From hospitals and clinics to data warehousing companies, data is the key to all sorts of improvements. As hospitals and clinics identify opportunities to improve provider performance that will lead to better patient care, and private-sector companies identify opportunities to improve team member performance that will lead to better overall company performance, implicit biases can be a stumbling block on the path to improvement.
Implicit bias occurs when people act on the basis of preconceived prejudices and stereotypes without intending to do so. Everyone has implicit biases, however, African Americans, women, and the LGBTQ+ community are more likely to be negatively impacted. In the world of healthcare, the effects of implicit biases can be even further reaching—such as preventing a patient from receiving the right care.
Allina Health, a not-for-profit health system in Minneapolis, and Health Catalyst, a leading provider of healthcare data and analytics technology headquartered in Salt Lake City, used data to identify potential areas of bias and intervene to ensure equitable treatment for all.
At Allina Health, these implicit biases proved to be a barrier to patient care. They negatively impacted patient access to important resources like hospice care. Similarly, at Health Catalyst, CEO Dan Burton, identified an opportunity to increase the number of women in leadership positions. This opportunity is consistent with the general lack of diversity in the technology sector.
Leadership teams in both organizations understood the value of addressing implicit biases to improve patient outcomes for Allina Health and improve gender representation at Health Catalyst.
Read how Minneapolis-based Allina Health has been working toward health care equality after finding a disparity with its Black patients.
In 2018, Allina Health’s Health Equity Leadership team reviewed a list of the 35 top identified disparities based on data within the health system and prioritized the hospice referrals disparity to improve in 2019.
Data revealed that the African American populations receiving care at Allina Health were not enrolling in hospice programs when they were eligible. As improvement teams dug deeper into the data to understand the root cause behind the lack of awareness, they realized that hospitalists weren’t referring African Americans who were eligible for hospice at the same rate as other populations.
The team at Allina Health—led by Vivian Anugwom, Program Manager of Health Equity, and Emily Downing, MD, VP of Medical Operations at Allina Home Care Services—presented the data to different provider groups to increase awareness of the disparities. Seeing the data first-hand prompted thoughtful discussion between all members of the care team about the reasons behind the disparities and the steps to fix it. The data showed the disparities and prompted discussion about why the disparities exist and what measures the Allina Health team could implement to address the disparities. In the discussion groups, providers said things like, “I’ll be honest, I’m probably not going to bring hospice up because in the past, I had a negative experience.”
Through provider discussion groups that fostered honest discussions and feedback and reviewing existing research around potential barriers to accessing end-of-life care for African Americans, the team realized that implicit biases were preventing providers from referring African American patients to hospice.
As a result of these discussion groups, the implicit bias training program was created to target providers, starting with hospitalists, who frequently provide care for hospice-eligible patients. Creating awareness of implicit biases would empower Allina Health’s hospitalists to deliver better care and improve hospice access for African Americans seeking better end-of-life care.
With the support of the leadership team and hospitalist leadership, the team created and implemented trainings to help hospitalists become more aware of their biases. The leadership team agreed to include the implicit bias training as one of the options in the required annual continuing education package for hospitalists.
Many of the hospitalists were curious about the topic, while others admitted they were only there to receive credit. By the end of every implicit bias training session, every hospitalist left with an identified personal implicit bias and the tools to address it.
After the training, many providers made comments such as, “I didn’t know I needed this training; this will make a difference in my care.”
Using data to identify disparities is only the first step to address implicit biases. The next step is to create a training program that will have the highest likelihood of success, incorporating four best practices:
Allina Health offered three implicit bias training sessions in 2019 and plans to offer additional trainings in 2020. The leadership team also decided to implement the trainings in other areas of the organization based on data-backed disparities. Allina Health has also committed to developing trainings for providers geared toward all minorities, not just African Americans.
While Allina Health waits to see the results of their new efforts to increase health equity, efforts to leverage data to identify disparities in care will continue.
Hospitals are not the only organizations benefitting from implicit bias training and diversity improvement efforts. Although Health Catalyst is a healthcare data analytics company whose team members don’t work directly with patients, the leadership team recognized that implicit biases are ubiquitous—no matter the industry—and that the technology sector in particular struggles with a clear lack of gender diversity.
Like Allina Health, the Health Catalyst leadership team implemented implicit bias trainings to tackle implicit biases and improve gender parity.
The goal of the implicit bias training was two-fold: increase awareness of team member biases and then give team members the tools to overcome their biases.
First Step in Implicit Bias Training: Create Awareness
The first step in creating implicit bias awareness was to provide training to all team members. The first round of implicit bias training at Health Catalyst focused on the basics of implicit biases—what they are and how we develop them. Once people understand the fundamentals behind implicit biases—and that everyone has them—team members were prepared for a deeper dive into their biases.
As part of the initial wave of training, Health Catalyst also shared specific examples of common microaggressions in the workplace, and provided examples of alternate, more acceptable, approaches to those common scenarios.
Second Step in Implicit Bias Training: Equip Team Members with the Right Tools
Once team members are aware of their own bias, the next step is to equip team members with the right tools to overcome their biases. Health Catalyst’s initial training program provided a framework for decreasing bias in decision-making and speaking up when inequities are identified. Similar to the approach at Allina Health, the training included a challenge to each team member to select one specific behavior to improve or change. Coming in 2020, the company also plans to roll out a second round of implicit bias training to focus on additional tools and actions to support an inclusive culture.
Beyond classroom training, Health Catalyst took a multi-faceted approach to tackling implicit bias by creating affinity groups to build a community for underrepresented groups like women in technology and LGBTQ+ team members. Through affinity groups, team members in underrepresented groups have had opportunities to lead, have their voices heard, and share learnings with organizational leaders.
After nearly four years of regular discussions and trainings about overcoming biases and improving diversity, Health Catalyst has seen major improvements:
Although the above metrics focus on gender, which has been a primary focus for Health Catalyst during the last four years, the next trainings will expand to focus on improving equity for other underrepresented groups.
Whether within a health system, clinic, or a data and analytics company, addressing implicit biases with team members (from providers and clinicians to data analysts) can improve performance and equity because it allows team members to better understand personal roadblocks to development—inherent prejudices. When team members understand these limitations, steps can be taken to overcome them as care is provided to patients and work occurs in team settings.
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