From the 2022 HVPA National Conference
Alaysia Phillips MPH (Johns Hopkins Health System), Rohit Toteja MSHI, MHA
Johns Hopkins Medicine’s (JHM’s) Office of Diversity, Inclusion, and Health Equity (ODIHE) institutional strategic priority is to accurately capture patient demographics, which is driven by National Culturally and Linguistically Appropriate Services (CLAS) Standards (2000, revised 2013) (Source 5). Under “Engagement, Continuous Improvement, and Accountability,” health and health care organizations should “collect and maintain accurate and reliable demographic data to monitor and evaluate the impact of CLAS on health equity and outcomes and to inform service delivery.”
Describe a process for increasing the accurate collection of race, ethnicity, and language (REaL) dataDiscuss lessons learned and best practicesExplain how to stratify data to identify health equity improvement opportunities
Methods and Results
JHM’s ODIHE held focus groups with clinicians, community members, and patients. Results showed that patient access services (PAS) and registration staff did not understand the importance of accurately collecting REaL data or how this data was used. In 2017, baseline data indicated that 10.48% of race and 18.53% of ethnicity data were blank or missing. In 2018, the ODIHE instituted a 2-hour training “Collecting Patient Sociodemographic Data” for PAS. During the first few months of COVID-19, reporting of positive cases, testing, and death by race and ethnicity was non-existent. We realized that when this data was starting to be reported in 2020, that our own COVID-19 call center was not collecting REaL data, which was the impetus for relaunching this intervention. There was a large percentage of missing data for Black, Indigenous, and other communities of color. Between 4/30/2020-6/2/2020, ODIHE conducted 17, 2-hour live trainings via Zoom for 363 staff from the COVID-19 Call Center, PAS, and Johns Hopkins Community Physicians Call Center staff. In addition, during fall 2020, JHM Language Services completed an additional 22 training sessions for 191 Emergency Department registration staff for all JHM (excluding the one hospital that was not yet live on EPIC).
Conclusions and Clinical Implications
ODIHE tracked the REaL data from the Epic EMR warehouse and created various demographic dashboards. “Blank” or “missing” REaL data has been consistently declining over the years, due to the interventions of training employees to enter REaL information for patients. For example, for The Johns Hopkins Hospital, “blank” data for Race decreased from 10.48% in 2017 to 0.47% in 2021, while for ethnicity, “blank” data decreased from 18.53% in 2017 to 0.47% in 2021. Similarly, for Johns Hopkins Bayview Medical Center, we noted that our Spanish-speaking patients increased by 100% from calendar years 2017 to 2021. This was used to create the business case statement for increased resources for Spanish-language interpreters and qualified bilingual staff.
The ODIHE team is currently standardizing REaL data by curating REaL data views on the Epic EMR warehouse for analytics teams to use across JHM. The ODIHE team is also tracking which job positions are capturing the REaL data for ongoing employee training. Accurate REaL data collection is a preliminary step for achieving health equity among our most vulnerable populations and a core step to promoting patient-centered care and reducing health and health care disparities.
JHM’s ODIHE will partner with internal and external stakeholders to develop tailored interventions using REaL data to reduce health/health care disparities and improve patient outcomes. Reduction of disparities and success of tailored interventions will be tracked in a several reporting mechanisms.