Disparities in healthcare delivery and outcomes have been linked, in part, to the difficulties physicians have in establishing effective communication with patients who differ from themselves in terms of race, ethnicity and economic circumstances. Diabetes mellitus and pre-diabetic nutrition-based obesity are an important case in point. Racial and socio-economic differences can impede doctors'ability to understand their patients' constraining realities, such as the complex tradeoffs and decision strategies involved in daily activities like purchasing food and medications. When clinical encounters about diabetes diagnosis and management are not tailored to the patient's pragmatic realities, they become less likely to lead to a shared understanding of what needs to be done. This can, in turn, lead physicians to perceive that minority patients are non-compliant and ignoring their advice. To be effective, these encounter-based discussions about care, behavior, and self-care require a dialog that is adaptive to the cultural assumptions, cognitive/emotional concerns, and systemic socio- economic constraints of the individual patient. This area is in need of improvement - clinicians treating populations affected by health disparities must possess the competencies to understand how to frame and tailor their dialogs to the unique needs of these patients. The system we envision-Realizing Enhanced Patient Encounters through Aiding and Training (REPEAT) - will provide an innovative alternative to current (very minimal) training. It encapsulates best practices in a low cost, ubiquitously accessible system based on experiential learning. REPEAT will offer a realistic virtual environment that allows learning to occur through simulated interactions with synthetic standardized patients (SSPs). These are interactive computer-generated avatars that can act and react realistically to clinician (verbal and via nonverbal) behaviors. Emerging cognitive simulation technology will imbue the SSPs with attributes (e.g., environmental and economic limitations, beliefs, attitudes, fears) that are representative of shared characteristics of a specific patient subpopulation. Phase I will build a preliminary REPEAT prototype as a limited set of virtual clinical encounters with SSPs supported by learning scaffolding drawn from intelligent tutoring technology. These virtual encounters will allow clinicians to practice interacting with, and to learn from interactions with, SSPs that represent an initial population of concern - pre-diabetic and diabetic African-American patients in economically depressed urban food deserts. This Phase I prototype will be assessed for learning effect and user acceptance at our healthcare partner, the Virginia Commonwealth University School of Medicine, using a sample of clinicians (n=20). This work will provide considerable insight into how to develop effective SSPs and embed them into a viable training environment. The technology has the potential to be translated to broad usage, including a version usable by patients, and to reduce healthcare disparities by improving communication among providers and patients. It also specifically addresses broader mandated requirements for medical students, residents and physicians in Cultural sensitivity, and Interpersonal Skills and Communication.

Public Health Relevance

This research will address health disparities in diabetes care and outcomes by improving the provider-patient communication process. The REPEAT (Realizing Enhanced Patient Encounters through Aiding and Training) system is a low-cost, highly scalable learning technology that provides open-ended, tailored practice/feedback opportunities using synthetic standardized patients (SSPs). Through it, clinicians can learn crucial cultural competence, interpersonal and communication skills. REPEAT has high potential to dramatically improve clinical communication and diabetes management, and reduce complications in low-income African American populations.

National Institute of Health (NIH)
National Institute on Minority Health and Health Disparities (NIMHD)
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
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Special Emphasis Panel (ZEB1-OSR-B (M1))
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Tabor, Derrick C
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Starship Health Technologies
Fort Washington
United States
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