Reducing unnecessary sedative-hypnotic use among hospitalised older adults
Bibliographic record
Abstract
BACKGROUND: Benzodiazepines and sedative hypnotics (BSH) have numerous adverse effects that can lead to negative outcomes, particularly in vulnerable hospitalised older adults. At our institution, over 15% of hospitalised older adults are prescribed sedative-hypnotics inappropriately. Of these prescriptions, 87% occurred at night to treat insomnia and almost 20% came from standard admission order sets. METHODS: We conducted a time-series study from January 2015 to August 2016 among medical and cardiology inpatients following the implementation in August 2015 of a sedative reduction bundle (education, removal of BSH from available admission order sets and non-pharmacological strategies to improve sleep). Preintervention period was January-July 2015 and postintervention period was August 2015-August 2016. A surgical ward served as control. Primary outcome was the proportion of BSH-naive (not on BSH prior to admission) patients 65 years or older discharged from medical and cardiology wards who were prescribed any new BSH for sleep in hospital. Data were analysed on statistical process control (SPC) p-charts with upper and lower limits set at 3δ using standard rules. Secondary measures included Patient-reported Median Sleep Quality scores and rates of fall and sedating drug prescriptions that may be used for sleep (dimenhydrinate). RESULTS: During the study period, there were 5805 and 1115 discharges from the intervention and control units, respectively. From the mean baseline BSH prescription rate of 15.8%, the postintervention period saw an absolute reduction of 8.0% (95% CI 5.6% to 10.3%; p<0.001). Adjusted for temporal trends, the intervention produced a 5.3% absolute reduction in the proportion of patients newly prescribed BSH (95% CI 5.6% to 10.3%; p=0.002). BSH prescription rates remained stable on the control ward. Patient-reported measure of sleep quality, falls and use of other sedating medications remained unchanged throughout the study duration. CONCLUSION: A comprehensive intervention bundle was associated with a reduction in inappropriate BSH prescriptions among older inpatients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".