Impact of a smartphone application for appropriate antibiotic prescribing at three tertiary hospitals: an international, multicentre stepped-wedge cluster randomized trial
Bibliographic record
Abstract
OBJECTIVES: Smartphone applications (apps) for antibiotic prescribing have been studied in observational studies. Here, we assessed whether the use of a smartphone app increased appropriate antibiotic therapy (AAT) in hospitals. METHODS: An antibiotic stewardship app (Firstline.org) was populated with local guidelines and tested in a stepped-wedged cluster randomized trial in three hospitals in the Netherlands, Sweden, and Switzerland. Defined clusters were randomized per hospital for the intervention (use of app) or standard of care. Primary outcome was AAT assessed by chart review in 15 random patients per cluster per intervention period. Secondary outcomes included clinical outcomes and user analytics. A questionnaire identifying barriers to app use was disseminated. Multivariable multilevel logistic models with time periods as fixed effects to adjust for time trend and treatment as fixed effects were employed to estimate the odds ratio of treatment. RESULTS: Twelve clusters in the Netherlands (1085 patients) were included, 12 in Sweden (362 patients) and 8 in Switzerland (653 patients). Overall, AAT was not increased (2.0% [95% CI, -5.92% to 9.97%]) in the intervention arm compared with control across the three centres. Mean frequency of app use by cluster was associated with an AAT increase (1.9% [95% CI, 1.18-2.62%]) across study centres; 3.2% in the Netherlands (p < 0.01), 2.8% in Switzerland (p < 0.01), and remained similar in Sweden (0.4%; p 0.46). No difference was found for the other secondary outcomes. Main barriers for app use reported in the questionnaire were easily forgetting using the app and having other tools to help prescribing antibiotics. DISCUSSION: Overall, the introduction of a stewardship app did not significantly increase AAT, but a prespecified secondary analysis of app use frequency was associated with a small but significant improvement of AAT. Variable uptake of the app, coexisting routes to guidelines and the impact of the COVID-19 pandemic during the trial likely had an impact on the results. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, trial number NCT03793946.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".