Appropriateness, effectiveness and safety of care delivered in Canadian hospitals: a longitudinal assessment on the utility of publicly reported performance trend data between 2012–2013 and 2016–2017
Bibliographic record
Abstract
OBJECTIVES: To assess the utility of publicly reported performance trend results of Canadian hospitals (by hospital size/type and jurisdiction). DESIGN: Longitudinal observational study. SETTING: 489 hospitals in Canada between fiscal years 2012-2013 and 2016-2017. PARTICIPANTS: Analysis focused on indicator results of individual Canadian hospitals. PRIMARY AND SECONDARY OUTCOMES: Eight outcome indicators of hospital performance: in-hospital mortality (2), readmissions (4) and adverse events (2). Performance trend outcomes of improving, weakening or no change over time. Comparators in performance by hospital size/type of above, below or same as average. RESULTS: At the national level, between 2012-2013 and 2016-2017, Canadian hospitals largely reduced in-hospital mortality: hospital deaths (hospital standardised mortality ratio) -9%; hospital deaths following major surgery -11.1%. Conversely, readmission to hospital increased nationwide: medical 1.5%; obstetric 5%; patients aged 19 years and younger 4.6% and surgical 3%. In-hospital sepsis declined -7.1%. Approximately 10% of the 489 hospitals in this study had a trend of improving performance over time (n=49) in one or more indicators, and a similar number showed a weakening performance over time (n=52). Roughly half of the hospitals in this study (n=224) had no change in performance over time for at least four out of the eight indicators. No single hospital had an improving or weakening trend in more than two indicators. Teaching and larger-sized hospitals showed a higher ratio of improving performance compared with smaller-sized hospitals. CONCLUSIONS: Analysis of Canadian hospital performance through eight indicators shows improvement of in-hospital mortality and in-hospital sepsis, but rising rates of readmissions. Subdividing the analysis by hospital size/type shows greater instances of improvement in teaching and larger-sized hospitals. There is no clear pattern of a particular province/territory with a significant number of hospitals with improving or weakening trends. The overall assessment of trends of improving and weakening as presented in this study can be used more systematically in monitoring progress.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".