Psychology and decision making: modelling health behavior with multiattribute utility theory.
The success of much of dental practice is linked to patient behavior. Understanding the issues that influence patients' decisions when they choose to not follow preventive or therapeutic dental recommendations is instrumental to improving adherence, and ultimately, to improving dental health outcomes. Multiattribute Utility Theory (MAU) provides a methodology for systematically exploring these issues. It is based on a well-established body of knowledge in the psychological literature, and currently represents a state-of-the-art model for predicting behavior and delineating potentially modifiable behavioral determinants. Two examples are presented to illustrate how MAU can be used in clinical settings. In the first example, MAU is used to identify key reasons why nearly 70 percent high-risk patients did not obtain flu shots, a behavioral problem comparable to many confronted in dentistry. MAU correctly predicted the vaccination behavior of 82 percent of patients, and an intervention based on MAU findings nearly doubled vaccination rates. The second example used MAU to identify physician behaviors that influenced patients' satisfaction with an ambulatory care visit. MAU findings from this study identified specific behaviors in a provider's style that if modified may improve patient satisfaction. These MAU applications also emphasize the importance of soliciting the patient's perspective in clinical interactions since some of the most important determinants of patient behavior are not represented in traditional clinical decision models.
Carter
W B
WB
Department of Health Services, School of Public Health and Community Medicine, University of Washington, Seattle 98195.
D003657:Decision Making; D003661:Decision Support Techniques; D005544:Forecasting / Q000379:methods; D015438:Health Behavior; D006293:Health Promotion / Q000379:methods; D006801:Humans; D007252:Influenza Vaccines; D007251:Influenza, Human / Q000517:prevention & control; D008960:Models, Psychological; D017060:Patient Satisfaction; D011237:Predictive Value of Tests; D012372:ROC Curve; D012680:Sensitivity and Specificity; D014611:Vaccination / Q000523:psychology / Q000706:statistics & numerical data
D007252:Influenza Vaccines