Statement of problem: Many healthcare decisions are difficult because they are complex and have important consequences such as the impact on survival or quality-of-life of individuals and on allocation of limited resources. The present state-of-the-art in healthcare decision modeling is often inadequate to properly assess these decisions.
Methods: Based on a literature search and the experience of the authors, typical methodologies used in healthcare decision analysis modeling are explored and compared with methods used in other practices. An example of hormonal therapy decisions is used.
Results: Useful methods that have been developed in other fields are presented. These include methods targeted toward appropriate assessment and representation of the complexity of decisions, assessment of uncertainty, use of nonexpected value decision analysis, and use of multi-attribute decision criteria.
Conclusion: The state-of-the-art in healthcare decision modeling can be improved through learning from other practices.