Diagnosing Mental Health Disorders in Primary Care: Evaluation of a New Training Tool
Rachel Satter, Arizona State University, United States
Arizona State University . Awarded
Major Depressive Disorder (MDD) and Posttraumatic Stress Disorder (PTSD) are highly prevalent illnesses that can result in profound impairment. While many patients with these disorders present in primary care, research suggests that physicians under-detect and suboptimally manage MDD and PTSD in their patients. The development of more effective training interventions to aid primary care providers in diagnosing mental health disorders is of the utmost importance. This research focuses on evaluating computer-based training tools (Avatars) for training family physicians to better diagnose MDD and PTSD. Three interventions are compared: a "choice" avatar simulation training program, a "fixed" avatar simulation training program, and a text-based training program for training physicians to improve their diagnostic interviewing skills in detecting and diagnosing MDD and PTSD. Two one-way ANCOVAs were used to analyze the differences between the groups on diagnostic accuracy while controlling for mental health experience. In order to assess specifically how prior mental health experience affected diagnostic accuracy the covariate of prior mental health experience was then used as an independent variable and simple main effects and pairwise comparisons were evaluated. Results indicated that for the MDD case both avatar treatment groups significantly outperformed the text-based treatment in diagnostic accuracy regardless of prior mental health experience. For the PTSD case those receiving the fixed avatar simulation training more accurately diagnosed PTSD than the text-based training group and the choice-avatar training group regardless of prior mental health experience. Confidence ratings indicated that the majority of participants were very confident with their diagnoses for both cases. Discussion focused on the utility of avatar technology in medical education. The findings in this study indicate that avatar technology aided the participants in diagnosing MDD and PTSD better than traditional text-based methods employed to train PCPs to diagnose. Regardless of experience level the fixed avatar group outperformed the other groups for both cases. Avatar technology used in diagnostic training can be user-friendly and cost-effective. It can also have a world-wide reach. Additional educational benefit could be provided by using automated text analysis to provide physicians with feedback based on the extent to which their case diagnostic summaries cover relevant content. In conclusion, avatar technology can offer robust training that could be potentially transferred to real environment performance.
Satter, R. Diagnosing Mental Health Disorders in Primary Care: Evaluation of a New Training Tool. Ph.D. thesis, Arizona State University. Retrieved February 16, 2019 from https://www.learntechlib.org/p/120522/.
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