The financial impact of hospital closures on surrounding hospitals
Bibliographic record
Abstract
Objective: To test whether hospital closures hurt or help surrounding hospitals financially. Do hospital closures improve marketefficiency or do they merely shift the least profitable patients to hospitals that can better cross-subsidize them?Methods: Using California hospital data from 2000 to 2011, the analysis employed random-effect and fixed-effect models to testfor a change in operating margin before and after a series of 2004, 2007 and 2009 hospital closures (the highest volume years forclosures). The main independent variable was each hospital’s predicted percent increase in patient volume due to absorption fromclosing hospitals. We used 5-digit zip code and DRG patient flow data to predict the number of patients each open hospital wouldabsorb from nearby hospital closures.Results: Hospitals experiencing the biggest increase in patient volume due to nearby hospital closings saw a drop in operatingmargin following those closures. This drop could not be explained by changes in payer mix or reimbursement type for thosepatients.Conclusions: Our results suggest that hospital closures are shifting high cost patients to open hospitals, not necessarily improving efficiency in the market.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".