Pennsylvania Rural Health Model: Early Impacts on Potentially Avoidable Hospitalizations
Bibliographic record
Abstract
Scoring is only possible in the semifinals hall Abstract: Background The Pennsylvania Rural Health Model (PARHM) is a novel alternative payment model (APM) that began in 2019 and is slated to run through 2024. It is designed to test whether hospital global budgets and transformation plans can improve the financial viability of rural hospitals and the health of their patients. The expectation is that, when hospitals operate on a prospective budget, they have an incentive to reduce potentially avoidable hospitalizations (PAH) to control costs. Therefore, our objective was to assess the early impacts of PARHM on PAH for ambulatory care sensitive conditions. Methods We conducted a retrospective cohort study using a difference-in-differences (DID) with multiple time periods approach. Hospitals joined PARHM in three cohorts at different points in time (January 2019, 2020, and 2021), creating a natural policy experiment to study this program’s effects. The treated group included HSAs of 18 treated hospitals and the control group included HSAs of 43 eligible but not participating hospitals. We used visit-level inpatient discharge data from the Pennsylvania Health Care Cost Containment Council between 1/1/2016 and 5/31/2022. We measured PAH using the Agency for Healthcare Research and Quality Prevention Quality Indicators algorithm. Analyses were conducted at the hospital service area (HSA)-quarter level. We fit models using a doubly robust DID estimator that accounted for staggered treatment timing. All models were weighted using propensity scores and population size in each HSA. Results The introduction of PARHM was associated with 9.71 fewer PAHs per 100k residents per quarter (95% CI: 0.21 to 19.21) overall. This represents a 2.25% relative reduction in treated vs. control HSAs. However, when cohorts were analyzed separately, only PARHM Cohort 1 HSAs consistently a showed statistically significant (p<0.05) relative decreases in PAHs across overall, acute, chronic, and diabetes composite measures for PAH compared to changes in the control group. Results were similar across sensitivity and subgroup analyses. Conclusion Our findings on the early impacts of PARHM suggest that it is associated with fewer PAHs. Although, heterogeneous treatment effects by cohort were observed. This study addresses an important gap in the current literature regarding the impact of PARHM on health care utilization. Findings can be used to evaluate the success of PARHM and inform the development and implementation of future APMs across rural settings.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".