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Researchers, policymakers and hospital managers often encounter numerous quality measures when assessing hospital quality. The purpose of this paper is to address the challenge of summarising, interpreting and comparing multiple quality measures across different quality dimensions by proposing a simple method of constructing a composite quality index. The method is applied to hospital administrative data to demonstrate its use in analysing hospital performance.

Logistic and fixed effects regression analyses are applied to secondary admitted patient data from all hospitals in the state of Victoria, Australia for the period 2000/2001–2011/2012.

The derived composite quality index was used to rank hospital performance and to assess changes in state-wide average hospital quality over time. Further regression analyses found private hospitals, day hospitals and non-acute hospitals were associated with higher composite quality, while small hospitals were associated with lower quality.

The method will enable policymakers and hospital managers to better monitor the performance of hospitals. It allows quality to be related to other attributes of hospitals such as size and volume, and enables policymakers and managers to focus on hospitals with relevant characteristics such that quantity and quality changes can be better understood, monitored and acted upon.

A simple method of constructing a composite quality is an indispensable practical tool in tracking the quality of hospitals when numerous measures are used to capture different aspects of quality. The derived composite quality can be used to summarise hospital performance and to identify factors associated with quality via regression analyses.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.