Hospital Restructuring in Ontario and its Impacts on Quality, Cost and Patient Welfare
Bibliographic record
Abstract
In this thesis, I investigate the impacts of hospital mergers in markets where hospitals compete primarily on quality. In Chapter 2, I use previous literature to discuss the potential impacts of hospital mergers, while in Chapter 3, I discuss a specific wave of mergers occurring in Ontario, Canada from 1997 through 2001 and describe the data I use to study these mergers. In Chapter 4, co-authored with Michael Green, we evaluate the effect of the hospital mergers on measures of hospital quality and cost. We use a matched differences-in-differences approach based on the construction of synthetic controls, and we compare hospital mergers involving the closure of acute care services at one or more hospital sites to hospital mergers without this feature. The results suggest that relative to the matched control group, both types of mergers improve quality but only those involving the closure of acute care services improve financial outcomes. In Chapter 5, co-authored with Eliane H. Barker and Michael Green, we use the same data at the patient level, focusing on the impact of mergers on patient welfare. We use pre-merger data and a patient choice model to estimate preferences parameters for travel distance to the hospital as well as for hospital quality and attributes, finding evidence that patients choose hospitals based on distance and quality. We then estimate the distribution of idiosyncratic shocks to utility, allowing us to control for unobservable utility in two counterfactual scenarios. In the first counterfactual, which represents the immediate effect of mergers, hospital sites that close acute care services during the merger wave are removed from the choice set. In the second, these hospital sites are still removed, and hospital quality and attributes are updated using post-merger data. The results suggest that once hospital quality has adjusted, mergers benefit some patients but harm others. For example, compared to urban patients, a greater proportion of rural patients are harmed by mergers. Taken together, these chapters suggest that hospital mergers have the potential to improve quality and cost but that there are trade-offs in terms of patient welfare.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".