Beyond guideline knowledge: a theory-based qualitative study of low-value preoperative testing
Bibliographic record
Abstract
BACKGROUND: Choosing Wisely Canada and most major anesthesia and preoperative guidelines recommend against obtaining preoperative tests before low-risk procedures. However, these recommendations alone have not reduced low-value test ordering. In this study, the theoretical domains framework (TDF) was used to understand the drivers of preoperative electrocardiogram (ECG) and chest X-ray (CXR) ordering for patients undergoing low-risk surgery ('low-value preoperative testing') among anesthesiologists, internal medicine specialists, nurses, and surgeons. METHODS: Using snowball sampling, preoperative clinicians working in a single health system in Canada were recruited for semi-structured interviews about low-value preoperative testing. The interview guide was developed using the TDF to identify the factors that influence preoperative ECG and CXR ordering. Interview content was deductively coded using TDF domains and specific beliefs were identified by grouping similar utterances. Domain relevance was established based on belief statement frequency, presence of conflicting beliefs, and perceived influence over preoperative test ordering practices. RESULTS: Sixteen clinicians (7 anesthesiologists, 4 internists, 1 nurse, and 4 surgeons) participated. Eight of the 12 TDF domains were identified as the drivers of preoperative test ordering. While most participants agreed that the guidelines were helpful, they also expressed distrust in the evidence behind them (knowledge). Both a lack of clarity about the responsibilities of the specialties involved in the preoperative process and the ease by which any clinician could order, but not cancel tests, were drivers of low-value preoperative test ordering (social/professional role and identity, social influences, belief about capabilities). Additionally, low-value tests could also be ordered by nurses or the surgeon and may be completed before the anesthesia or internal medicine preoperative assessment appointment (environmental context and resources, beliefs about capabilities). Finally, while participants agreed that they did not intend to routinely order low-value tests and understood that these would not benefit patient outcomes, they also reported ordering tests to prevent surgery cancellations and problems during surgery (motivation and goals, beliefs about consequences, social influences). CONCLUSIONS: We identified key factors that anesthesiologists, internists, nurses, and surgeons believe influence preoperative test ordering for patients undergoing low-risk surgeries. These beliefs highlight the need to shift away from knowledge-based interventions and focus instead on understanding local drivers of behaviour and target change at the individual, team, and institutional levels.
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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 | 0.037 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".