An analytic approach to medical risk management: Decisions for breast cancer prevention

We have developed a risk management decision model for breast cancer prevention with the goal of empowering patients by improving their knowledge base and by facilitating a logical decision process within prevention care. The risk management model includes a structured approach to prevention decisions, interactive clinical decision tools, and a platform to model patient outcomes and economic impact of prevention decisions. By evaluating the clinical and economic feasibility of the decision model, we evaluated the clinical decision model, potential risk assessment techniques, and prevention intervention strategies.; Decision analysis principles and precepts were used to define and assess the current state of medical decision-making as applied to disease prevention. We categorized and evaluated current prevention methods for effectiveness and developed a flexible disease and financial model capable of incorporating additional knowledge bases obtained from future studies and expert opinion. Using decision quality as our measure, we applied decision analysis to redirect the current methods of prevention care. The resulting decision methodology provides a framework for breast cancer risk management in the setting of a collaborative consultation.; Preliminary testing of the clinical decision tool suggests that individually tailored risk assessment changed patient decision-making, implying recently developed risk assessment procedures are valuable to patients. The economic model we built serves as a platform to evaluate the value of risk management strategies that incorporate risk assessment tools and prevention interventions for breast cancer. The results of our economic analyses demonstrate that these risk assessment techniques are cost-effective in a cohort of high-risk women. Insights from the model can guide the development and use of risk assessment tools and prevention interventions.; As scientific research progresses, dynamic tools that function to tailor individual intervention will be essential to quality medical care. The decision model we have developed is the first such model to provide the ability to accomplish this and to both integrate current evidence and stimulate research to reduce breast cancer risk.