Decision Making Tool for Medical Procedures with Risks

Researchers: John Maleyeff & Danrong Chen

The aim of this research was to create and evaluate a decision aid to support a patient’s ability to share in decision making, where the options available have uncertain benefits and potential harms.  A color-coded matrix was used as the decision aid where randomly placed cells representing benefits, harms, and neutral outcomes in quantities corresponding to their respective likelihoods.  An iterative approach generated an indifference point for a set of hypothesized decision scenarios.

Forty-eight study participants were asked their preferences across 12 hypothesized cancer screening testing decisions.  Plots of each participant’s indifference curve and regions for accepting or rejecting a screening test regimen were used to illustrate how the method can be applied, and to illustrate that some participants’ decision process was irrational.  The decision aid provides a useful mechanism for communicating uncertain medical outcomes to patients.  It can use helpful to assist in the implementation of patient-centered medical decision making.  It can also identify patients who cannot effectively interpret information in a rational manner.

The research continues by using a consumer health informatics approach to investigate the development of a patient-centered decision support system (DSS) with individualized utility functions.  It supports medical decisions that have uncertain benefits and potential harms.  The system’s underlying optimization model incorporates two user-specific utility functions – one that quantifies life-saving benefits and one that quantifies harms, such as unnecessary follow-up tests, surgeries, or treatments.  The system requires sound decision making.

Therefore, the decision-making process was studied using a decision aid in the form of a color-coded matrix with the potential outcomes randomly placed in proportion to their likelihoods.  Data were collected from 48 study participants, based on a central composite experimental design.  The results show that the DSS can be effective, but health consumers may not be rational decision makers.

Image by Danrong Chen