Perspectives on low-value care and barriers to de-implementation among primary care physicians: a multinational survey
Bibliographic record
Abstract
BACKGROUND: Healthcare costs are rising worldwide. At the same time, a considerable proportion of care does not benefit or may even be harmful to patients. We aimed to explore attitudes towards low-value care and identify the most important barriers to the de-implementation of low-value care use in primary care in high-income countries. METHODS: Between May and June 2022, we email surveyed primary care physicians in six high-income countries (Austria, Finland, Greece, Italy, Japan, and Sweden). Physician respondents were eligible if they had worked in primary care during the previous 24 months. The survey included four sections with categorized questions on (1) background information, (2) familiarity with Choosing Wisely recommendations, (3) attitudes towards overdiagnosis and overtreatment, and (4) barriers to de-implementation, as well as a section with open-ended questions on interventions and possible facilitators for de-implementation. We used descriptive statistics to present the results. RESULTS: Of the 16,935 primary care physicians, 1,731 answered (response rate 10.2%), 1,505 had worked in primary care practice in the last 24 months and were included in the analysis. Of the respondents, 53% had read Choosing Wisely recommendations. Of the respondents, 52% perceived overdiagnosis and 50% overtreatment as at least a problem to some extent in their own practice. Corresponding figures were 85% and 81% when they were asked regarding their country's healthcare. Respondents considered patient expectations (85% answered either moderate or major importance), patient's requests for treatments and tests (83%), fear of medical error (81%), workload/lack of time (81%), and fear of underdiagnosis or undertreatment (79%) as the most important barriers for de-implementation. Attitudes and perceptions of barriers differed significantly between countries. CONCLUSIONS: More than 80% of primary care physicians consider overtreatment and overdiagnosis as a problem in their country's healthcare but fewer (around 50%) in their own practice. Lack of time, fear of error, and patient pressures are common barriers to de-implementation in high-income countries and should be acknowledged when planning future healthcare. Due to the wide variety of barriers to de-implementation and differences in their importance in different contexts, understanding local barriers is crucial when planning de-implementation strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".