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Development of Peer‐Group‐Classification Criteria for the Comparison of Cost Efficiency among General Hospitals under the Korean NHI Program
Author(s) -
Kang HeeChung,
Hong JaeSeok,
Park HeonJin
Publication year - 2012
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/j.1475-6773.2012.01379.x
Subject(s) - medicine , case mix index , health care , cluster (spacecraft) , service (business) , actuarial science , family medicine , nursing , computer science , business , marketing , economics , programming language , economic growth
Objectives To classify general hospitals into homogeneous systematic‐risk groups in order to compare cost efficiency and propose peer‐group‐classification criteria. Data Sources Health care institution registration data and inpatient‐episode‐based claims data submitted by the K orea N ational H ealth I nsurance system to the H ealth I nsurance R eview and A ssessment S ervice from J uly 2007 to D ecember 2009. Study Design Cluster analysis was performed to classify general hospitals into peer groups based on similarities in hospital characteristics, case mix complexity, and service‐distribution characteristics. Classification criteria reflecting clustering were developed. To test whether the new peer groups better adjusted for differences in systematic risks among peer groups, we compared the R 2 statistics of the current and proposed peer groups according to total variations in medical costs per episode and case mix indices influencing the cost efficiency. Data Collection A total of 1,236,471 inpatient episodes were constructed for 222 general hospitals in 2008. Principal Findings New criteria were developed to classify general hospitals into three peer groups (large general hospitals, small and medium general hospitals treating severe cases, and small and medium general hospitals) according to size and case mix index. Conclusions This study provides information about using peer grouping to enhance fairness in the performance assessment of health care providers.