Factors determining the adherence to antimicrobial guidelines and the adoption of computerised decision support systems by physicians: A qualitative study in three European hospitals
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Antimicrobial stewardship (AMS) programs aim to optimize antibiotic use and reduce inappropriate prescriptions through a panel of interventions. The implementation of clinical guidelines is a core strategy of AMS programs. Nevertheless, their dissemination and application remain low. Computerised decision support systems (CDSSs) offer new opportunities for semi-automated dissemination of guidelines. This qualitative study aimed at gaining an in-depth understanding of the determinants of adherence to antimicrobial prescribing guidelines and CDSSs adoption and is part of a larger project, the COMPASS trial, which aims to assess a CDSS for antimicrobial prescription. The final objective of this qualitative study is to 1) provide insights from end-users to assist in the design of the COMPASS CDSS, and to 2) help with the interpretation of the quantitative findings of the randomised controlled trial assessing the COMPASS CDSS, once data will be analysed. METHODS: We conducted semi-structured individual interviews among in-hospital physicians in two hospitals in Switzerland and one hospital in France. Physicians were recruited by convenience sampling and snowballing until data saturation was achieved. RESULTS: Twenty-nine physicians were interviewed. We identified three themes related to the potential barriers to guideline adherence: 1) insufficient clarity, accessibility and applicability of guidelines, 2) need of critical thinking skills to adhere to guidelines and 3) impact of the team prescribing process and peers on physicians in training. As to the perception of CDSSs, we identified four themes that could affect their adoption: 1) CDSSs are perceived as time-consuming, 2) CDSSs could reduce physicians' critical thinking and professional autonomy and raise new medico-legal issues, 3) effective CDSSs would require specific features, such as ease of use and speed, which affect usability and 4) CDSSs could improve physicians' adherence to guidelines and patient care. DISCUSSION: CDSSs have the potential to overcome several barriers for adherence to guidelines by improving accessibility and providing individualised recommendations backed by patient data. When designing CDSSs, mixed clinical and information technology teams should focus on user-friendliness, ergonomics, workflow integration and transparency of the decision-making process.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".