There were significant improvements in process and clinical outcomes for patients on a GPMP or a GPMP and TCA, particularly when these were followed up by regular reviews. Patients using cdmNet were four times more likely to have their GPMP or TCA followed up through regular reviews than the national average.

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The paper describes a new multiagent system with enhanced capabilities obtained through a hybrid of intelligent techniques. The processing in the model is handled by two types of agents: distributed agents and a central administrator agent. Localized processing at the individual agents is carried out using mathematical techniques and genetic algorithms. The central administrator agent dynamically obtains information about the problem domain from the Internet and maintains a knowledge pool using a clustering technique called the growing self-organizing map (GSOM). Distributed agents communicate with the central administrator agent if they need further knowledge about the problem domain to provide solutions to user-defined tasks. The approach integrates traditional mathematical, data mining, and evolutionary techniques with a multiagent system. The proposed system is implemented as a travel optimizer application for the e-tourism domain. Finally, the possibilities of integrating the proposed technique with currently available e-tourism applications to provide the customer with enhanced solutions are identified.