Success factors for interventions to reduce low-value imaging. Six crucial lessons learned from a practical case study in Norway
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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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 | 0.038 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".