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Open Access Research article

Performance evaluation of inpatient service in Beijing: a horizontal comparison with risk adjustment based on Diagnosis Related Groups

Weiyan Jian1*, Yinmin Huang1, Mu Hu2 and Xiumei Zhang3

Author Affiliations

1 School of Public Health, Health Science Center, Peking University, 38# Xue Yuan Road, Hai Dian District, Beijing, PR China

2 Health Insurance Office, the Third Medical School, Peking University, 17# Xue Yuan Road, Hai Dian District, Beijing, PR China

3 Beijing Public Health Information Center, 59# Bei Wei Road, Xuan Wu District, Beijing, PR China

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BMC Health Services Research 2009, 9:72  doi:10.1186/1472-6963-9-72

Published: 30 April 2009

Abstract

Background

The medical performance evaluation, which provides a basis for rational decision-making, is an important part of medical service research. Current progress with health services reform in China is far from satisfactory, without sufficient regulation. To achieve better progress, an effective tool for evaluating medical performance needs to be established. In view of this, this study attempted to develop such a tool appropriate for the Chinese context.

Methods

Data was collected from the front pages of medical records (FPMR) of all large general public hospitals (21 hospitals) in the third and fourth quarter of 2007. Locally developed Diagnosis Related Groups (DRGs) were introduced as a tool for risk adjustment and performance evaluation indicators were established: Charge Efficiency Index (CEI), Time Efficiency Index (TEI) and inpatient mortality of low-risk group cases (IMLRG), to reflect respectively work efficiency and medical service quality. Using these indicators, the inpatient services' performance was horizontally compared among hospitals. Case-mix Index (CMI) was used to adjust efficiency indices and then produce adjusted CEI (aCEI) and adjusted TEI (aTEI). Poisson distribution analysis was used to test the statistical significance of the IMLRG differences between different hospitals.

Results

Using the aCEI, aTEI and IMLRG scores for the 21 hospitals, Hospital A and C had relatively good overall performance because their medical charges were lower, LOS shorter and IMLRG smaller. The performance of Hospital P and Q was the worst due to their relatively high charge level, long LOS and high IMLRG. Various performance problems also existed in the other hospitals.

Conclusion

It is possible to develop an accurate and easy to run performance evaluation system using Case-Mix as the tool for risk adjustment, choosing indicators close to consumers and managers, and utilizing routine report forms as the basic information source. To keep such a system running effectively, it is necessary to improve the reliability of clinical information and the risk-adjustment ability of Case-Mix.