Rheumatoid arthritis (RA) is a serious disease, both painful and life-altering. Sufferers often find themselves unable to perform basic tasks, including dressing themselves, opening containers, using silverware, rising from a chair. There are medications available that, in many cases with high probability can enable the RA patient to accomplish such tasks. However, these medications often have low-probability but scary side effects. Patients often choose to not take drugs, because of highly unlikely scenarios.
Although clinicians tell patents the probabilities of side effects, it seems that the patients do not grasp the unlikelihood of those possibilities. We propose a decision support system to help them understand probabilities of effects and side effects of RA drugs by "experiencing" them in a computer-game-like setting.
The proposed system will allow patients to view and manipulate an avatar that suffers a similar level of RA, and can choose to be treated with drugs the patient is considering. The system will run simulations of every-day tasks, showing multiple windows with varying levels of success. The levels will be determined by the patient's disease level, the choice of treatment (for the avatar), and the known probabilities of efficacy and side effects.
===University of Kentucky, College of Medicine===
Dr. Kristine Lohr
* Special Faculty, Internal Medicine - Rheumatology
Jamie L. Studts, Ph.D
* Associate Professor, Department of Behavioral Science
=University of Kentucky, College of Engineering=
Dr. Judy Goldsmith
* Professor, Department of Computer Science
* Computer Science
=University of Kentucky, College of Education=
Malachy L. Bishop
* Regular Faculty, Special Education and Rehabilitation
* Assistant Professor, Curriculum and Instruction
IRB Protocol number:
Source of Funding