Does What Happens in the ED Stay in the ED? The Effects of Emergency Department Physician Workload on Post-ED Care Use
Bibliographic record
Abstract
Problem Definition: Emergency department (ED) crowding has been a persistent concern in healthcare systems in the U.S. and other developed countries. As such, many researchers have studied its effects on outcomes within the ED. We study the effects of ED crowding on system performance outside the ED, specifically post-ED care utilization. Further, we explore the mediating effects of care intensity in the ED on post-ED care use. Methodology/Results: We utilize a dataset assembled from more than four years of microdata from a large U.S. hospital and exhaustive billing data in an integrated health system. We use count models and instrumental variable analyses to answer the proposed research questions.We show that there is an increasing concave relationship between ED physician workload and post-ED care use. When ED workload increases from its 5th percentile to the median, the number of post-discharge care events (i.e., medical services) for patients who are discharged home from the ED increases by 5% and it is stable afterwards. Further, we identify physician test ordering behavior as a mechanism for this effect: when the physician is busier, she responds by ordering more tests for less severe patients. We document that this “extra” testing generates “extra” post-ED care utilization for these patients. Managerial Implications: This paper contributes new insights on how physician and patient behavior under ED crowding impacts a previously unstudied system performance measure: post-ED care utilization. Our findings suggest that prior studies estimating the cost of ED crowding underestimate the true effect, as they do not consider the “extra” post-ED care utilization.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".