Using an algorithm to be an upstander in the face of discrimination

The events of the past year have been a potent reminder of the prevalence of racism and discrimination that non-White Americans deal with on a daily basis. The recent statement from the Centers for Disease Control and Prevention that racism is a public health issue cements the need for the medical community to address these issues with patients, but what happens when the person experiencing the racism or discrimination is the health care provider or a colleague, and the one discriminating is a patient or a family member? At the virtual 2021 Pediatric Academic Societies meeting, Sahar Rahiem, MD, MHS, a resident at Texas Children’s Hospital and Baylor College of Medicine in Houston, discussed a model that she had created with colleagues to turn someone from a bystander to an upstander.

Rahiem and her colleagues began their research because discrimination from families or patients is common, accounting for 22% of discrimination faced by trainees, but poses a conundrum. Clinicians want to have a good rapport with patients because that improves care outcomes and likelihood of treatment compliance, but at the same time, they should be able to speak up if the patient or patient's caregiver are making biased comments. Futhermore, colleagues need to be supported, and a lack of response from a fellow clinician who was a witness to the exchange could be seen as not caring about the discrimination or even agreeing with the discrimination.

The model created by the team was taught in a workshop and included 3 steps:

• setting the stage where the clinicians were told about what was going to occur
• playing out a set scenario that fit 1 of 3 possible discrimination types, (1) discriminatory statement eg, “I’m so glad that you’re a white doctor,” (2) a discriminatory request eg, “Could we have a white doctor please,” or (3) mistaken identity eg, a patient or family member acting like a Black doctor is on the janitorial staff
• participants having a debrief period where they discussed what had happened during the role playing session. Each discrimination type had an algorithm for how to respond.

Rahiem presented how the algorithm would work with a discriminatory statement. The first step is assessing the medical condition of the child. If the condition is unstable, clinicians should provide urgent medical treatment and have a debrief session later. If the child is stable, the clinician should determine how to respond based on whether the statement was overly discriminatory or was a microaggression. In the case of overt discrimination, a clinician should use “I” statements and make the position clear with a statement like “I’m asking you to not use that language while your child is being treated by our medical staff.” In cases of microaggressions, the clinician should reflect the statement back with an “I” statement such as “What I heard is that you think…” In both cases, the clinician should then move the conversation back to the child’s medical care. Following an encounter, a clinician should tell other colleagues about what occurred and a debriefing should happen.

According to Rahiem, the participants in the study found it very helpful with many indicating that the lessons learned in the workshop would lead to changes in clinical practice. Many of the participants also said that the algorithms used were helpful, with many commenting that they felt better equipped to handle situations in the future because of the

Reference

1. Rahiem ST. Moving from bystand to upstander: responding to discrimination from patients/families. Pediatric Academic Societies Meeting 2021; May 1, 2021; virtual. Accessed May 1, 2021.