Improved hospital safety performance and reduced medicolegal risk: an ecological study using 2 Canadian databases
Bibliographic record
Abstract
BACKGROUND: Few empirical studies have validated the relation between medicolegal risk and hospital patient safety performance. We sought to determine whether there was a relation between in-hospital patient safety events and medicolegal cases involving Canadian physicians. METHODS: In this ecological study, we used Poisson regression to compare data from the Canadian Institute for Health Information's Discharge Abstract Database and the database of the Canadian Medical Protective Association (CMPA) of medicolegal cases over 10 years (2005/06 to 2014/15). We identified incidents and cases based on 15 Agency for Healthcare Research and Quality patient safety indicators within the Canadian Institute for Health Information and CMPA data sets. We performed subgroup analyses for obstetrical and surgical cases. RESULTS: = 15 180) (parameter estimate 1.15, 95% confidence interval [CI] 0.4 to 1.9). This association suggests that, on average, a 10% decrease in events would correspond to a decrease of 11% in medicolegal cases. The degree of positive association varied by practice type, with obstetrics (97 982 patient safety indicator events, 865 cases) showing a 25% decrease in medicolegal cases for every 10% decrease in events (parameter estimate 2.9, 95% CI 0.5 to 5.3) and surgery (168 886 patient safety indicator events, 4568 cases) showing a decrease of 9% for every 10% decrease in events (parameter estimate 0.9, 95% CI 0.2 to 1.7). INTERPRETATION: The statistically significant positive association between patient safety indicator events and medicolegal cases quantifies a relation between patient safety and physician medicolegal risk in Canadian hospitals. This suggests new, practical uses for both medicolegal and patient safety indicator data in system-level quality-improvement efforts.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".