Derivation and Validation of a Quality Indicator of Acute Care Length of Stay to Evaluate Trauma Care
Bibliographic record
Abstract
In Brief Objective: To derive and internally validate a quality indicator (QI) for acute care length of stay (LOS) after admission for injury. Background: Unnecessary hospital days represent an estimated 20% of total LOS implying an important waste of resources as well as increased patient exposure to hospital-acquired infections and functional decline. Methods: This study is based on a multicenter, retrospective cohort from a Canadian provincial trauma system (2005–2010; 57 trauma centers; n = 57,524). Data were abstracted from the provincial trauma registry and the hospital discharge database. Candidate risk factors were identified by expert consensus and selected for model derivation using bootstrap resampling. The validity of the QI was evaluated in terms of interhospital discrimination, construct validity, and forecasting. Results: The risk adjustment model explains 37% of the variation in LOS. The QI discriminates well across trauma centers (coefficient of variation = 0.02, 95% confidence interval: 0.011–0.028) and is correlated with the QI on processes of care (r = −0.32), complications (r = 0.66), unplanned readmissions (r = 0.38), and mortality (r = 0.35). Performance in 2005 to 2007 was predictive of performance in 2008 to 2010 (r = 0.80). Conclusions: We have developed a QI on the basis of risk-adjusted LOS to evaluate trauma care that can be implemented with routinely collected data. The QI is based on a robust risk adjustment model with good internal and temporal validity, and demonstrates good properties in terms of discrimination, construct validity, and forecasting. This QI can be used to target interventions to reduce LOS, which will lead to more efficient resource use and may improve patient outcomes after injury. We propose a quality indicator (QI) on the basis of risk-adjusted hospital length of stay to evaluate trauma care that can be implemented with routinely collected data. The QI has excellent internal and temporal validity and is correlated with QI on clinical processes and risk-adjusted mortality, readmission, and complication rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".