Aiming for Zero Preventable Deaths: Using Death Review to Improve Care and Reduce Harm
Bibliographic record
Abstract
In 2005, our organization set a goal of zero preventable deaths by 2010--notionally a sound goal but extremely challenging to measure, monitor and evaluate. The development of an interdisciplinary Death and Adverse Event Review process has provided a measure and framework for action to decrease adverse events (AEs) that cause harm. Death and Adverse Event Review is a formal process in which trained reviewers consider patient deaths using a modified Global Trigger Tool to establish the presence of AEs or quality of care issues that may have potentially led to death or harm. When identified, these charts go to second-level review by a physician/interdisciplinary team to determine recommendations for actions to prevent future reoccurrences. Data have provided trending of system influences to patient safety. In 2008-2009, 1,817 deaths were reviewed and AE rates of 12.1% and 16.3% were identified. There were 422 AEs and 114 quality of care issues identified for follow-up. Of the 4.7% and 6.3% referred to the physician/interdisciplinary team for secondary review, 2.3% and 2.6% resulted in recommendations for improvement. In addition to local improvements, many system improvements have occurred as a result of the review, such as proposed minimum standards for physician documentation; a formal review of post-operative guidelines for patients with sleep apnea; and a working group to review nursing documentation, communication/follow-up of vital signs, fluid balance and pain management. The Death and Adverse Event Review process provides a new critical level of detail that supports continuous improvements to our care processes and ongoing progress toward our goal of zero preventable deaths.
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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 | 0.144 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".