Understanding the Drivers of Low-value Care and De-implementation Processes: A Multi-methods Study
Bibliographic record
Abstract
Low-value care (LVC) are tests, treatments, medications or procedures which have been deemed, through evidence, to be ineffective, harmful or unnecessary. It is estimated that 30% of current healthcare dollars are spent on these practices (Canadian Institute for Health Information, 2017). The de-implementation of low-value care is widely recognized as critical for patient safety, healthcare resources and health system sustainability. In recent years, a number of initiatives, such as Choosing Wisely, have targeted the de-implementation of low-value practices in healthcare. The de-implementation of LVC is an important endeavour that poses challenging conceptual and practical questions. This dissertation explores the conceptual and practical aspects of LVC and de-implementation through three complementary studies. In the first study, I conducted a comprehensive scoping review of the literature on the use of theory to understand or explain efforts to reduce LVC. This review offers four key findings: 1. De-implementation is still largely atheoretical, but the use of theory is increasing; 2. TDF is the most commonly used framework; 3. Theory was most commonly used to identify determinants or inform data analysis; and 4. The lack of studies examining the impact of patients on efforts to de-implement LVC. In the second study, I developed the Implementation Process Model (IPM) using a modified Delphi approach. This study, to advance understanding of de-implementation processes and explicate the elements of implementation processes, identified four critical elements important to implementation processes: 1. Stakeholder engagement throughout the process; 2. Iterative nature of implementation processes; 3. Importance of context; and 4. Importance of using guiding theories or frameworks. The third study used the knowledge and tools produced in the first two studies to conduct a qualitative exploration of factors which impact LVC and de-implementation processes. The findings of this study explicated the drivers of LVC, the magnitude of the problem of LVC and unique influences on de-implementation processes. Understanding LVC and de-implementation processes is critical to improving patient care, reducing waste and improving the healthcare system. The results of these studies provide insights into theoretical approaches, implementation processes, and factors which influence LVC and de-implementation applicable to both research and practice.
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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 | 0.072 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".