Henry Ford + MSU Implicit Bias Education

This virtual Henry Ford + MSU Implicit Bias Education session explores how social, economic, and environmental conditions influence health outcomes and intersect with unconscious attitudes held by healthcare professionals.

Through Michigan-specific data, real-world case scenarios, and self-reflection activities, participants examine how automatic beliefs and structural inequities contribute to disparities in care. Gain practical strategies to recognize and reduce biased behaviors, enhance culturally requirement, responsive care, and promote equity at both individual and systemic levels.

This session will be presented by Henry Ford Health System Director of Nursing Research Nutrena Tate, Ph.D., MBA, APRN, CPNP-PC.

Targe Audience

Registered nurses, registered nurses in specialty area, advanced practice providers, physicians, and other healthcare workers.

Continuing Educaiton 

• Contact Hours: Participants completing the event and the post-session evaluation will receive 1 continuing nursing education contact hour. Meets the content requirements for 1.0 contact hour of State of Michigan implicit bias continuing education (CE) credit. Michigan State University College of Nursing is accredited as a provider with distinction of nursing continuing professional development by the American Nurses Credentialing Center’s Commission on Accreditation. Learn more at nursing.msu.edu.
• Learning Outcome: At the completion of this learning activity, participants will state increased knowledge of how unconscious attitudes and social determinants of health contribute to health disparities and provide a takeaway example of a strategy they can use to promote equitable and bias-aware care.

Michigan State University College of Nursing is accredited as a provider with distinction of nursing continuing professional development by the American Nurses Credentialing Center’s Commission on Accreditation. Learn more at nursing.msu.edu.

Zoom Information

Click here for the Zoom link.
Meeting ID: Meeting ID: 912 7525 7979
Passcode: 366898