Performance management is an important mechanism for ensuring accountability and improving the quality of health-care services. The last decade has witnessed a proliferation in the development of performance measurement systems for assessing health-care processes and outcomes at the program, hospital, district, system and national level. This has allowed for comparison and benchmarking between similar aspects of care at each of these levels. Unfortunately, most performance systems are devoid of clear mechanisms for translating feedback from measures into strategies for action, thus leaving largely unfulfilled the quality and management aspect necessary to improve health-care services. Therefore, the thinking that goes into designing these systems must change. This article outlines a management framework called systematic outcome mapping that provides for performance management rather than just performance measurement by allowing for quality improvement to be built into performance indicator development. It utilizes evidence-based medicine and expert consensus opinion to establish linkages between processes of care and their outcomes with the clear intent that feedback from information provided by performance indicators can be used to modify health-care activities so as to improve health outcomes. This fulfils the quality improvement aspect of performance measurement and makes it an integral part of a performance management framework that reinforces organizational learning through feedback from outcomes and the assessment of organizational routines.

The concepts of hospital service population and its estimation techniques is refined and generalized from a model-building point of view, and the generalized model is applied to the Alberta, Canada, hospital system. The assumptions underlying the so-called relevance and commitment index methods are investigated. A set of computer programs is developed for estimating the service population of Alberta hospitals. The programs use census, patient origin, and hospital statistics related to costs as input, and provide resource allocation and utilization rates on an age-sex adjusted per capita basis for all Alberta general acute hospitals and hospital districts. The estimates based on relevance and commitment index methods are compared and found to be very similar, except at the extreme tail areas of the distribution.

A statistical decision model is applied to the benefit evaluation of screening projects to derive an expression which provides upper and lower limits for average benefits in terms of prevalance rates of PurposeThe purpose of this paper is to propose an analytical method for evaluating screening projects. As noted elsewhere1, mass screening has become an increasingly common practice in the health care system, at substantial cost to the public, and evaluation of the benefits of such programs is of vital importance for health planners and administrators. This paper is based on the senior author's experience with a validation study for the screening of pre-school children for possible physical and/or psycho-social disabilities which may hinder educational performance. Like any other health service program, it was very difficult to demonstrate the benefits as compared with the cost of this screening project, and this motivated us to try to develop a quantitative model for such evaluation. Essentially, the method estimates the value of information obtainable from a screening project, in terms of input cost, by an application of statistical decision theory.For any such evaluation, it is important to estimate the monetary value of the benefits resulting. However, it is difficult, and sometimes impossible, to place monetary values on Assumptions

Health outcomes measurement has not fulfilled its potential. An important reason is the inability of the systems in place to properly utilize the information provided by outcome indicators to improve care. This paper describes a measurement framework that empowers healthcare professionals to act on information that is provided by using logical predetermined protocols. The benefits of this framework include enhanced evidence-based healthcare processes, organizational learning through knowledge generation and dissemination, and improved health outcomes.