Abstract
Background
This study focuses on the application of Provan and Kenis’ modes of network governance to the specific field of public healthcare networks, extending the framework to an analysis of systems in which networks are involved. Thus, the aim of this study is to analyze and compare the governance of two cancer networks in two Italian regions that underwent system reconfiguration processes due to reforms in the healthcare system.
Methods
A qualitative study of two clinical networks in the Italian healthcare system was conducted. The sample for interviews included representatives of the regional administration (n = 4), network coordinators (n = 6), and general and clinical directors of health organizations involved in the two networks (n = 25). Data were collected using semi-structured interviews.
Results
Our study shows that healthcare system reforms have a limited impact on network governance structures. In fact, strong inertial tendencies characterize networks, especially network administrative organization models (NAO). Networks tend to find their own balance with respect to the trade-offs analyzed using a mix of formal and informal ties. Our study confirms the general validity of Provan and Kenis’ framework and shows how other specific factors and contingencies may affect the possibility that cancer networks find positive equilibria between competing needs of inclusivity and efficiency, internal and external legitimacy, and stability and flexibility. It also shows how networks react to external changes.
Conclusions
Our study shows the importance of considering three factors and contingencies that may affect network effectiveness: a) the importance of looking at network governance modes not in isolation, but in relationship to the governance of regional systems; b) the influence of a specific network’s governance structure on the network’s ability to respond to tensions and to achieve its goals; and c) the need to take into account the role of professionals in network governance.
Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content
