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Journal Article
Unraveling the effects of DIP payment reform on inpatient healthcare: insights into impacts and challenges
2024
Overview
Background
The Diagnosis-Intervention Packet (DIP) payment system, initiated by China's National Healthcare Security Administration, is designed to enhance healthcare efficiency and manage rising healthcare costs. This study aims to evaluate the impact of the DIP payment reform on inpatient care in a specialized obstetrics and gynecology hospital, with a focus on its implications for various patient groups.
Methods
To assess the DIP policy's effects, we employed the Difference-in-Differences (DID) approach. This method was used to analyze changes in total hospital costs and Length of Stay (LOS) across different patient groups, particularly within select DIP categories. The study involved a comprehensive examination of the DIP policy's influence pre- and post-implementation.
Results
Our findings indicate that the implementation of the DIP policy led to a significant increase in both total costs and LOS for the insured group relative to the self-paying group. The study further identified variations within DIP groups both before and after the reform. In-depth analysis of specific disease groups revealed that the insured group experienced notably higher total costs and LOS compared to the self-paying group.
Conclusions
The DIP reform has led to several challenges, including upcoding and diagnostic ambiguity, because of the pursuit of higher reimbursements. These findings underscore the necessity for continuous improvement of the DIP payment system to effectively tackle these challenges and optimize healthcare delivery and cost management.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ China
/ Diagnosis-intervention packet (DIP)
/ Difference-in-differences (DID)
/ Disease
/ Female
/ Finance
/ Health Care Reform - economics
/ Hospital Costs - statistics & numerical data
/ Hospitalization - statistics & numerical data
/ Hospitals, Gynecologic and obstetric
/ Humans
/ Inpatients - statistics & numerical data
/ Length of Stay - statistics & numerical data
/ Medicine
/ Patients
