Impacts of the medical arms race on medical expenses: a public hospital-based study in Shenzhen, China, during 2009–2013

Abstract

Background Has the medical arms race (MAR) increased healthcare expenditures? Existing literature has yet to draw a consistent conclusion. Hence, this study aims to reexamine the relationship between the MAR and medical expenses by the data from public hospitals in Shenzhen, China, during the period of 2009 to 2013. Methods This study’s data were collected through panel datasets spanning 2009 to 2013 from the Shenzhen Statistical Yearbook, Shenzhen Health Statistical Yearbook, and annual reports from the Shenzhen Municipal Health Commission. The Herfindahl–Hirschman index and hierarchical linear modeling were combined for empirical analysis. Results The MAR’s impact on medical examination fees differed during the inpatient and outpatient stages. Further analysis verified that the MAR had the most significant impact on outpatient examination fees. Due to the characteristics of China’s medical system, government regulations in the healthcare market may consequently accelerate the MAR among public hospitals. Strict government regulations on the medical system have also promoted increased medical examination costs to some extent. Once medical service prices are under strict administrative control, only drug and medical examination fees are the primary forms of extra income for hospitals. After the proportion of drug fees is further regulated, medical examinations will then become another staple method to generate extra revenue. These have distorted Chinese public hospitals’ medical fees, which completely differ from those in other countries. Conclusion The government should confirm that they have allocated sufficient financial investments for public hospitals; otherwise, the competition among hospitals will transfer the burden to patients, and especially to those who can afford to pay for care. A core task for public hospitals involves providing safer, less expensive, and more reliable medical services.

Analysis of the Forces Driving Public Hospitals’ Operating Costs Using LMDI Decomposition: The Case of Japan

“…Meanwhile, temporarily, during pandemic periods such as COVID-19, people falling sick in large numbers has resulted in patients being rushed to public hospitals, immediately causing overcapacity problems, with serious resource and economic costs as a consequence [9][10][11][12][13][14]. To increase the cost effectiveness of public hospitals, national governments have formulated and implemented various reform plans such as developing a hierarchical medical system, incorporating public hospitals, introducing private capital and more efficient management systems, introducing a prospective payment system, implementing public reporting for price transparency, and even allowing market competition in a bid to suppress operating costs [15][16][17][18][19][20].…”

Analysis of the Forces Driving Public Hospitals’ Operating Costs using LMDI Decomposition: The Case of Japan

“…Especially during the COVID-19 pandemic, people falling sick in large numbers resulted in patients being rushed to public hospitals, immediately causing overcapacity problems with serious resource and economic costs as a consequence 1,2 . To increase the cost effectiveness of public hospitals, the national governments formulated and implemented various reform plans such as developing an hierarchical medical system, incorporating public hospitals, introducing private capital and more efficient management systems, introducing a prospective payment system, implementing public reporting for price transparency, and even allowing market competition in a bid to suppress operating costs [3][4][5][6][7][8] . Although various analysis tools, such as data envelope analysis, regression models, and time series analysis, have been applied to evaluate the improvements in efficiency at public hospitals after institutional reform, the results are variable and not intuitional enough to identify the key influencing factors that significantly affect the operating effectiveness of these institutions 5,9-12 .…”