The goal of this proposal is to develop a novel web-based software program to evaluate consumer preferences in the treatment of depression and potentially other disorders. During year 1 we will develop the requisite software, and during year 2 we will pilot the software with 20 clinicians and 75 consumers in a community mental health setting to determine feasibility and clarity. We will also assess the stability of preference scores over a two month period. The method for assessing preferences, developed and n validated with a previous NIMH R34 award, involves first asking consumers to provide input on their preferences regarding the attributes of different treatments for depression. Attributes of treatments are primarily the side effects of antidepressants, the time commitment for and nature of psychotherapy. Based on our initial R34 work that involved clinicians and consumer input, we arrived at a final list of 18 attributes. The software will use a MAXDIFF (maximum difference scaling, also known as ?best worst scaling?) method that presents 4 (of the 18) attribute choices to consumers at a time interatively, and the consumer chooses the most and least preferred of the 4. Scores are then output that rank the attributes from most preferred to least preferred. For each consumer, these preference scores are then compared to actual atrtributes of treatments for depression using multiattribute decision modeling to arrive at a final ranking of treatments from most preferred to least preferred. Our previous research has shown that receiving a non-preferred treamtent leads to considerably longer durations of treatment and significantly greater likelihood of changing treatments. To move this program of research forward, software is needed so that preferred treatments can be measured in real time so consumer preferences can be integrated into actual treatment decisions made at the initial visits to the clinic.Incorporating consumer preferences into the decision-making process for the treatment of depression and other disorders can help expand the use of available options, better meet consumers' needs, and potentially yield better outcomes. Programming the software will involve integrating a web interface for MAXDIFF assessment programming for scoring MAXDIFF output, conducting MADM analyses, and outputing a report that will inform clinicians about consumer treatment preferences.
Major depressive disorder is one of the most common psychiatric disorders and is associated with considerable social and occupational disability. Incorporating consumer preferences into treatment of this and other disorders will facilitate the tailoring of evidence-based practice to the individual and potentially increase consumer satisfaction and improve outcomes. A software product that measures consumer preferences in real-time, with a report for the treating clinician, is needed to achieve such long-term goals.
