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Case Study 2019-03-16T09:46:59-04:00

Improving Race, Ethnicity and Language Data Collection With Staff Training

The Situation

In healthcare, the data tell the story. Patient populations are more diverse than ever. Accurate race, ethnicity and language (REAL) data is a key driver of quality improvements. REAL data helps hospitals provide better care, which leads to better health and lower costs.

With Medicare and Medicaid reimbursements now tied to quality, obtaining accurate REAL data is a top priority at hospitals across the country.

Hospital leaders are realizing that they need to make major changes to more accurately document race, ethnicity and language data. So the question is: How do you provide healthcare transformation training so employees can obtain this data from your diverse patient population?

The Solution

In 2010, More Inclusive Healthcare was hired by The Greater Cincinnati Health Council to work with 24 member hospitals on improving the accuracy and reliability of REAL data collection. Our goals were to:

  • Improve quality of care for all patients.
  • Provide data collection training that was engaging, informative and easy to use.
  • Ensure sensitivity when employees asked patients questions about race, ethnicity and language.
  • Guarantee consistency in data collection techniques across multiple shifts in different areas of each hospital.

Emily Seitz-Pawlek, who is vice president of Revenue Cycle at Catholic Health Partners, was on a multi-disciplinary leadership team that guided the REAL data collection project.

“This work is absolutely necessary to improve quality and the patient experience, and also to lower costs. We ask for race, ethnicity and language data so we can provide the best care.” Seitz-Pawlek says.

More Inclusive Healthcare’s eLearning Module

More Inclusive Healthcare developed an eLearning module for REAL data collection training that reduced variability in REAL data collection and ensured complete, accurate data.

Seitz-Pawlek says that Lisa Sloane of More Inclusive Healthcare was thoughtful and creative in her approach.

“This work is very complicated and involves layers of personnel and systems,” Setiz-Pawlek says. “Lisa reached out and used her large network to brainstorm, guide conversations and keep us on track.”

Employees could easily log in to the REAL data eLearning module at any time. They used it to understand how to sensitively and consistently collect data, and why this is important. The eLearning module also:

  • Met meaningful use requirements and Joint Commission recommendations.
  • Used principles of adult learning.
  • Was seamlessly uploaded to 24 member hospitals’ learning management systems.

How We Got It Done

Our first priority was obtaining critical support from health systems’ top executives. Then we worked with member hospitals to create a disparities resolution program that included standardizing REAL data and training staff in patient self-reported methodology.

We ensured that all member hospitals were using the same categories and that the categories were consistent with Office of Management and Budget guidelines. Our final step was developing an eLearning module that trained hospital and primary care staff in patient self-reporting methods.

The Results

The REAL data project trained more than 1,200 registration staff across Greater Cincinnati. It yielded immediate and dramatic improvements in race, ethnicity and language data collection.

This was our timeline for project implementation by quarter-years:

Q3 2009
Representatives from 24 participating hospitals were nominated by hospital/health system CEOs.

Q4 2009
GAP analysis performed to determine the hospital/system changes needed to standardize data-collection protocols.

Q1 2010
Participating hospitals adjusted their registration systems.

Q2 2010
We trained registration staff in patient self-reporting methods.

Q3 2010
A community relations plan was implemented, with each hospital participating.

Q4 2010
Participating hospitals began collecting REAL data using patient self-reporting methods.

TABLE 1: This table shows aggregated hospital reported pre-program implementation data as of Q4 2010. Compare that to our Q1 and Q2 2011 results, after in-person training and project implementation.

IndicatorCases Q4-2010Percent with IndicatorCases Q1-2011Percent with IndicatorCases Q2-2011Percent with Indicator
Race56,86568.88%57,62790.25%57,48390.42%
Ethnicity56,86540.79%57,62769.38%57,48366.57%
Language56,86559.10%57,62760.54%57,48375.82%

Source: The Greater Cincinnati Health Council

TABLE 2: This table shows the results well after implementation of our eLearning module in 2012. Quarters one and two are combined.

Percent of inpatient discharges with indicator present by quarter

IndicatorCases Q1 & Q2 – 2012Percent with Indicator
Race112,61299.97%
Ethnicity112,61297.84%
Language112,61290.59%

Source: The Greater Cincinnati Health Council

The REAL data collection eLearning module could easily be uploaded to each hospital’s learning management system. It became an integral part of staff training during orientation, and also as an annual refresher.

“All of us are in healthcare to impact patients’ lives for the better,” Seitz-Pawlek says. “When we step back and think about how people are impacted by our work, why wouldn’t we want to do everything we can to have better outcomes?”