Physician Practice Style and Healthcare Costs: Evidence from Emergency Departments
Bibliographic record
Abstract
We study healthcare operations of emergency departments (EDs) by examining the practice styles and skills of ED physicians. Our data include all residents of Montreal, Canada, with an initial ED visit in Montreal during a nine-month period. For each visit, our data record the initial treating hospital, ED physician, ED billed expenditures, and all interactions with the health system within the subsequent 90 days. Physicians in Montreal rotate across shifts between simple and difficult cases, implying a quasi-random assignment of patients to physicians within an ED. We consider three medical conditions that present frequently in the ED and for which mistreatment may have dramatic consequences—angina, appendicitis, and transient ischemic attacks—jointly examining diagnostic and disposition skills. To control for variation in diagnosis, our sample for each condition consists of patients with a broader set of symptoms and signs potentially indicative of the condition. Separately by condition, we regress healthcare usage and cost measures on indicators for physicians to estimate the skill and practice style of each physician. We then evaluate the variation across physicians in their practice style and skills and the correlations between different measures of skill and practice style. We find significant variation across physicians in their practice styles and skills. We also find that physicians with costly practice styles often have worse outcomes in terms of more ED revisits and more hospitalizations. Finally, the practice styles and skills of physicians correlate positively across the three conditions that we consider. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: Gowrisankaran acknowledges funding from the Center for Management Innovations in Healthcare at the University of Arizona. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4544 .
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".