# Counter Bias Training Simulation (CBTsim) Healthcare: A Novel Approach for Reducing the Impact of Implicit Bias on Healthcare Delivery > **NIH NIH R01** · WASHINGTON STATE UNIVERSITY · 2024 · $426,262 ## Abstract ABSTRACT Bias in how clinicians form relationships with and treat patients exists based on patient race, ethnicity, gender, socio-economic status, LGBTQ+ status, disability, addiction and other factors. Implicit or unconscious biases (the ways in which our beliefs, attitudes, and values influence how we see the world and the people in it) are widespread and affect patients’ health outcomes. Our team has developed Counter Bias Training Simulation (CBTsim), an innovative and unique training program that uses simulation to reduce the impact of implicit bias on how people interact and make decisions that affect others. Versions have been developed for policing and military professionals to enhance their ability to interact with diverse groups of citizens in unbiased ways. CBTsim has great relevance to healthcare, for which existing bias trainings are typically video or lecture based, and may be ineffective at reducing the impact of bias on patient-clinician relationships (PCR) and consequent healthcare delivery. The purpose of this study is to develop “CBTsim Healthcare” and evaluate its effectiveness at reducing bias in how nurses treat their patients. The proposed study will be jointly conducted in the Washington State University College of Nursing Simulation Lab and Providence Medical Center in Spokane. First, we will develop CBTsim Healthcare scenarios based on an extensive review of the literature on healthcare disparities. Then, we will conduct a randomized control trial with 100 nurses to test the effectiveness of CBTsim Healthcare. Nurses will receive 2 hours of baseline testing, then 50 (treatment group) will receive a 4-hour CBTsim Healthcare training and the other 50 (control group) will watch a 1-hour video on implicit bias in healthcare, typical of current standard practice. Then, all 100 nurses will receive 2 hours of post-intervention testing. Testing will include the implicit association test (IAT) to measure implicit bias, questionnaires to measure prejudice, and patient care scenarios using simulation mannequins to test for bias in PCR and other aspects of healthcare delivery. Finally, we will track treatment and control group nurses in the hospital for 6 months following the intervention to assess disparities in healthcare, measured using patient satisfaction with nursing care scales (quantitative measure) and narratives to document experience of PCR (qualitative measure). All major health agencies have identified reduction of implicit bias in healthcare and resulting minority health disparities as a matter of extreme importance and urgency. The economic impact of health disparities is an estimated $230 billion a year, and the social justice impact is immeasurable. Our current focus on nurses is due to our existing relationship (e.g., on AHRQ R01 HS025965-01), however we anticipate that CBTsim Healthcare could be modified for other healthcare professional groups and could have a revolutionary impact on reducing bias in healthcare d... ## Key facts - **NIH application ID:** 10934324 - **Project number:** 5R01MD018467-02 - **Recipient organization:** WASHINGTON STATE UNIVERSITY - **Principal Investigator:** Lois James - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $426,262 - **Award type:** 5 - **Project period:** 2023-09-22 → 2026-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10934324 ## Citation > US National Institutes of Health, RePORTER application 10934324, Counter Bias Training Simulation (CBTsim) Healthcare: A Novel Approach for Reducing the Impact of Implicit Bias on Healthcare Delivery (5R01MD018467-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10934324. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*