Physician Financial Incentives to Reduce Unplanned Hospital Readmissions: A Propensity Score Weighted Cohort Study
Bibliographic record
Abstract
BACKGROUND: Unplanned hospital readmissions are associated with adverse patient outcomes and substantial healthcare costs. It remains unknown whether physician financial incentives for enhanced discharge planning can reduce readmission risk. METHODS: In 2012, policymakers in British Columbia, Canada, introduced a $75 fee-for-service physician payment to incentivize enhanced discharge planning (the "G78717" fee code). We used population-based administrative health data to compare outcomes among G78717-exposed and G78717-unexposed patients. We identified all nonelective hospitalizations potentially eligible for the incentive over a 5-year study interval. We examined the composite risk of unplanned readmission or death and total direct healthcare costs accrued within 30 days of discharge. Propensity score overlap weights and adjustment were used to account for differences between exposed and unexposed patients. RESULTS: A total of 5262 of 24,787 G78717-exposed and 28,096 of 136,541 unexposed patients experienced subsequent unplanned readmission or death, suggesting exposure to the G78717 incentive did not reduce the risk of adverse outcomes after discharge (crude percent, 21.1% vs 20.6%; adjusted odds ratio, 0.97; 95% CI, 0.93-1.01; P = .23). Mean direct healthcare costs within 30 days of discharge were $3082 and $2993, respectively (adjusted cost ratio, 1.00; 95% CI, 0.95-1.05; P = .93). CONCLUSIONS: A physician financial incentive that encouraged enhanced hospital discharge planning did not reduced the risk of readmission or death, and did not significantly decrease direct healthcare costs. Policymakers should consider the baseline prevalence and effectiveness of enhanced discharge planning, the magnitude and design of financial incentives, and whether auditing of incentivized activities is required when implementing similar incentives elsewhere. TRIAL REGISTRATION: ClinicalTrials.gov ID, NCT03256734.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".