OBJECTIVES: Conventional economic evaluation involves piecewise comparisons of competing interventions at a single point in a broader care pathway. METHODS: This approach is subject to several problems: a) there remains an ongoing debate surrounding the appropriateness of threshold-based decision rules and whether their repeated use will maximise health; b) restricting the model scope to a single decision point means that other adoption decisions elsewhere in the disease pathway may be treated as independent of the problem under consideration; and c) the absence of model development guidance leads to inconsistencies between analyses addressing similar decision problems. In light of these problems, this study puts forward the notion of “Whole Disease Modelling.” This involves simulating whole disease and treatment pathways within a single model, from preclinical disease through to diagnosis and referral, adjuvant treatment, follow-up, potential recurrence, palliative treatment, end-of-life care and eventual death. A methodological framework has been developed based on three key principles: 1) the model boundary and breadth should capture all relevant aspects of the disease and its treatment; 2) the model should be developed such that the decision node is conceptually transferable across the pathway; and 3) the costs and consequences of service elements should be structurally related. RESULTS: Case study applications in colorectal cancer services suggest that Whole Disease Modelling is feasible and may provide a consistent platform for economic analysis at virtually any point in a disease pathway using multiple economic decision rules. CONCLUSIONS: The value of the approach may be realised when: multiple decision problems require formal economic analysis at a single timepoint; services are subject to rapid innovation and the model can be re-used; a substantial proportion of currently provided service elements have not previously been subjected to economic analysis, and; standard cost-utility decision rules fail to reflect the complexity of the decision-makers’ objectives.