Mixed-methods approach to understanding clinician macrocognition in the design of a clinical decision support tool: a study protocol
Bibliographic record
Abstract
Introduction The anatomic variants of congenital heart disease (CHD) are multiple. The increased survival of these patients and disposition into communities has led to an increase in their acute presentation to non-CHD experts in primary care clinics and emergency departments. Given the vulnerability and fragility of these patients in the face of acute illness, new clinical decision support systems (CDSS) are urgently needed to better translate the best practice recommendations for the care of these patients. This study aims to understand the perceived confidence and macrocognitive processes of non-CHD experts (emergency medicine physicians) and CHD experts (paediatric cardiac intensivists) when treating children with CHD during acute illness and apply this to optimise the design of a CDSS (MyHeartPass ™ ) for these patients. Methods and analysis The first phase of the study involves a survey of non-CHD experts and CHD experts to understand their perceived confidence as it relates to treating acutely ill patients with CHD. The second phase is a qualitative cognitive task analysis using critical decision method to characterise and compare the macrocognitive processes used by non-CHD experts and CHD experts during the critical decision making. In phases 3 and 4, heuristic evaluation and usability testing of the CDSS will be completed. These results will be used to inform design changes to the chosen CDSS (MyHeartPass ™ ). In the final phase, a within-participant simulation design will be used to study the effect of the CDSS on clinical decision making compared with baseline (without use of CDSS). Ethics and dissemination Ethics approval from The Hospital for Sick Children in Toronto, Ontario, Canada has been obtained for all phases. Results will be published in peer-reviewed journals and presented at relevant conferences. On successful completion of these studies, it is anticipated that there will be a controlled implementation of the redesigned CDSS.
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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 | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".