Using newly linked hospital and population data to identify opportunities to reduce harms from sedative-hypnotic prescribing at population scale
Bibliographic record
Abstract
Objective and ApproachSedative-hypnotic medications are associated with harms such as delirium and falls, and may be overprescribed in hospitals. Measuring potentially inappropriate in-hospital sedative-hypnotic prescriptions requires distinguishing new initiations from pre-hospital prescriptions, but pre-hospital medications may not be captured in hospital data. To quantify physician- and hospital-level variation in sedative-hypnotic prescribing, we use linked data between GEMINI (which includes EMR data from >1.8 million admissions across 30 hospitals, approximately 60% of medical inpatient beds in Ontario) and ICES (which houses administrative healthcare databases, including outpatient prescriptions for adults ≥65 years). We will validate whether data in hospital clinical notes or pharmacy records can reliably identify pre-hospital prescriptions. ResultsUsing provincial health insurance number, 98.1% of GEMINI records could successfully be linked deterministically to ICES. Our exploratory sedative-hypnotic cohort included 90,806 general medicine patients treated by 437 physicians from 17 hospitals across Ontario from 2021-2022. Median age was 72 years and 48.7% were female. The proportion of patients receiving a new sedative-hypnotic prescription varied markedly across hospitals (median 0.21, range 0.04-0.41) and across physicians within hospitals (median within-hospital difference between lowest and highest prescriber: 0.18, range of within-hospital differences 0.07-0.67). Validation of new rather than continued prescriptions is underway. Full results will be ready for presentation at the conference. ConclusionSedative-hypnotic prescribing is common and highly variable in hospitals across Ontario, indicating an opportunity to improve patient safety. Implications Based on these analyses, the province of Ontario has made reducing in-hospital sedative-hypnotic prescribing a provincial priority for 2023-2024.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".