Implementation of the Maryland Global Budget Revenue Model and Variation in the Expenditures and Outcomes of Surgical Care
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of the Global Budget Revenue (GBR) program on outcomes after surgery. BACKGROUND: There is limited data summarizing the effect of the GBR program on surgical outcomes as compared with traditional fee-for-service systems. METHODS: The Medline, Embase, Scopus, and Web of Science databases were used to conduct a systematic literature search on April 5, 2022. We identified full-length reports of comparative studies involving patients who underwent surgery in Maryland after implementation of the GBR program. A random effects model calculated the overall pooled estimate for each outcome which included complications, rates of readmission and mortality, length of stay, and costs. RESULTS: Fourteen studies were included in the qualitative synthesis, with 8 unique studies included in the meta-analysis. Our analytical sample was comprised of 170,011 Maryland patients, 78,171 patients in the pre-GBR group, and 91,840 patients in the post-GBR group. The pooled analysis identified modest reductions in costs [standardized mean difference (SMD) -0.34; 95% CI, -0.42, -0.25; P <0.001], complications [odds ratio (OR): 0.57; 95% CI, 0.36-0.92, P =0.02], readmission (OR: 0.78; 95% CI, 0.72-0.85, P <0.001), mortality (OR: 0.58; 95% CI, 0.47-0.72, P <0.001), and length of stay (standardized mean difference: -0.26; 95% CI, -0.32, -0.2, P <0.001) after surgery. CONCLUSIONS: Implementation of the GBR program is associated with improved outcomes and reductions in costs among Maryland patients who underwent surgical procedures. This is particularly salient given the increasing need to disseminate and scale population-based payment models that improve patient care while controlling health care costs.
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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 | 0.035 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".