Abstract
With the success of DRGs (Diagnosis Related Groups) in developing countries, this prospective payment system has been imported into China from the early 21(st) century. However, DRGs has been struggling and has made little progress since (its adoption in) 2004. This study contributes to the debate on how to bridge the pay-for-service (system/scheme) and DRGs (Diagnosis Related Groups) during the transitional period of payment reform in China. From 2008 to 2012, sixty regional general hospitals in Shanghai were divided into three groups according to their economic level, and one hospital was picked from each group randomly. After ranking of morbidity, 22130 patients with hypertension or coronary heart disease were chosen as sample. Using multiple linear regression analysis, the inter relationships between the total medical expenses of the inpatients, and age, gender of the inpatients, length of stay, region and economic level of the hospitals were examined. The main findings were (1) Age, LOS and the economic level of treatment location had a statistically significant impact on patients with hypertension or coronary heart disease. However, gender is only a significant factor to patients with coronary heart disease. The results suggested that age, LOS and the economic level of treatment location should be considered in formulating pricing standards for the hypertension patient group. Besides the above mentioned factors, gender should also be considered in formulating pricing standards for the coronary heart disease patient group. (2) Under the premise of limited resources, developing countries should first narrow down to screen for common and frequently occurring diseases, then study the key factors which affect the treatment cost of the diseases. Simplification of the DRGs standard- setting process based on standardized clinical pathways and accurate costing will greatly increase the efficiency of implementing DRGs in the developing world.
Highlights
IntroductionDiagnosis Related Groups (DRGs) are one of the most striking prospective payment systems around the world in recent years[1,2] As a classification system that groups patients according to (a) principal diagnosis, (b) type of treatment, (c) age, (d) surgery, and (e) discharge status,[3,4] prospective payment system have gradually become the principal means of reimbursing hospitals in most developed countries, such as US, Germany and Australia.[5]
Simplification of the Diagnosis Related Groups (DRGs) standard- setting process based on standardized clinical pathways and accurate costing will greatly increase the efficiency of implementing DRGs in the developing world
Diagnosis Related Groups (DRGs) are one of the most striking prospective payment systems around the world in recent years[1,2] As a classification system that groups patients according to (a) principal diagnosis, (b) type of treatment, (c) age, (d) surgery, and (e) discharge status,[3,4] prospective payment system have gradually become the principal means of reimbursing hospitals in most developed countries, such as US, Germany and Australia.[5]
Summary
Diagnosis Related Groups (DRGs) are one of the most striking prospective payment systems around the world in recent years[1,2] As a classification system that groups patients according to (a) principal diagnosis, (b) type of treatment, (c) age, (d) surgery, and (e) discharge status,[3,4] prospective payment system have gradually become the principal means of reimbursing hospitals in most developed countries, such as US, Germany and Australia.[5]. Successful experiences of DRGs in developed countries have encouraged developing countries to adopt the system. Since 2000, countries such as Brazil, Mexico and Iran have introduced DRGs reform in order to control cost and to increase hospital efficiency.[8,9] those countries met varying degrees of difficulties during the implementation of the policy. Iran developed an interest in casemix funding of hospital as part of a major health financing reform in 2003.10 But the usefulness of DRG information for either management or funding arrangement is still under question due to the poor quality of hospital data.[11]
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