The evaluation of case-mix adjusted efficiency scores the case of the South African private hospital industry

• Main Author: Dreyer, Kathryn Ann
• Other Authors: Ramjee, Shivani
• Format: Dissertation
• Language: English
• Published: University of Cape Town 2014
• Subjects: Actuarial Science
• Online Access: http://hdl.handle.net/11427/5813

Includes abstract. === Includes bibliographical references. === There is little existing South African literature relating to hospital efficiency that allows for differences in case mix across hospitals. One of the primary motivations for this dissertation is to help fill this gap in the literature by examining the impact that adjusting for differences in case mix has on efficiency scores. Data Envelopment Analysis (DEA) is chosen as the efficiency measurement method because of its exibility and ease of handling multiple inputs and outputs. A number of DEA models are applied to a sample of South African private hospitals for the years 2008 to 2011 inclusive. Three different case-mix adjustment techniques are investigated and their ability to capture differences in case mix is assessed. The three techniques investigated are: a case-mix adjustment factor (constructed using Diagnosis-Related Groups (DRGs)) to adjust outputs; including the case-mix adjustment factor as an additional output; and disaggregating hospital admissions into broad categories which are used as outputs. A comparison of the unadjusted model with the case-mix adjusted model reveals that omitting the adjustment can have a considerable impact on efficiency scores. Whilst little difference is noted in average efficiency scores for the group of hospitals, 90% for the unadjusted model and 92% for the adjusted model in 2011, there are substantial differences between the adjusted and unadjusted efficiency scores of individual hospitals. On comparison of the three different techniques investigated, it is evident that if there is sufficient data to construct a case-mix adjustment factor, case-mix adjusted admissions should be used, rather than using the factor as an additional output variable. In the case where insufficient data is available, disaggregating admissions does capture some of the differences in case mix but a substantial amount of power is lost as a result of increasing the number of output variables.