Comparing risk adjustment estimation methods under data availability constraints.

ABSTRACT: The Italian National Healthcare Service relies on per capita allocation for healthcare funds, despite having a highly detailed and wide range of data to potentially build a complex risk-adjustment formula. However, heterogeneity in data availability limits the development of a national model. This paper implements and ealuates machine learning (ML) and standard risk-adjustment models on different data scenarios that a Region or Country may face, to optimize information with the most predictive model. We show that ML achieves a small but generally statistically insignificant improvement of adjusted R² and mean squared error with fine data granularity compared to linear regression, while in coarse granularity and poor range of variables scenario no differences were observed. The a

SUBMITTER: Iommi M 

PROVIDER: S-EPMC9320950 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature