Impact of an Electronic Decision-Support System on Nursing Triage Process: A Usability and Workflow Analysis
Bibliographic record
Abstract
PURPOSE: To qualitatively contrast emergency department triage workflow using a paper-based triage system and an electronic decision-support tool (electronic Canadian Triage and Acuity Scale (eCTAS)). METHODS: Triage nurses from a single institution were invited to perform triage assessments of mock patients using a paper-based triage system and eCTAS. These assessments were completed using simulation scenarios, some of which involved facilitators probing triage nurses' thoughts on the design of the eCTAS system. Participants were asked to "think aloud," describing their thought patterns as they completed the triage process. Similar patient scenarios with the same Canadian Emergency Department Information System (CEDIS) presenting complaint and triage score were used for comparison between paper-based triage and electronic decision-support tool (eCTAS) conditions. RESULTS: Eight participants completed at least two simulation scenarios for each condition and at least one usability scenario with eCTAS. The simulated encounters showed eCTAS provided several advantages to paper-based triage assessment process by shortlisting possible CEDIS complaints and preselecting relevant modifiers. However, usability concerns were identified with eCTAS including challenges related to data entry and eCTAS score overrides. CONCLUSIONS: Our study highlights several positive features of eCTAS and usability issues that should be addressed to enhance the intended use of eCTAS and support user adoption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".