Developing a framework to guide the de-adoption of low-value clinical practices in acute care medicine: a study protocol
Bibliographic record
Abstract
Abstract Background Healthcare systems have difficulty incorporating scientific evidence into clinical practice, especially when science suggests that existing clinical practices are of low-value (e.g. ineffective or harmful to patients). While a number of lists outlining low-value practices in acute care medicine currently exist, less is known about how best to initiate and sustain the removal of low-value clinical practices (i.e. de-adoption). This study will develop a comprehensive list of barriers and facilitators to the de-adoption of low-value clinical practices in acute care facilities to inform the development of a framework to guide the de-adoption process. Methods The proposed project is a multi-stage mixed methods study to develop a framework to guide the de-adoption of low-value clinical practices in acute care medicine that will be tested in a representative sample of acute care settings in Alberta, Canada. Specifically, we will: 1) conduct a systematic review of the de-adoption literature to identify published barriers and facilitators to the de-adoption of low-value clinical practices in acute care medicine and any associated interventions proposed (Phase one); 2) conduct focus groups with acute care stakeholders to identify important themes not published in the literature and obtain a comprehensive appreciation of stakeholder perspectives (Phase two); 3) extend the generalizability of focus group findings by conducting individual stakeholder surveys with a representative sample of acute care providers throughout the province to determine which barriers and facilitators identified in Phases one and two are most relevant in their clinical setting (Phase three). Identified barriers and facilitators will be catalogued and integrated with targeted interventions in a framework to guide the process of de-adoption in each of four targeted areas of acute care medicine (Emergency Medicine, Cardiovascular Health and Stroke, Surgery and Critical Care Medicine). Analyses will be descriptive using a combination of qualitative and quantitative analyses. Discussion There is a growing body of literature suggesting that the de-adoption of ineffective or harmful practices from patient care is integral to the delivery of high quality care and healthcare sustainability. The framework developed in this study will map barriers and facilitators to de-adoption to the most appropriate interventions, allowing stakeholders to effectively initiate, execute and sustain this process in an evidence-based manner.
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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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 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".