The diagnosis related group (DRG) was the most commonly used prospective hospital payment platform in developed countries. One of the major limitations of the DRG system is that the DRG grouping is not sufficiently homogeneous in benchmarking underlying resource needs. We developed a novel hospital payment and management system called Big Data Diagnosis & Intervention Packet (BD-DIP) by applying the similar case mix index (CMI) principles but the grouping is based on unique combination of ICD-10 and ICD-9 v3 codes. The initial prototype of BD-DIP was developed using hospital discharge records in Shanghai and then piloted in Guangzhou, China. The average coefficient of variation of the DB-DIP is about one-third smaller than the US DRG system. Results from the pilot evaluation showed that introduction of the BD-DIP lead to about 5% hospital budget savings and notable improvement in hospital care efficiency, including increased institutional CMI, lower admission rates, smaller variation in hospital charges, and lower patient cost-sharing burdens. The implementation of hospital monitoring tools resulted in identification of potential irregular practices to enable further auditing and investigation. The BD-DIP platform has a number of advantages over DRG-based payment models in terms of more homogeneous resource utilization within groups, design simplicity, dynamic in grouping, and reimbursement value in reflecting real-world treatment pathways and costs, and easy to implement.
Keywords: Big Data; Case Mix; Diagnosis-Related Groups; International Classification of Diseases; Novel Hospital Payment Platform.
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