Evaluating the use of clinical decision aids in an Australian emergency department: A cross‐sectional survey
Bibliographic record
Abstract
OBJECTIVE: To identify healthcare professionals' knowledge, self-reported use, and documentation of clinical decision aids (CDAs) in a large ED in Australia, to identify behavioural determinants influencing the use of CDAs, and healthcare professionals preferences for integrating CDAs into the electronic medical record (EMR) system. METHODS: Healthcare professionals (doctors, nurses and physiotherapists) working in the ED at the Gold Coast Hospital, Queensland were invited to complete an online survey. Quantitative data were analysed using descriptive statistics, and where appropriate, mapped to the theoretical domains framework to identify potential barriers to the use of CDAs. Qualitative data were analysed using content analysis. RESULTS: Seventy-four healthcare professionals (34 medical officers, 31 nurses and nine physiotherapists) completed the survey. Healthcare professionals' knowledge and self-reported use of 21 validated CDAs was low but differed considerably across CDAs. Only 4 out of 21 CDAs were reported to be used 'sometimes' or 'always' by the majority of respondents (Ottawa Ankle Rule for ankle injury, Wells' criteria for pulmonary embolism, Wells' criteria for deep vein thrombosis and PERC rule for pulmonary embolism). Most respondents wanted to increase their use of valid and reliable CDAs and supported the integration of CDAs into the EMR to facilitate their use and support documentation. Potential barriers impacting the use of CDAs represented three theoretical domains of knowledge, social/professional role and identity, and social influences. CONCLUSIONS: CDAs are used variably by healthcare professionals and are inconsistently applied in the clinical encounter. Preferences of healthcare professionals need to be considered to allow the successful integration of CDAs into the EMR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".