The Effect of Service Quality Variables on Healthcare Organizations’ Efficiency Measurements

Over the last two centuries the business world has changed from low scale manufacturing, through mass production industries, to a high volume service oriented world. The importance of traditional industries has declined and the role of the new services industries has grown dramatically and recently became about 80 percent of the global economy volume. Thus, the main concern of economics and efficiency researchers shifted towards service organizations such as banks, hospitals, schools and transportation operators.

Healthcare systems or hospitals in particular, are by nature very resource intensive, so increasing their efficiency potentially yields more savings. However, unlike other types of service organizations, the considerations taken by decision makers in hospitals are influenced mostly by clinical and medical impacts rather than service quality ratings. Until recent years, most studies measuring the performance and efficiency of healthcare organizations and hospitals have mostly considered operational attributes. A few of them included medical/clinical quality factors, but did not refer to service quality factors, obtained via patient satisfaction surveys. Our research addresses the effect of service quality on healthcare organizations’ efficiency measurements and positioning.

Our objectives are to study the impact of service quality measurements on hospitals` efficiency and offer a better way for operational decision making. By using both the efficiency score and the service quality indices, hospitals management can allocate resources in a more economical way with minimal degradation of the quality of service.

For the empirical part of the study we focused on New-Jersey acute care hospitals, using AHA (American health Association) database for the operational figures and AHRQ (Agency for Healthcare Research and Quality) patient satisfaction survey as a proxy for the service quality indexes. The study was conducted using two-stage data envelopment analysis (DEA). In the first stage we run an operating efficiency (OP) DEA model, calculating the hospitals efficiency scores. As a second-stage we perform two paralleled analyses. In the first analysis, we use OLS regression, for examining the relations between service quality variables/components, as well as other exploratory and environmental variables on the efficiency score obtained in the first stage DEA. In the second analysis, we extend the DEA model of the first stage by using the SQI as an additional output variable.

 It is clear that there is a mix of hospitals with significantly differing efficiency levels such that less than 10% lie on the Pareto frontier. In a second stage, OLS regression, we find a significant relationship between the overall likelihood that a hospital will be recommended and their technical efficiency level. Perhaps surprisingly, the higher the perceived level of quality, the higher their efficiency estimate which suggests that the better managed hospitals also achieve greater patient satisfaction.