Quality Management and the Care Keys Quality Matrix

Theoretical and empirical research on the effect of management strategies and service organisation on the quality of care and on the quality of life of clients is still developing. In view of the growing importance of the provision of care in an “ageing society” this state of the art should raise concern and induce enhanced research efforts. The Care Keys project tried to make a contribution to the theoretical and empirical issues, even addressing practical concerns by developing tools for quality management in long-term care. The theoretical framework for management has been presented elsewhere (Chapter 6), and the reader might like to refer to this discussion for more detail. The objectives for the following analyses focus on empirical issues. We will investigate the effects of elements of “good” management strategies on the quality of care and care outcomes for the client. Drawing on the theoretical background, the concept of management will be put into operational terms by a set of indicators, which may guide quality management. The aim is to demonstrate the empirical significance and impact of the indicators in order to provide at least preliminary empirical support both for the indicators and for the structure and comprehensiveness of the strategy employing them. The practical aim of developing a matrix of quality indicators into a tool for quality management will, thus, be supported by a first evidence base.

The contribution will proceed in the following steps: first, the theoretical background will be sketched out. The aim is to clarify basic concepts of “good management” and to provide a model of management impacts as a basis for the empirical theses to be investigated. Second, the aim is to define and to structure the set of variables and to discuss some issues about the indicators, data and methods used in the following analyses. Third, the results of first analyses are shown focusing on the reconstruction of management styles from the data by cluster analysis, and the investigation of causal impacts by regression analyses. Finally, the results will be discussed and some conclusions drawn for further research.