Evidence map›Paper›PMID 38916936›Full record

ArticleJournal of medical Internet research2024

Making Science Computable Using Evidence-Based Medicine on Fast Healthcare Interoperability Resources: Standards Development Project.

Andrey Soares, Lisa M Schilling, Joshua Richardson, Bhagvan Kommadi, Vignesh Subbian, Joanne Dehnbostel, Khalid Shahin, Karen A Robinson, Muhammad Afzal, Harold P Lehmann and 2 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Making guidelines computable.Clinical and public health guidelines · 2024
    Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Andrey SoaresDepartment of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.ORCID 0000-0003-4319-9411
Lisa M SchillingDepartment of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.ORCID 0000-0002-6878-189X
Joshua RichardsonCenter for Informatics, Research Triangle Institute International, Berkeley, CA, United States.ORCID 0000-0003-3066-2673
Bhagvan KommadiQuantica Computacao, Hyderabad, India.ORCID 0000-0002-1098-1792
Vignesh SubbianScientific Knowledge Accelerator Foundation, Franklin, NC, United States.ORCID 0000-0001-9974-8382
Joanne DehnbostelScientific Knowledge Accelerator Foundation, Franklin, NC, United States.ORCID 0000-0002-9896-9842
Khalid ShahinScientific Knowledge Accelerator Foundation, Franklin, NC, United States.ORCID 0000-0002-3526-7667
Karen A RobinsonScientific Knowledge Accelerator Foundation, Franklin, NC, United States.ORCID 0000-0003-1021-7820
Muhammad AfzalScientific Knowledge Accelerator Foundation, Franklin, NC, United States.ORCID 0000-0002-7851-2327
Harold P LehmannScientific Knowledge Accelerator Foundation, Franklin, NC, United States.ORCID 0000-0002-7698-219X
Ilkka KunnamoScientific Knowledge Accelerator Foundation, Franklin, NC, United States.ORCID 0000-0002-4817-6991
Brian S AlperScientific Knowledge Accelerator Foundation, Franklin, NC, United States.ORCID 0000-0003-4300-4928

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEvidence-based medicine (EBM) has the potential to improve health outcomes, but EBM has not been widely integrated into the systems used for research or clinical decision-making. There has not been a scalable and reusable computer-readable standard for distributing research results and synthesized evidence among creators, implementers, and the ultimate users of that evidence. Evidence that is more rapidly updated, synthesized, disseminated, and implemented would improve both the delivery of EBM and evidence-based health care policy.

objectiveThis study aimed to introduce the EBM on Fast Healthcare Interoperability Resources (FHIR) project (EBMonFHIR), which is extending the methods and infrastructure of Health Level Seven (HL7) FHIR to provide an interoperability standard for the electronic exchange of health-related scientific knowledge.

methodsAs an ongoing process, the project creates and refines FHIR resources to represent evidence from clinical studies and syntheses of those studies and develops tools to assist with the creation and visualization of FHIR resources.

resultsThe EBMonFHIR project created FHIR resources (ie, ArtifactAssessment, Citation, Evidence, EvidenceReport, and EvidenceVariable) for representing evidence. The COVID-19 Knowledge Accelerator (COKA) project, now Health Evidence Knowledge Accelerator (HEvKA), took this work further and created FHIR resources that express EvidenceReport, Citation, and ArtifactAssessment concepts. The group is (1) continually refining FHIR resources to support the representation of EBM; (2) developing controlled terminology related to EBM (ie, study design, statistic type, statistical model, and risk of bias); and (3) developing tools to facilitate the visualization and data entry of EBM information into FHIR resources, including human-readable interfaces and JSON viewers.

conclusionsEBMonFHIR resources in conjunction with other FHIR resources can support relaying EBM components in a manner that is interoperable and consumable by downstream tools and health information technology systems to support the users of evidence.

Indexed as

Evidence-Based MedicineHealth Information InteroperabilityCOVID-19Health Level SevenHumanscomputable evidenceEBMonFHIRevidence-based medicineevidence-based medicine on Fast Healthcare Interoperability ResourcesFast Healthcare Interoperability ResourcesFHIR

Identifiers

PMID38916936
PMCPMC11234056

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.