Avatar-Based Decision Support Technology for Surrogate Decision Makers

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DESCRIPTION (provided by applicant): One of the most burdensome responsibilities faced by any family member is making health care decisions for a loved one who is decisionally impaired. Given the aging of the population and increasing options for treatment of acute illnesses, most Americans will find themselves facing this tremendous challenge of assuming the surrogate decision maker role for a relative or friend. Despite the frequency with which family members face this taxing responsibility, we have yet to identify effective ways to support them in decision making. This project builds on an established decision support framework and the team's prior experience with health technology. We aim to create an interactive, avatar-based, tailored intervention using serious gaming technology to deliver decision support to surrogate decision makers in preparation for post-discharge decisions about a patient's resuscitation and rehospitalization status. The purpose of this study is the development and evaluation of a novel decision support strategy that, if shown to be effective, can be widely disseminated and offered to surrogate decision makers facing the many significant health care decisions required in the acute and chronic care of their cognitively impaired loved one. The project is planned in two phases: Phase 1 will consist of the creation, development, and design of an interactive, avatar-based, individually tailored electronic program that will engage and prepare family members for the role of surrogate decision maker. Phase 2 will be a three-arm, randomized, clinical trial comparing the tailored, avatar-based simulation system, "electronic Surrogate Decision Maker Resources and Tailored Training" (eSMARTT) with a non-tailored passive educational program, "electronic Nursing Home Decision Aid" (eNHDA), and with usual care (UC). By engaging surrogate decision makers as co-designers and evaluators of decision support strategies, this application will generate data that will have a significant impact on clinical practice and the future direction of research in this area.