Impact of pay-for-performance on hospital readmissions in Lebanon: an ARIMA-based intervention analysis using routine data
Bibliographic record
Abstract
BACKGROUND: The objective of this paper was to estimate the impact of country-wide hospital pay-for-performance on readmissions for a set of common conditions in Lebanon. METHODS: This retrospective cohort study included all hospitalizations under the coverage of the Ministry of Public Health in Lebanon between 2011 and 2019. We calculated 30-day all-cause readmissions following general, pneumonia, cholecystectomy and stroke cases. We used an interrupted time series design, including the use of AutoRegressive Integrated Moving Average models. This nationwide study including 1,333,691 hospitalizations was undertaken in Lebanon, using hospitalizations at about 140 private and public hospitals contracted by the Ministry. The participants included citizens across all ages under the Ministry's coverage (52% of citizens). The intervention was the engagement of hospital leaders by the Ministry, informing them of the addition of a readmissions component to the ongoing pay-for-performance initiative. Engagement participants included hospital directors and managers, and the leadership of the Syndicate of Private Hospitals. The main outcome measure was age-adjusted monthly all-cause readmission rates for each of general, pneumonia, cholecystectomy and stroke cases. We also assessed for change in readmissions for three conditions not included in the intervention (myocardial infarction, cataract surgery and appendectomy). RESULTS: Across 2011-2019, the overall readmission rates were 6.00% (SD 0.24%) for general readmissions, 5.06% (SD 0.22%) for pneumonia, 2.54% (SD 0.16%) for cholecystectomy, and 6.55% (SD 0.25%) for stroke. Using ARIMA models we found a relative percentage decrease in mean monthly readmissions in the post-intervention period for cholecystectomy (5.9%; CI 0.1%-11.8%) and stroke (13.6%; CI 3.1%-24.2%). There was no evidence of intervention impact on pneumonia and general readmissions, both overall and among small, medium and large hospitals. There was also no evidence of change in non-P4P readmissions of myocardial infarction, cataract surgery and appendectomy. CONCLUSIONS: Including readmissions within pay-for-performance has the potential to improve hospital performance and patient outcomes, even in countries with more limited resources. Effects may vary across conditions, indicating the need for careful design and understanding of the particular context, both with respect to implementation and to evaluation of impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".