Understanding low-value care and associated de-implementation processes: a qualitative study of Choosing Wisely Interventions across Canadian hospitals
Bibliographic record
Abstract
BACKGROUND: Choosing Wisely (CW) is an international movement comprised of campaigns in more than 20 countries to reduce low-value care (LVC). De-implementation, the reduction or removal of a healthcare practice that offers little to no benefit or causes harm, is an emerging field of research. Little is known about the factors which (i) sustain LVC; and (ii) the magnitude of the problem of LVC. In addition, little is known about the processes of de-implementation, and if and how these processes differ from implementation endeavours. The objective of this study was to explicate the myriad factors which impact the processes and outcomes of de-implementation initiatives that are designed to address national Choosing Wisely campaign recommendations. METHODS: Semi-structured interviews were conducted with individuals implementing Choosing Wisely Canada recommendations in healthcare settings in four provinces. The interview guide was developed using concepts from the literature and the Implementation Process Model (IPM) as a framework. All interviews were conducted virtually, recorded, and transcribed verbatim. Data were analysed using thematic analysis. FINDINGS: Seventeen Choosing Wisely team members were interviewed. Participants identified numerous provider factors, most notably habit, which sustain LVC. Contrary to reporting in recent studies, the majority of LVC in the sample was not 'patient facing'; therefore, patients were not a significant driver for the LVC, nor a barrier to reducing it. Participants detailed aspects of the magnitude of the problems of LVC, providing insight into the complexities and nuances of harm, resources and prevalence. Harm from potential or common infections, reactions, or overtreatment was viewed as the most significant types of harm. Unique factors influencing the processes of de-implementation reported were: influence of Choosing Wisely campaigns, availability of data, lack of targets and hard-coded interventions. CONCLUSIONS: This study explicates factors ranging from those which impact the maintenance of LVC to factors that impact the success of de-implementation interventions intended to reduce them. The findings draw attention to the significance of unintentional factors, highlight the importance of understanding the impact of harm and resources to reduce LVC and illuminate the overstated impact of patients in de-implementation literature. These findings illustrate the complexities of de-implementation.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".