Association of Hospital Spending Intensity With Mortality and Readmission Rates in Ontario Hospitals
Bibliographic record
Abstract
CONTEXT: The extent to which better spending produces higher-quality care and better patient outcomes in a universal health care system with selective access to medical technology is unknown. OBJECTIVE: To assess whether acute care patients admitted to higher-spending hospitals have lower mortality and readmissions. DESIGN, SETTING, AND PATIENTS: The study population comprised adults (>18 years) in Ontario, Canada, with a first admission for acute myocardial infarction (AMI) (n = 179,139), congestive heart failure (CHF) (n = 92,377), hip fracture (n = 90,046), or colon cancer (n = 26,195) during 1998-2008, with follow-up to 1 year. The exposure measure was the index hospital's end-of-life expenditure index for hospital, physician, and emergency department services. MAIN OUTCOME MEASURES: The primary outcomes were 30-day and 1-year mortality and readmissions and major cardiac events (readmissions for AMI, angina, CHF, or death) for AMI and CHF. RESULTS: Patients' baseline health status was similar across hospital expenditure groups. Patients admitted to hospitals in the highest- vs lowest-spending intensity terciles had lower rates of all adverse outcomes. In the highest- vs lowest-spending hospitals, respectively, the age- and sex-adjusted 30-day mortality rate was 12.7% vs 12.8% for AMI, 10.2% vs 12.4% for CHF, 7.7% vs 9.7% for hip fracture, and 3.3% vs 3.9% for CHF; fully adjusted relative 30-day mortality rates were 0.93 (95% CI, 0.89-0.98) for AMI, 0.81 (95% CI, 0.76-0.86) for CHF, 0.74 (95% CI, 0.68-0.80) for hip fracture, and 0.78 (95% CI, 0.66-0.91) for colon cancer. Results for 1-year mortality, readmissions, and major cardiac events were similar. Higher-spending hospitals had higher nursing staff ratios, and their patients received more inpatient medical specialist visits, interventional (AMI cohort) and medical (AMI and CHF cohorts) cardiac therapies, preoperative specialty care (colon cancer cohort), and postdischarge collaborative care with a cardiologist and primary care physician (AMI and CHF cohorts). CONCLUSION: Among Ontario hospitals, higher spending intensity was associated with lower mortality, readmissions, and cardiac event rates.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".