Monographic Study
The big data diagnosis-intervention packet payment method: experience from Shanghai and Guangzhou
Xu Su, Wu Jinglei, Xie Hua, Lin Li, Zeng Qian, Cui Xin, Xuan Jianwei, Ying Xiaohua, Yang Yujia, Ying Yazhen
Published 2021-03-02
Cite as Chin J Hosp Admin, 2021, 37(3): 186-190. DOI: 10.3760/cma.j.cn111325-20201009-02012
Abstract
Medical insurance payment model is transforming from project-based purchases to service bundle-based strategic purchases. The new form of bundled purchases should found on a scientifically-led design process of such bundles. The core to bundled purchase would be the payment standard, and the key to its success would be process control. Establishment of such a foundation, a core, and a key, would promote the current price standards, and lead service providers to a standardized medical service standard, so as to ensure a precise rewarding system of payment and service. The big data diagnosis-intervention packet(DIP)is able to fulfill mentioned ambitions by integrating insurance payment and supervision into one management. DIP is a full-process payment mode that encompasses pre-service estimation, in-service process control, post-service grading, and resource allocation. It is an innovative practice in line with China′s national conditions for the modern governance of medical security and medical services.
Key words:
Big data; Diagnosis-intervention packet; Market mechanism; Prediction model; Process control
Contributor Information
Xu Su
Shanghai Municipal Health Commission, Shanghai 200125, China
Wu Jinglei
Shanghai Municipal Health Commission, Shanghai 200125, China
Xie Hua
Shanghai Information Center for Health, Shanghai 200040, China
Lin Li
Guangzhou Municipal Healthcare Security Administration, Guangzhou 510030, China
Zeng Qian
Guangzhou Municipal Healthcare Security Administration, Guangzhou 510030, China
Cui Xin
Shanghai Information Center for Health, Shanghai 200040, China
Xuan Jianwei
School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510275, China
Ying Xiaohua
School of Public Health, Fudan University, Shanghai 200433, China
Yang Yujia
Shanghai Information Center for Health, Shanghai 200040, China
Ying Yazhen
National Institute of Healthcare Security, Capital Medical University, Beijing 100037, China