Do Hospital Standardized Mortality Ratios Measure Patient Safety? HSMRs in the Winnipeg Regional Health Authority
Bibliographic record
Abstract
The Canadian Institute for Health Information began publishing hospital standardized mortality ratio (HSMR) data for select Canadian hospitals in November 2007. This paper describes the experience of the Winnipeg Regional Health Authority in assessing the validity of the HSMR through statistical analysis, coding definitions and chart audits. We found a lack of empirical evidence supporting the use of the HSMR in measuring reductions in preventable deaths. We also found that limitations in standardization as well as differences in palliative care coding and place of death make inter-facility comparisons of HSMRs invalid. The results of our chart audit show that the HSMR is not a sensitive measure of adverse events as defined by "unexpected death" in the Canadian Adverse Events Study. It should not be viewed as an important indicator of patient safety or quality of care. We discuss the cumulative sum statistic as an alternative to the HSMR in monitoring in-hospital mortality.
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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 | 0.020 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".