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: We study the effects of emergency department (ED) physician workload on healthcare system utilization after the patient leaves the ED. Further, we explore the mediating effects of care intensity in the ED on post-ED care use. Academic/practical relevance: ED crowding has been a pressing concern in healthcare systems in the United States and other developed countries. As such, many researchers have studied its effects on outcomes within the ED. In contrast, we present novel results regarding the impacts of ED crowding on system performance outside the ED—specifically, on post-ED care utilization. Methodology: We utilize a data set 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. Results: We find that there is an increasing concave relationship between ED physician workload and post-ED care use. When ED workload increases from its fifth 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 afterward. 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 behaviors under ED crowding impact 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. Funding: Support for this research was provided by the University of Alberta Endowment Fund for the Future: Support for the Advancement of Scholarship, Canadian Utilities Faculty Fellowship, and the University of Wisconsin–Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation. Brian Patterson’s contribution to this research was supported by funding from the Agency for Healthcare Research and Quality (AHRQ) [Grant K08HS024558]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1110 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".