Health care decision makers often have to make their decisions quickly and using information that is less than perfect. The provision of information is challenging as short timescales preclude primary data collection. As a result reliance is place on modelling exercises informed by systematic review. OBJECTIVE: Using value of information techniques may help to quantify the extent of uncertainty and identify whether it is cost-effective to gather more information. Uncertainty in modelling is outlined here in the frame of the rapid assessment of the cost-effectiveness of home haemodialysis for end stage renal failure (ESRF). METHODS: A net benefit approach is adopted to compute value of gathering more information. Evaluation was hampered as data on the costs and relative effectiveness of ESRF treatments are limited. The short time frame available for the study caused further uncertainty, resulting in two treatment options being treated as absorbing states (as was death) in the Markov model . Generalisable data were also limited. For example, cost data, although detailed and comprehensive, related to one centre and no utility data were available for the setting of interest. Such challenges were overcome by discussing the framing of the research question with the decision-maker and undertaking thorough sensitivity analysis. RESULTS: An initial deterministic model provided sufficient information for decision. A subsequent probabilistic analysis broadly supported the decision (although its results were unavailable at the time to decision-makers). The initial decision did not explicitly consider the value of improvements to the evidence base although this was facilitated by the probabilistic analysis. CONCLUSON: Value of information analysis can help to measure uncertainty surrounding results based on imperfect information and may become a useful addition both to those undertaking economic evaluations and decision makers themselves.