Understanding Overuse of Computed Tomography for Minor Head Injury in the Emergency Department: A Triangulated Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Overuse of computed tomography (CT) for minor head injury continues despite developed and rigorously validated clinical decision rules like the Canadian CT Head Rule (CCHR). Adherence to this sensitive and specific rule could decrease the number of CT scans performed in minor head injury by 35%. But in practice, the CCHR has failed to reduce testing, despite its accurate performance. OBJECTIVES: The objective was to identify nonclinical, human factors that promote or inhibit the appropriate use of CT in patients presenting to the emergency department (ED) with minor head injury. METHODS: This was a qualitative study in three phases, each with interview guides developed by a multidisciplinary team. Subjects were recruited from patients treated and released with minor head injuries and providers in an urban academic ED and a satellite community ED. Focus groups of patients (four groups, 22 subjects total) and providers (three groups, 22 subjects total) were conducted until thematic saturation was reached. The findings from the focus groups were triangulated with a cognitive task analysis, including direct observation in the ED (>150 hours), and individual semistructured interviews using the critical decision method with four senior physician subject matter experts. These experts are recognized by their peers for their skill in safely minimizing testing while maintaining patient safety and engagement. Focus groups and interviews were audio recorded and notes were taken by two independent note takers. Notes were entered into ATLAS.ti and analyzed using the constant comparative method of grounded theory, an iterative coding process to determine themes. Data were double-coded and examined for discrepancies to establish consensus. RESULTS: Five core domains emerged from the analysis: establishing trust, anxiety (patient and provider), constraints related to ED practice, the influence of others, and patient expectations. Key themes within these domains included patient engagement, provider confidence and experience, ability to identify and manage patient anxiety, time constraints, concussion knowledge gap, influence of health care providers, and patient expectations to get a CT. CONCLUSIONS: Despite high-quality evidence informing use of CT in minor head injury, multiple factors influence the decision to obtain CT in practice. Identifying and disseminating approaches and designing systems that help clinicians establish trust and manage uncertainty within the ED context could optimize CT use in minor head injury.
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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 | 0.036 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".