Sample Case Study

Decision Support Tool for Cancer Treatment Alternatives


When men are diagnosed with low to medium risk prostate cancer, there is no evidence that any treatment option outperforms the others when measured on 5-year survival rates. Additionally, it is necessary to consider the quality of life that the patients experience post-treatment.

Most approaches to treatment decisions focus on population-based statistics, rather than personalized probability outcomes and associated side effects.

Furthermore, patients have differing preferences for potential side effects of each treatment, and eliciting these preferences can be long and tedious.

Stanford Hospital, like most hospitals, struggled to personalize the decision for each new patient, leaving patients to make decisions by absorbing statistical information from their medical team and relying heavily on anecdotal evidence from family and friends.


Automated system to assess preferences for patients with a few simple questions, without necessary input from the analysis team, using concepts for preference elicitation developed as part of a Stanford Ph.D. dissertation in Decision Analysis.

Calculation of patient-specific risk factors and outcomes, based on patient preferences combined with correlation analysis predicting the materialization of each side effect.

Easy-to-use application to graphically show how a patient values each treatment alternative and help them make a decision. The tool is reactive, allowing patients to see how real-time changes in their thinking affect the solution options.

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Intuitive Understanding

Better patient understanding of side effect outcomes and how they relate to their preferences.

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Data-Driven Guidance

Rigorous analysis improved the ability of the medical team to guide patients through their decision-making process.

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Patient Satisfaction

Patients who used the tool expressed a high degree of satisfaction with their decision-making process.

Service Highlights

Decision support tools that provide descriptive analytics, graphical visualization, scenario modeling, and sensitivity analysis to make data-driven decisions.

Consolidate various streams of data into a single source of truth supported by agreed-upon methodologies, allowing you to perform rigorous decision analysis with team consensus.

Clear, graphical approach to understand possible scenarios, updated live at your optimal frequency.