Improving Mortality Through a Multihospital, Collaborative Quality Improvement Project
Bibliographic record
Abstract
Improving hospital mortality is a key focus of quality and safety efforts at both the local and national level. Structured interventions can assist organizations in determining whether interventional efforts have led to sustained improvement. The PARiHS framework (Promoting Action on Research Implementation in Health Services) can assist organizations in implementing research into practice. This study investigates the use of the PARiHS framework in implementing a multihospital quality improvement project aimed at improving observed-to-expected mortality as measured by Vizient's Clinical Data Base (CDB). Structured interventions during the study period included mortality reviews, clinical documentation improvement opportunities, educational webinars, training and support in the use of CDB to explore ongoing opportunities for mortality improvement and quarterly reports to each participating hospital's leadership team on their performance. Data were gathered from an improvement collaborative in the Upper Midwest, which comprised 34 hospitals, of which 17 participated in the intervention. Measurement occurred from Quarter 4 2016 through Quarter 3 2020 and consisted of a preintervention, intervention, and postintervention period. Although both participating and nonparticipating hospitals achieved a significant reduction in their mortality observed-to-expected ratio from the preintervention period through the postintervention period, the participating hospitals achieved a greater reduction in their observed-to-expected mortality ratio ( P < 0.0004). In addition, the participating hospitals achieved a relative 21% improvement in the mortality domain rank of the Vizient Quality & Accountability Study.
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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 | 0.042 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".