Evidence map›Paper›PMID 31592534›Full record

SynthesisJournal of the American Medical Informatics Association : JAMIA2020

Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis.

Pavithra I Dissanayake, Tiago K Colicchio, James J Cimino

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 2 pooled it
–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

29 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  15. [Ontologies Applied in Clinical Decision Support Systems for Diabetes].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2023
    Review
  16. Ontology for Symptomatic Treatment of Multiple Sclerosis.Healthcare informatics research · 2022
    Article
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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

3 authors.

Pavithra I DissanayakeInformatics Institute, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Tiago K ColicchioInformatics Institute, University of Alabama at Birmingham, Birmingham, Alabama, USA.
James J CiminoInformatics Institute, University of Alabama at Birmingham, Birmingham, Alabama, USA.

Funding

National Dissemination of I-Corps@NCATS: Accelerating Translation through CommercializationUL1TR003096 · NCATS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI GUTIERREZ, ORLANDO M, KIMBERLY, ROBERT P. · 2019 to 2023
$43.6M
NCATS NIH HHS UL1 TR003096
6 · The paper itself

Abstract

objectiveThe study sought to describe the literature describing clinical reasoning ontology (CRO)-based clinical decision support systems (CDSSs) and identify and classify the medical knowledge and reasoning concepts and their properties within these ontologies to guide future research.

methodsMEDLINE, Scopus, and Google Scholar were searched through January 30, 2019, for studies describing CRO-based CDSSs. Articles that explored the development or application of CROs or terminology were selected. Eligible articles were assessed for quality features of both CDSSs and CROs to determine the current practices. We then compiled concepts and properties used within the articles.

resultsWe included 38 CRO-based CDSSs for the analysis. Diversity of the purpose and scope of their ontologies was seen, with a variety of knowledge sources were used for ontology development. We found 126 unique medical knowledge concepts, 38 unique reasoning concepts, and 240 unique properties (137 relationships and 103 attributes). Although there is a great diversity among the terms used across CROs, there is a significant overlap based on their descriptions. Only 5 studies described high quality assessment.

conclusionWe identified current practices used in CRO development and provided lists of medical knowledge concepts, reasoning concepts, and properties (relationships and attributes) used by CRO-based CDSSs. CRO developers reason that the inclusion of concepts used by clinicians' during medical decision making has the potential to improve CDSS performance. However, at present, few CROs have been used for CDSSs, and high-quality studies describing CROs are sparse. Further research is required in developing high-quality CDSSs based on CROs.

Indexed as

Biological OntologiesClinical ReasoningDecision Support Systems, ClinicalHumansclinical conceptsclinical decision supportclinical ontologyclinical reasoning ontologyontology properties

Identifiers

PMID31592534
PMCPMC6913230

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.