Objective: Given health budget constraints, decisions about investing in implementation strategies have to be made alongside decisions about health care provision and research funding. Using a Bayesian decision theoretic approach, a framework has been developed to simultaneously inform these decisions, establishing the expected value of both perfect information (EVPI) and perfect implementation (EVPIM). We applied this framework to inform decision making about resource allocation to metastatic hormone-refractory prostate cancer (mHRPC) in the UK. Methods: Based on available economic evidence on all plausible treatments for mHRPC, we determined which treatment options were cost-effective and explored the uncertainty associated with this decision. Considering the decision uncertainty and the variation in care provided, we then determined the EVPI and EVPIM. Finally, we performed sensitivity analyses to examine the implications of variation in decision parameters on the efficiency of resource allocation. Results and conclusions: Depending on the cost-effectiveness threshold (ë), we identified mitoxantrone plus prednisolone and docetaxel plus prednisone/prednisolone (3-weekly) as the optimal options to treat mHRPC. Given current clinical practice, there appears to be considerable scope for improving the efficiency of health care provision: the EVPI indicates that acquiring further information could be cost-effective; and the EVPIM suggests that investing in strategies to implement the cost-effective treatment regimens is potentially worthwhile. Through sensitivity analyses, we found that the EVPI and EVPIM are mainly driven by ë, the treatment options being considered, the current level of implementation, and the size of the patient population. The application demonstrates that the framework provides a simple and useful tool for decision makers to simultaneously address resource allocation problems between health care provision, further research and implementation.