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Record W4410523636 · doi:10.1136/bmjoq-2025-qshu.211

211 Sustaining changes in clinician behavior using audit and feedback

2025· article· en· W4410523636 on OpenAlexaboutno aff
Karl Chamberlin, Christopher DiTullio, Martin A. Reznek, Kevin A. Kotkowski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsAuditComputer sciencePsychologyBusinessAccounting

Abstract

fetched live from OpenAlex

Introduction Validated clinical decision rules such as the NEXUS Cervical Spine (C-spine) Rule can be used to risk stratify patients who present to the emergency department (ED) for neck trauma, identifying patients who are low-risk for a clinically significant injury and obviating the need for computed tomography (CT) imaging. 1 Despite the availability of such tools, imaging overutilization remains a pervasive issue.2 Overutilization of CT imaging exposes patients to unnecessary radiation, impairs hospital throughput, and increases healthcare costs.2–4 In a prior randomized controlled trial of audit-and-feedback strategies, we demonstrated the effectiveness of audit-and-feedback for reducing overutilization of CT C-spine imaging in the ED.5 In this study, we aim to characterize the longitudinal effectiveness of our prior audit-and-feedback intervention on CT overutilization.Methods This was an observational study of a cohort of emergency clinicians within a single academic department of emergency medicine that staffs five emergency departments in a regional healthcare system. Clinicians in this department were previously enrolled in a trial of audit-and-feedback strategies to reduce overutilization of CT C-spine, and at the conclusion of the trial, all clinicians received individualized digital feedback on their own practice patterns, including their individual CT C-spine overutilization rate and the number of CTs they ordered per month. For one year after the conclusion of the trial, we reviewed the medical records of every patient who underwent CT C-spine during a one-week audit period each quarter. These data were used to calculate the cohort’s average overutilization rate, defined as the percentage of patients who underwent CT C-spine but were low-risk by the NEXUS C-spine Rule. The outcomes of interest in this study were the CT overutilization rates at three, six, nine, and twelve months after the conclusion of the trial.Results There were 130 emergency clinicians included in the observation cohort, who ordered 755 CT C-spine studies during the four audit periods. The pre-trial baseline overutilization rate was 47% of CT C-spine studies, decreasing to 33% and 36% in the intervention groups by the end of the trial. After the conclusion of the trial, the overutilization rate across the entire cohort remained lower than baseline at all time points: 36% at three months, 30% at six months, 35% at nine months, and 33% at twelve months.Conclusion Our results provide evidence that digital audit-and-feedback is associated with a sustained reduction in CT overutilization for at least one year after the intervention. These results support audit-and-feedback as an effective strategy to sustain changes in clinician behavior.References Hoffman JR, Mower WR, Wolfson AB, et al. Validity of a set of clinical criteria to rule out injury to the cervical spine in patients with blunt trauma. National Emergency X-Radiography Utilization Study Group. N Engl J Med. 2000;343(2):94–9.Mills AM, Raja AS, Marin JR. Optimizing diagnostic imaging in the emergency department. Acad Emerg Med. 2015;22(5):625–31.Bellolio MF, Heien HC, Sangaralingham LR, et al. Increased computed tomography utilization in the emergency department and its association with hospital admission. West J Emerg Med. 2017;18(5):835–45.Evans CS, Arthur R, Kane M, et al. Incidental radiology findings on computed tomography studies in emergency department patients: a systematic review and meta-analysis. Ann Emerg Med. 2022;80(3):243–56.Chamberlin KT, Ditullio C, Rossman J, et al. A randomized controlled trial of audit-and-feedback strategies to reduce imaging overutilization in the emergency department. BMJ Qual Saf. Accepted for publication, March 2025.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.004

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.

Opus teacher head0.112
GPT teacher head0.510
Teacher spread0.398 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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