Smartphone-Based Virtual and Augmented Reality Implicit Association Training (VARIAT) for Reducing Implicit Biases Toward Patients Among Health Care Providers: App Development and Pilot Testing

Jiabin Shen; Alex J Clinton; Jeffrey Penka; Megan E Gregory; Lindsey Sova; Sheryl Pfeil; Jeremy Patterson; Tensing Maa

doi:10.2196/51310

Smartphone-Based Virtual and Augmented Reality Implicit Association Training (VARIAT) for Reducing Implicit Biases Toward Patients Among Health Care Providers: App Development and Pilot Testing

JMIR Serious Games. 2024 Mar 7:12:e51310. doi: 10.2196/51310.

Authors

Jiabin Shen¹, Alex J Clinton¹, Jeffrey Penka², Megan E Gregory³, Lindsey Sova⁴, Sheryl Pfeil⁵, Jeremy Patterson⁶, Tensing Maa⁷

Affiliations

¹ Department of Psychology, University of Massachusetts Lowell, Lowell, MA, United States.
² LittleSeed, Inc, Columbus, OH, United States.
³ Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.
⁴ Center for Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research, College of Medicine, Ohio State University, Columbus, OH, United States.
⁵ College of Medicine, Ohio State University, Columbus, OH, United States.
⁶ Advanced Computing Center for Arts and Design, Ohio State University, Columbus, OH, United States.
⁷ Center for Clinical Excellence, Nationwide Children's Hospital, Columbus, OH, United States.

PMID: 38488662
PMCID: PMC11004623
DOI: 10.2196/51310

Abstract

Background: Implicit bias is as prevalent among health care professionals as among the wider population and is significantly associated with lower health care quality.

Objective: The study goal was to develop and evaluate the preliminary efficacy of an innovative mobile app, VARIAT (Virtual and Augmented Reality Implicit Association Training), to reduce implicit biases among Medicaid providers.

Methods: An interdisciplinary team developed 2 interactive case-based training modules for Medicaid providers focused on implicit bias related to race and socioeconomic status (SES) and sexual orientation and gender identity (SOGI), respectively. The simulations combine experiential learning, facilitated debriefing, and game-based educational strategies. Medicaid providers (n=18) participated in this pilot study. Outcomes were measured on 3 domains: training reactions, affective knowledge, and skill-based knowledge related to implicit biases in race/SES or SOGI.

Results: Participants reported high relevance of training to their job for both the race/SES module (mean score 4.75, SD 0.45) and SOGI module (mean score 4.67, SD 0.50). Significant improvement in skill-based knowledge for minimizing health disparities for lesbian, gay, bisexual, transgender, and queer patients was found after training (Cohen d=0.72; 95% CI -1.38 to -0.04).

Conclusions: This study developed an innovative smartphone-based implicit bias training program for Medicaid providers and conducted a pilot evaluation on the user experience and preliminary efficacy. Preliminary evidence showed positive satisfaction and preliminary efficacy of the intervention.

Keywords: Medicaid; XR; augmented reality; efficacy; extended reality; gender identities; gender identity; gender preference; gender preferences; health care; health care provider; health care providers; implicit bias; implicit bias training program; innovative; mHealth; mobile app; mobile application; sexual orientation; sexual orientations; smartphone; socioeconomic; training; virtual reality.