Evidence map›Paper›PMID 35477149›Full record

ArticleApplied clinical informatics2022

Usability and Acceptability of Clinical Decision Support Based on the KIIDS-TBI Tool for Children with Mild Traumatic Brain Injuries and Intracranial Injuries.

Jacob K Greenberg, Ayodamola Otun, Pyi Theim Kyaw, Christopher R Carpenter, Ross C Brownson, Nathan Kuppermann, David D Limbrick, Randi E Foraker, Po-Yin Yen

Abstract read
In one paragraph

Article in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Implementation of geriatric clinical decision support in the emergency department for potentially inappropriate medications.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2026
    Article
  3. Article
  4. Article
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

9 authors.

Jacob K GreenbergDepartment of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States.
Ayodamola OtunDepartment of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States.
Pyi Theim KyawMcKelvey School of Engineering, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States.
Christopher R CarpenterDepartment of Emergency Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States.
Ross C BrownsonBrown School of Social Work, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States.
Nathan KuppermannDepartment of Emergency Medicine, University of California Davis, Davis, California, United States.
David D LimbrickDepartment of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States.
Randi E ForakerInstitute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States.
Po-Yin YenInstitute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States.

Funding

Validation and Implementation of Clinical Decision Support for Managing Children with Mild Head Injuries and Intracranial InjuryF32HS027075 · AHRQ · WASHINGTON UNIVERSITY · PI GREENBERG, JACOB · 2020 to 2021
$88k
AHRQ HHS F32 HS027075
6 · The paper itself

Abstract

backgroundThe Kids Intracranial Injury Decision Support tool for Traumatic Brain Injury (KIIDS-TBI) tool is a validated risk prediction model for managing children with mild traumatic brain injuries (mTBI) and intracranial injuries. Electronic clinical decision support (CDS) may facilitate the clinical implementation of this evidence-based guidance.

objectiveOur objective was to evaluate the acceptability and usability of an electronic CDS tool for managing children with mTBI and intracranial injuries.

methodsEmergency medicine and neurosurgery physicians (10 each) from 10 hospitals in the United States were recruited to participate in usability testing of a novel CDS prototype in a simulated electronic health record environment. Testing included a think-aloud protocol, an acceptability and usability survey, and a semi-structured interview. The prototype was updated twice during testing to reflect user feedback. Usability problems recorded in the videos were categorized using content analysis. Interview transcripts were analyzed using thematic analysis.

resultsAmong the 20 participants, most worked at teaching hospitals (80%), freestanding children's hospitals (95%), and level-1 trauma centers (75%). During the two prototype updates, problems with clarity of terminology and navigating through the CDS interface were identified and corrected. Corresponding to these changes, the number of usability problems decreased from 35 in phase 1 to 8 in phase 3 and the number of mistakes made decreased from 18 (phase 1) to 2 (phase 3). Through the survey, participants found the tool easy to use (90%), useful for determining a patient's level of care (95%), and likely to improve resource use (90%) and patient safety (79%). Interview themes related to the CDS's ability to support evidence-based decision-making and improve clinical workflow proposed implementation strategies and potential pitfalls.

conclusionAfter iterative evaluation and refinement, the KIIDS-TBI CDS tool was found to be highly usable and useful for aiding the management of children with mTBI and intracranial injuries.

Indexed as

Brain Injuries, TraumaticCraniocerebral TraumaDecision Support Systems, ClinicalChildElectronic Health RecordsHospitals, PediatricHumans

Identifiers

PMID35477149
PMCPMC9045962

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Registered trials

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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.