Comparing Methods to Identify which Adult Emergency Department Visits Might be Avoided: A Retrospective Analysis of the MIMIC-IV-ED Database
Bibliographic record
Abstract
Abstract Background There are multiple ways to define and identify potentially avoidable emergency department visits, making it difficult to estimate their frequency accurately. In this study, we compared the proportions and characteristics of potentially avoidable ED visits identified using three commonly used algorithms. Methods We analyzed the publicly available Medical Information Mart for Intensive Care IV Emergency Department dataset (MIMIC-ED) to estimate the proportions and characteristics of potentially avoidable ED visits identified using i) the eventual discharge diagnosis ( Diagnosis ), ii) the use of hospital resources ( Resources ), and iii) the triage acuity score assigned to the patient during emergency department triage ( Triage-Acuity ). We found that the proportions and characteristics of potentially avoidable ED visits were affected by the algorithm used to identify them. Results The proportion of visits identified as potentially avoidable differed significantly between algorithms (2 - 21%), and few visits (<2%) were identified as potentially avoidable by all three algorithms. Presenting complaints, discharge diagnoses, and patient demographics were all similarly affected. Conclusion Methods for identifying potentially avoidable ED visits are not interchangeable. Choosing the appropriate definition and classification method will require researchers to carefully consider the types of visits and the characteristics of patients they wish to identify.
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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 | 0.025 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".