Quality and financial outcomes from gainsharing for inpatient admissions: A three‐year experience
Bibliographic record
Abstract
BACKGROUND: Gainsharing is a way to provide incentives to physicians to decrease hospital costs without compromising quality. METHODS: A pay-for-performance program was instituted over a three-year period from July 2006 to June 2009. Baseline length of stay (LOS) and case costs were developed during the year prior to the inception of the program. Best practice norms (BPNs) were established at the top 25th percentile of physicians for each all patient refined (APR)-diagnosis related group (DRG). Hospital costs were analyzed in several areas, including operating room charge (OR), supplies and implants, nursing and per-diem room costs. Payments were based upon case level performance compared to BPN's and the physician's historic performance. Eligible cases included commercial insurance only for the first 2 years but Medicare cases were included after October 2008 resulting from a Centers for Medicare and Medicaid Services (CMS)-approved demonstration project. Payments to physicians required meeting quality thresholds, including chart completion, and compliance with core measures. RESULTS: A total of 184 (54%) physicians enrolled into the program. There was a $25.1 million reduction in hospital costs during the 3 years ($16 million from participating and $9.1 million from non-participating physicians, P < 0.01). Most cost reductions were attributed to reduced LOS and reductions in medical supply costs. Total physician payouts were over $2 million (average $1,866 per quarter). Delinquent medical records decreased from an average of 43% in the second quarter 2006 to 30% (P < 0.0001) in the second quarter 2009. Quality measures improved during the study period but not by a statistical significance. CONCLUSIONS: Gainsharing provided an incentive for physicians to reduce hospital costs while maintaining hospital quality.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".