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Record W4410281322 · doi:10.2196/60268

Leveraging Dual Usability Methods to Evaluate Clinical Decision Support Among Patients With Traumatic Brain Injury: Mixed Methods Study

2025· article· en· W4410281322 on OpenAlexvenueno aff
Rubina Rizvi, Sameen Faisal, Mark Sussman, Patricia Mendlick, S. L. Brown, Elizabeth Lindemann

Bibliographic record

VenueJMIR Human Factors · 2025
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteNational Center for Advancing Translational SciencesAgency for Healthcare Research and Quality
KeywordsPreprintUsabilityTraumatic brain injuryDual (grammatical number)Clinical decision support systemMedicineDecision support systemPsychologyComputer scienceHuman–computer interactionData miningWorld Wide WebPsychiatry

Abstract

fetched live from OpenAlex

Background: Patients with traumatic brain injury are at an increased risk of developing venous thromboembolism. Clinical decision support systems (CDSSs) may improve the use of venous thromboembolism prophylaxis protocols, yet suffer from poor compliance among end users due to a lack of user-centered design. Objective: The objective of this work was to improve the content, design, and workflow integration of a traumatic brain injury-CDSS based on feedback from experts and end users. Methods: The CDSS was evaluated leveraging a dual usability approach. A set of usability experts (n=3) and trauma providers (n=5) performed heuristic evaluations and usability testing by end users. Data was collected through a triangulation of methods and analyzed using qualitative (thematic) and quantitative (descriptive) analyses. Results: Among the 145 total issues identified across both methods, 66 issues were found to be unique. Of the 66, a total of 17 issues were found by heuristic evaluations, 43 by usability testing by end users, and 6 were found across both methods. Thematic analysis was conducted on the 66 unique issues, which were further assigned to themes and subsequent subthemes. We identified 13 unique themes. The 3 most prevalent themes of 66 issues were lack of supporting evidence (n=17, 26%), operational barriers arising from the test environment (n=11, 17%), formatting inconsistencies, and lack of following standards (n=8, 12%). The system's usability scale survey score was 77.5 (SD 16, 95% CI 57.6-97.4), interpreted as an acceptable or good usability range. The mean response score for Single Ease Questions for all tasks was 5.9 (SD 0.53). Conclusions: Combining expert and end user-driven usability evaluation methods identified a more comprehensive list of issues. This can facilitate the optimization of the traumatic brain injury-CDSS, resulting in improved usability and care management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.079
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.419

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.094
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.202
GPT teacher head0.621
Teacher spread0.419 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes1
Has abstractyes

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