Development and usability testing of a multifaceted intervention to reduce low-value injury care
Bibliographic record
Abstract
BACKGROUND: Multifaceted interventions that address barriers and facilitators have been shown to be most effective for increasing the adoption of high-value care, but there is a knowledge gap on this type of intervention for the de-implementation of low-value care. Trauma is a high-risk setting for low-value care, such as unnecessary diagnostic imaging and the use of specialized resources. The aim of our study was to develop and assess the usability of a multifaceted intervention to reduce low-value injury care. METHODS: We used the Consolidated Framework for Implementation Research and the Expert Recommendations for Implementing Change tool as theoretical foundations to identify barriers and facilitators, and strategies for the reduction of low-value practices. We designed an initial prototype of the intervention using the items of the Template for Intervention Description and Replication. The prototype's usability was iteratively tested through four focus groups and four think-aloud sessions with trauma decision-makers (n = 18) from seven Level I to Level III trauma centers. We conducted an inductive analysis of the audio-recorded sessions to identify usability issues and other barriers and facilitators to refine the intervention. RESULTS: We identified barriers and facilitators related to individual characteristics, including knowledge and beliefs about low-value practices and the de-implementation process, such as the complexity of changing practices and difficulty accessing performance feedback. Accordingly, the following intervention strategies were selected: involving governing structures and leaders, distributing audit & feedback reports on performance, and providing educational materials, de-implementation support tools and educational/facilitation visits. A total of 61 issues were identified during the usability testing, of which eight were critical, 33 were moderately important, and 18 were minor. These issues led to numerous improvements, including the addition of information on the drivers and benefits of reducing low-value practices, changes in the definition of these practices, the addition of proposed strategies to facilitate de-implementation, and the tailoring of educational/facilitation visits. CONCLUSIONS: We designed and refined a multifaceted intervention to reduce low-value injury care using a process that increases the likelihood of its acceptability and sustainability. The next step will be to evaluate the effectiveness of implementing this intervention using a pragmatic cluster randomized controlled trial. TRIAL REGISTRATION: This protocol has been registered on ClinicalTrials.gov (February 24th 2023, #NCT05744154, https://clinicaltrials.gov/ct2/show/NCT05744154 ).
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How this classification was reachedexpand
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 | 0.021 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".