Racial and ethnic minorities have unequal access to healthcare and are more likely to experience health disparities. Collecting accurate and meaningful race, ethnicity and language (REaL) information is an ongoing challenge in healthcare – which is key to improving access to care.

This is a particularly hot topic as Healthcare IT News parent company HIMSS celebrates its annual Global Health Equity Week from October 23-27.

THE PROBLEM

For health system Denver Health, the first hurdle was to understand the pre-intervention landscape when it comes to REaL data in its own institution.

“We knew we had issues collecting this data, but we first needed to define the process of how to evaluate the quality of this data,” said Dr. Cory K. Hussain, associate chief medical information officer for health equity and clinical effectiveness at Denver Health. “Defining those metrics was really challenging. Once we had figured that barrier, we found we had some real issues in our REaL data.

“Our next step to determine the root causes that were driving these data quality issues was equally if not more challenging,” he continued. “It was studying system-level processes, especially those that can involve a lot of variation that can be taxing.”

Prior to implementing a REaL intervention in its EHR, Denver Health’s system-level settings required it to change some of its workflows.

“This was a difficult task as it required getting both buy-in from our frontline staff and then training them on this new method,” Hussain said. “This was only possible due to the dedication of the REaL team that was always embedded in the registration staff that did all the heavy lifting of making this program a success.”

PROPOSAL

Healthcare disparities continue to plague the Colorado health system. Many are systemic, and the only way to alleviate them is to understand the patients who are being affected and the systems in place that would perpetuate this.

“You can’t change something you don’t have reliable data on,” Hussain explained. “Knowing who our patients are is an essential first step in the right direction. However, many organizations, ours included, grapple with missing data and quality issues. If we cannot trust our data, we cannot devise interventions in the right population that would have the maximum impact intended with the limited resources that are available.

“Create training that addresses both how to ask these questions and how to respond to concerns when these questions are asked.”

Dr. Cory K. Hussain, Denver Health

“REaL was the necessary first step to do this work,” he continued. “Once we have reliable data, we can see which subgroups of our populations here at Denver Health are impacted. That would help us evaluate our health system delivery to these populations and any modifications that can reduce healthcare gaps in these vulnerable subsets of the population.”

MEETING THE CHALLENGE

Today, Denver Health is using this information to understand health disparities in its patient cohorts when it comes to cancer screenings or diabetes/high blood pressure control.

“Clinical champions and key stakeholders involved in health equity are using this data to identify gaps in our metrics and system processes that can improve on these,” Hussain said. “They are using this data as a filter and seeing all health outcomes using this data under the ‘lens of health equity.’

“At this stage, our demographic data on ethnic background is limited to Denver Health systems only,” he continued. “Epic has a module to exchange information with all other Epic hospitals using a functionality called Care Everywhere. This data field, however, has not been mapped currently to exchange this information.”

There are future plans by the office of management and budget to shift to more granular ethnic backgrounds being collected and additional race categories.

“We already are ahead of the curve by collecting this data currently,” Hussain noted. “At this moment, we have not integrated and are not sharing this data with any vendors as there are no federal reporting requirements for ethnic backgrounds. However, we see this changing and with the ability to exchange social determinants of health data, we will probably see liberalization of this data usage in the United States.”

RESULTS

Race and ethnicity data was missing for more than 13% of Denver Health patients. The new methodology has reduced this to less than 1%.

“We now have ethnic background data on more than 700,000 of our patients, and that allows us to see the richness of our population beyond the broad race and ethnicity categories and allows us to identify subpopulations within certain race categories that might still have healthcare gaps,” Hussain reported.

ADVICE FOR OTHERS

“Evaluate your current state of data and what process measures are in place or lacking that are leading to the holes in data,” Hussain advised. “We were only successful as we found out that there were significant gaps and barriers in how this data was being collected.

“By using quality improvement methodology, we were able to underpin our processes to specifically address pain points within our organization that would have the highest return of investment,” he continued. “This allowed us to achieve the best results we could have imagined. Every organization may have similar or nuanced processes that need to be improved.

Also consider engaging the communities served by asking how better to gather such information, he added.

“Devise patient-facing and patient-friendly technologies that explain what race, ethnicity and ethnic backgrounds are,” Hussain recommended. “Create training that addresses both how to ask these questions and how to respond to concerns when these questions are asked.

“But most of all, create a burning platform for such a project that gets the attention and buy-in from all the stakeholders in your organization, all the way from the C-suite to the registration staff that is doing the work,” he concluded.

Follow Bill’s HIT coverage on LinkedIn: Bill Siwicki
Email him: bsiwicki@himss.org
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