Success factors for interventions to reduce low-value imaging. Six crucial lessons learned from a practical case study in Norway
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Substantial overuse of health care services is identified and intensified efforts are incited to reduce low-value services in general and in imaging in particular. OBJECTIVE: To report crucial success factors for developing and implementing interventions to reduce specific low-value imaging examinations based on a case study in Norway. MATERIALS AND METHODS: Mixed methods design including one systematic review, one scoping review, implementation science, qualitative interviews, content analysis of stakeholders' input, and stakeholder deliberations. RESULTS: The description and analysis of an intervention to reduce low-value imaging in Norway identifies six general success factors: 1) Acknowledging complexity: advanced knowledge synthesis, competence of the context, and broad and strong stakeholder involvement is crucial to manage de-implementation complexity. 2) Clear consensus-based criteria for selecting low-value imaging procedures are key. 3) Having a clear target group is critical. 4) Stakeholder engagement is essential to ascertain intervention relevance and compliance. 5) Active and well-motivated intervention collaborators is imperative. 6) Paying close attention to the mechanisms of low-value imaging and the barriers to reduce it is decisive. CONCLUSION: Reducing low-value imaging is crucial to increase the quality, safety, efficiency, and sustainability of the health services. Reducing low-value imaging is a complex task and paying attention to specific practical success factors is key.
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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 | 0.006 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it