Antipsychotic, benzodiazepine and Z-drug prescriptions in a Swiss hospital network in the Choosing Wisely and COVID-19 eras: a longitudinal study
Bibliographic record
Abstract
AIMS OF THE STUDY: Physicians frequently prescribe antipsychotics off-label to treat, among others, insomnia and anxiety. The Swiss “smarter medicine – Choosing Wisely” campaign has tried to raise awareness about the risks and to limit benzodiazepine and Z-drug prescriptions. In the Italian-speaking part of Switzerland, our network of public hospitals joined the campaign with the aim of avoiding unnecessary benzodiazepine and Z-drug treatments, with prescription monitoring, benchmarking and educational contributions. Considering the risks of a possible shift towards the prescription of antipsychotics, and aware of the potential role of the COVID-19 pandemic, we decided to analyse the prescription trends of antipsychotics and benzodiazepines/Z-drugs before, during (2016–2017) and after the intervention. METHODS: For this longitudinal study, we reactivated a continuous monitoring of inpatient benzodiazepine/Z-drug and antipsychotics prescriptions/deprescriptions, paused in 2018 after the end of the internal Choosing Wisely campaign, based on routinely collected observational health data. We screened all demographic, administrative and prescription data of patients admitted to the internal medicine department of the four teaching hospitals (H1-H4) belonging to the EOC (Ente Ospedaliero Cantonale) network, from the fourth quarter of 2014 to the second quarter of 2023. RESULTS: We analysed 74,659 hospital stays (14,645 / 16,083 / 24,285 / 19,646 at hospitals H1 / H2 / H3 / H4 respectively). The mean (± SD) case mix (a metric that reflects the diversity, complexity and severity of the treated patients) and patient age were 1.08 ± 0.14 and 73 ± 2 years. 10.6% and 12.1% of patients received antipsychotics prior to admission and at discharge respectively (new prescriptions 3.3 ± 0.7%; deprescriptions 13.3 ± 5.2%). New prescriptions showed an upward trend, with +0.20% per year (p <0.001). Patients admitted with ongoing antipsychotics therapy increased 0.36% per year (p <0.001). New benzodiazepine/Z-drug prescriptions showed a 0.20% per year decrease (p = 0.01). Patients admitted with ongoing benzodiazepine/Z-drug therapy decreased 0.32% per year (p <0.001). New antipsychotics prescriptions showed differences between hospitals, with H3 above and H2 below the average. CONCLUSIONS: The increase in antipsychotics quantitatively matched the decrease in benzodiazepine/Z-drug prescribing, suggesting a shift from one to the other sedative therapy. The same trend was visible in the ongoing prescriptions at admission revealing a similar out-of-hospital approach. This suggests a change in sedative prescribing strategy rather than the choice of alternative, non-pharmacological approaches. Furthermore, the variation between similar services of different hospitals points out the consequences of local prescribing cultures and the importance of continuously monitoring and benchmarking medication prescriptions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".