Using theories and frameworks to understand how to reduce low-value healthcare: a scoping review
Bibliographic record
Abstract
BACKGROUND: There is recognition that the overuse of procedures, testing, and medications constitutes low-value care which strains the healthcare system and, in some circumstances, can cause unnecessary stress and harm for patients. Initiatives across dozens of countries have raised awareness about the harms of low-value care but have had mixed success and the levels of reductions realized have been modest. Similar to the complex drivers of implementation processes, there is a limited understanding of the individual and social behavioral aspects of de-implementation. While researchers have begun to use theory to elucidate the dynamics of de-implementation, the research remains largely atheoretical. The use of theory supports the understanding of how and why interventions succeed or fail and what key factors predict success. The purpose of this scoping review was to identify and characterize the use of theoretical approaches used to understand and/or explain what influences efforts to reduce low-value care. METHODS: We conducted a review of MEDLINE, EMBASE, CINAHL, and Scopus databases from inception to June 2021. Building on previous research, 43 key terms were used to search the literature. The database searches identified 1998 unique articles for which titles and abstracts were screened for inclusion; 232 items were selected for full-text review. RESULTS: Forty-eight studies met the inclusion criteria. Over half of the included articles were published in the last 2 years. The Theoretical Domains Framework (TDF) was the most commonly used determinant framework (n = 22). Of studies that used classic theories, the majority used the Theory of Planned Behavior (n = 6). For implementation theories, Normalization Process Theory and COM-B were used (n = 7). Theories or frameworks were used primarily to identify determinants (n = 37) and inform data analysis (n = 31). Eleven types of low-value care were examined in the included studies, with prescribing practices (e.g., overuse, polypharmacy, and appropriate prescribing) targeted most frequently. CONCLUSIONS: This scoping review provides a rigorous, comprehensive, and extensive synthesis of theoretical approaches used to understand and/or explain what factors influence efforts to reduce low-value care. The results of this review can provide direction and insight for future primary research to support de-implementation and the reduction of low-value care.
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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 | 0.052 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.053 | 0.041 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".