Bayesian Hierarchical Model in Ghanaian District Hospitals: Evaluating Clinical Outcomes

Bayesian hierarchical models have been increasingly applied in various fields to analyse complex data hierarchically. A Bayesian hierarchical model was developed and implemented to assess clinical outcomes across different hospital districts. Uncertainty quantification was conducted using robust standard errors. The analysis revealed significant variability in patient recovery rates between districts, with a notable difference in the 20% range for certain medical conditions. Bayesian hierarchical models provide a nuanced approach to evaluating clinical outcomes and system performance in district hospitals. The model's ability to account for spatial variation is particularly useful. The findings suggest that targeted interventions should be implemented based on the identified disparities, aiming to improve patient recovery rates across all districts. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.