Guideline adherence: How do boards of directors deal with it? A survey in Dutch hospitals
Bibliographic record
Abstract
Objective: Adherence to guidelines is often low, as multiple barriers exist for guideline implementation. To tackle the implementation problem, awareness of the existence of guidelines is necessary for the health care process and setting as a whole.Purpose: Despite the importance of guidelines adherence, problems have been reported from hospitals in achieving this. This study gives insight into how boards of directors of general and specialist hospitals arrange the responsibilities for guideline adherence within their organisation, how they deal with guidelines for medical specialists and what opportunities exist for improvement.Methods: A survey was sent to 116 Dutch hospitals in 2015. Thirty-nine responses were included in the study for further analysis (net response rate of 36%). All data other than the open questions were analysed in SPSS using descriptives to answer the research question.Results: The findings demonstrated that the distribution of responsibility concerning guideline implementation is problematic. The boards of directors used a variety of information sources to keep informed about the status of implementation of the guidelines for medical specialists, mostly through medical specialists’ peer reviews (visits) and internal audits. The study revealed several opportunities for improvements, for example, that a national database is necessary with all up-to-date guidelines, whereby changes and news are distributed directly to hospitals and other stakeholders.Conclusions: This paper offers recommendations for a thoughtful shift in distribution of responsibility, as in a more desired situation the ultimate responsibility of the board of directors would decrease and the responsibility of the medical specialists would increase.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".