Causal Analysis of Emergency Department Delays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Improvement teams make causal inferences, but the methods they use are based on statistical associations. This article shows how data and statistical models can be used to help improvement teams make causal inferences and find the root causes of problems. METHODS: This article uses attribution data, competing risk survival analysis, and Bayesian network probabilities to analyze excessive emergency department (ED) stays within one hospital. We use data recorded by ED clinicians that attributed the cause of excessive ED stays to 23 causes for the 70 049 ED visits between March 2011 and April 2014. We use competing risk survival analysis to identify contribution of each cause to the delay. We use Bayesian network models to analyze interaction among different causes of excessive stays and find the root causes of this problem. RESULTS: This article shows the utility of causal analysis to help improvement teams focus on the root causes of problems. For the example analyzed in the article, most causes for patients' excessive ED stays were related to the hospital operations outside the ED. Therefore, improvement projects inside the ED such as expanding ED, increasing staff at the ED, or improving operations are less likely to have a positive impact on reducing excessive ED stays. On the contrary, interventions that improve hospital occupancy (better discharge, expansion of beds, etc) or improve laboratory response times are more likely to result in positive outcomes.
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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 | 0.001 |
| 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 it