Failure to Rescue in Safety-Net Hospitals
Bibliographic record
Abstract
IMPORTANCE: Failure to rescue (FTR), the mortality rate among surgical patients with complications, is an emerging quality indicator. Hospitals with a high safety-net burden, defined as the proportion of patients covered by Medicaid or uninsured, provide a disproportionate share of medical care to vulnerable populations. Given the financial strains on hospitals with a high safety-net burden, availability of clinical resources may have a role in outcome disparities. OBJECTIVES: To assess the association between safety-net burden and FTR and to evaluate the effect of clinical resources on this relationship. DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort of 46,519 patients who underwent high-risk inpatient surgery between January 1, 2007, and December 31, 2010, was assembled using the Nationwide Inpatient Sample. Hospitals were divided into the following 3 safety-net categories: high-burden hospitals (HBHs), moderate-burden hospitals (MBHs), and low-burden hospitals (LBHs). Bivariate and multivariate analyses controlling for patient, procedural, and hospital characteristics, as well as clinical resources, were used to evaluate the relationship between safety-net burden and FTR. MAIN OUTCOMES AND MEASURES: FTR. RESULTS: Patients in HBHs were younger (mean age, 65.2 vs 68.2 years; P = .001), more likely to be of black race (11.3% vs 4.2%, P < .001), and less likely to undergo an elective procedure (39.3% vs 48.6%, P = .002) compared with patients in LBHs. The HBHs were more likely to be large, major teaching facilities and to have high levels of technology (8.6% vs 4.0%, P = .02), sophisticated internal medicine (7.7% vs 4.3%, P = .10), and high ratios of respiratory therapists to beds (39.7% vs 21.1%, P < .001). However, HBHs had lower proportions of registered nurses (27.9% vs 38.8%, P = .02) and were less likely to have a positron emission tomographic scanner (15.4% vs 22.0%, P = .03) and a fully implemented electronic medical record (12.6% vs 17.8%, P = .03). Multivariate analyses showed that HBHs (adjusted odds ratio, 1.35; 95% CI, 1.19-1.53; P < .001) and MBHs (adjusted odds ratio, 1.15; 95% CI, 1.05-1.27; P = .005) were associated with higher odds of FTR compared with LBHs, even after adjustment for clinical resources. CONCLUSIONS AND RELEVANCE: Despite access to resources that can improve patient rescue rates, HBHs had higher odds of FTR, suggesting that availability of hospital clinical resources alone does not explain increased FTR 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".