July 08, 2020, 04:28 pm News Staff – Considerable evidence demonstrates the effects of implicit bias on access to and quality of health care. For nearly two decades, research has shown that people of color are more likely to receive lower-quality health care than white individuals even after accounting for factors such as insurance coverage, income level, age and severity of health conditions.
The COVID-19 pandemic has only brought more attention to the issue. Ongoing research shows that Black, Indigenous and Latino American patients have all experienced infection, hospitalization and fatality rates higher than those seen in white patients, and implicit bias has been suggested as contributing to the racial disparities seen in COVID-19 outcomes, at least among Blacks.
To increase awareness about this critical issue and its effects on patient care and public health, the AAFP's Center for Diversity and Health Equity, through a grant from the AAFP Foundation, has awarded 12 Academy chapters the opportunity to host their own implicit bias training events starting this fall.
"Reducing health care disparities and achieving health equity requires multisector approaches by individuals, organizations and industries," said Danielle Jones, M.P.H., the Academy's director of diversity and health equity. "Physicians can do their part by first acknowledging that implicit bias exists and developing skills to reduce its effects in the clinical setting."
The 12 AAFP chapters to receive funding are California, Colorado, Illinois, Kansas, Maryland, Minnesota, Missouri, New Jersey, Ohio, Oregon, Pennsylvania and Utah. According to Jones, each chapter will receive $10,000.
Although the events are still in the planning stages and specific details have yet to be finalized, Jones said that in most cases, the training will consist of a combination of lectures and small-group discussions. Some events will be offered online, some will be offered in-person (depending on restrictions associated with the COVID-19 pandemic), and some will be offered live but with the ability for members to participate via a webcast or livestream. Some chapters may start to offer implicit bias training on a regular basis as part of that chapter's ongoing CME schedule.
The intended audiences for these trainings also may vary. According to Jones, some chapters plan to offer their events to students and residents in partnership with medical schools, as well as to nonmembers, including nonphysicians.
Even as these chapters develop their own implicit bias training events, all AAFP members are invited to become familiar with the Implicit Bias Training Guide developed by The EveryONE Project and published in January 2020. As was the case with many Academy resources, the guide's significance wasn't fully recognized during the early days of the COVID-19 pandemic, but as the data has begun to reveal the stark racial and ethnic disparities in hospitalizations and mortality rates, the guide's relevance has become increasingly clear.
As a refresher, the Implicit Bias Training Guide consists of multiple components. These include
The guide also includes four customizable PowerPoint presentations that FPs can use to adapt implicit bias training to specific audiences and settings. These presentations provide a general overview of implicit bias, information about the science behind it and its health effects, strategies to mitigate implicit bias in clinical practice, and a step-by-step guide to creating a safe and inclusive learning environment.
"As a benefit of membership, this resource is for members only," said Jones. "As other specialties and organizations seek to offer this type of education, they will undoubtedly look to family physicians as the subject matter experts."
Finally, Jones noted, although the guide was developed with primary care physicians and their practice teams in mind, it is designed in such a way that it can be used by health care professionals from every specialty, as well as administrators and those who provide care to patients who are at increased risk of experiencing implicit bias.