???Making decisions about complex health problems can be time consuming and emotionally taxing. We are gathering input from medical experts and actual patients to develop a useful tool that will make the decision process easier to handle.??? - Carl Snyderman, MD, UPMC Presbyterian


Grant Application

Carl Snyderman, MD, MBA, Stephanie Henry, RN, Igor Linkov, PhD, Faina Linkov, PhD, MPH, Rozann Saaty, PhD, UPMC Presbyterian


Proposed Innovation

Patients with complex medical problems are taxed with tough choices regarding best treatment options. Treatment choices are adversely affected by a number of factors: lack of evidence-based information; clinical bias of healthcare providers; uncertainty of outcome; influence of others; and emotional bias and stress.

The solution proposed to and funded by the Beckwith Institute involves more collaborative consideration of the patient’s values and includes all stakeholders in care or treatment options.


Improvements in Action

Multi-criteria decision analysis techniques will help improve the decision-making process for patients with complex medical problems such as cancer. The funded solution proposes to:

  • Define value criteria through focus groups and a panel of medical experts.
  • Build an analysis model that includes an evidence-based review of literature; expert consensus; an analytic hierarchic process; and supporting software systems to track and measure decision criteria.
  • Assess the model through a retrospective analysis of patient choices and a comparison of these choices to stakeholder groups, in order to generate collaborative stakeholder and patient satisfaction.


Results – In Progress

Through this innovation, benchmarks will be established based on focus group results, background research, and data collection. To date, decision criteria have been established through patient focus groups and discussions with clinicians. An exhaustive review of the medical literature has defined outcomes for key decision factors. In addition, a consensus panel of surgeons and radiation therapists has provided missing data. This information is currently being used to build the computer model for testing in a patient population.