Identifying payment-driven patient selection using machine-learning-predicted cost risk: evidence from neonatal care in China

Abstract

BackgroundThe global pursuit of Universal Health Coverage (UHC) has spurred a worldwide shift towards case-based payment systems to control costs and expand health coverage. However, payment reform without adequate risk adjustment may incentivize hospitals to avoid high-cost-risk patients, potentially threatening health equity. China’s innovative Diagnosis-Intervention Packet (DIP) reform represents a major, globally-relevant experiment in implementing a simplified case-based system at scale. This study evaluates its impact on patient selection for a vulnerable pediatric population.MethodsThe study included 157,739 neonatal jaundice hospitalizations, recorded between January 2018 and Septemb..