate a new item, you can create a Citation for it with the Citation Builder.
      • Release 0.13.0 (May 2, 2022) revises the Instructions at the top of the starting page to provide a one-line description of each of the profiles.



FHIR Resource Updates:

Details summarized in a list of the FHIR Tracker items across the EBMonFHIR/COKA efforts.

Changes approved to be applied to the FHIR specification include:



Proposed changes to the FHIR specification include:

·        Consider Characteristic Datatype

o   Propose to add SearchParameter.characteristic 0..1 Characteristic

o   Use the BackboneElement in EvidenceVariable.characteristic

·        For the EvidenceVariable Resource:

·       For the EvidenceReport Resource:

o   We will replace it with an EvidenceReport Profile of Composition Resource, following discussions with the HL7 Structured Documents Work Group. (https://jira.hl7.org/browse/FHIR-31867)

o   We will create an EvidenceSubject Profile of EvidenceVariable Resource, to support EvidenceReport.subject when describing the subject of an evidence report as a collection of characteristics.

·       For the Group Resource:

o   We proposed the addition of the Expression data type as an option for characteristic.value[x] with the example for implementation at https://fevir.net/resources/Group/18125 (https://jira.hl7.org/browse/FHIR-33264) --- this may be replaced with the changes applied to EvidenceVariable to support Eligibility Criteria.

o   Add codes to Group.type or allow Group.subpartType (https://jira.hl7.org/browse/FHIR-32318) – may be resolved with allowing Group

·       For the Identifier Datatype:

o   Change definition of “usual” to “The identifier recommended for display and use in real-world interactions, used when such identifier is different from the ‘official’ identifier.” (https://jira.hl7.org/browse/FHIR-32308)

 

·       To support efforts to convert ClinicalTrials.gov data to FHIR Resources (> 500 element-to-element conversion path listings) we are proposing to add a recruitmentStatus extension to Location and Organization Resources to support use for research sites (https://jira.hl7.org/browse/FHIR-33000, https://jira.hl7.org/browse/FHIR-32317). 

 

·        To support efforts to use FHIR Resources for research with ResearchSubject Resources:

o   Modify the source element or add extension to Reference(ResearchSubject) in QuestionnaireResponse Resource

o   Consider similar modifications for Observation.subject and Observation.performer

 

Knowledge Ecosystem and Scholarly Communications Updates:


We created a PowerPoint presentation for Reaching the Desired Healthcare Knowledge Ecosystem with Computable Resources


In the HL7 January Working Group Meeting we presented


Upcoming conferences for consideration for submitting presentation proposals:


Pathways to Enable Open-Source Ecosystems (POSE)


We are submitting a proposal this week for a National Science Foundation POSE program to support scoping the development of an open-source ecosystem (OSE). An OSE is a platform for many developers to contribute to shared technology. Current thinking is to establish the Comment, Rating Or Classifier (CROC) Library on the FEvIR Platform as a mature open-source product and propose developing an ecosystem to enable many more users and contributors to the efforts to extend FHIR to scientific knowledge (EBMonFHIR) by the creation and use of CROCs about scientific knowledge.


Request for Public Comment (AHRQ PCORTF) – response submitted from the COKA Initiative, in response to AHRQ’s Patient-Centered Outcomes Research Strategic Framework: Request for Public Comment.


 


Quotes for Thought:

 

  • “The things we fear most in organizations - fluctuations, disturbances, imbalances - are the primary sources of creativity.” --Margaret J. Wheatley
  • "Don't let what you can't do stop you from doing what you can do." --John Wooden


  • "You must unlearn what you have learned." –Yoda (appearing in a vision on May 4)
  • "A career is what you get paid for. A calling is what you are made for." –Steve Harvey
  • “When you talk, you are only repeating what you already know. But if you listen, you may learn something new. –Dalai Lama

Let me know if you have a quote to suggest for sharing.


 

Code System Development 

Current Status of Code System Development (As of April 2022)

COKA Participation




COKA Impact

A recently published article, Novel Informatics Approaches to COVID-19 Research: from methods to applications, includes: “A group of researchers from the COVID-19 Knowledge Accelerator (COKA) initiative proposed the development of a code system for electronic data exchange for the identification, processing, and reporting of scientific findings of COVID-19.”

 

We are pleased to be featured prominently in the LHS ACTS Concept Demonstration-LHS Case Study


We were pleased to see the EBMonFHIR efforts noted in a report by the AHRQ CEDAR project and they are using the Citation Resource for this effort (https://digital.ahrq.gov/sites/default/files/docs/citation/cedar-environmental-scan.pdf).

 

We were pleased to see the EBMonFHIR and COKA and ACTS efforts noted in an article in Yearbook of Medical Informatics titled “The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System” (https://www.thieme-connect.de/products/ejournals/html/10.1055/s-0041-1726503).


Scholarly Publications:

  • Alper BS, Dehnbostel J, Shahin K, Soares A, Tristan M, Tufte J, for the COVID-19 Knowledge Accelerator (COKA) Initiative. Making Science Computable: Advancing Evidence-Based Health Care with Standard Terminologies. International Society for Evidenced Based Health Care: 30th ISEHC Newsletter December 2021 Edition. Published by email distribution December 23, 2021. https://mailchi.mp/3a6a1fbb8958/30th-isebhc-newsletter-dec-2021-5946756

  • Alper BS, Dehnbostel J, Afzal M, Subbian V, Soares A, Kunnamo I, Shahin K, McClure RC, For the COVID-19 Knowledge Accelerator (COKA) Initiative. Making Science Computable: Developing code systems for statistics, study design, and risk of bias. Journal of Biomedical Informatics 2021 Mar;115:103685. Published online January 21, 2021. doi: 10.1016/j.jbi.2021.103685. https://doi.org/10.1016/j.jbi.2021.103685

  • Alper BS, Richardson JE, Lehmann HP, Subbian V. It is time for computable evidence synthesis : The COVID-19 Knowledge Accelerator Initiative. J Am Med Inform Assoc 2020 Aug;27(8):1338-1339. Doi : 10.1093/jamia/ocaa114. https://academic.oup.com/jamia/article/27/8/1338/5842142

  • Alper, BS, Flynn, A, Bray, BE, et al. Categorizing metadata to help mobilize computable biomedical knowledge. Learn Health Sys. 2021;e10271. https://doi.org/10.1002/lrh2.10271

Cited by:

Challenges of evidence synthesis during the 2020 COVID pandemic: a scoping review, J Clin Epidemiol. 2021 Oct 27;S0895-4356(21)00338-3. PMID 34718121 https://www.jclinepi.com/article/S0895-4356(21)00338-3/fulltext