Featured in Integrated Healthcare Executive
In Philadelphia, public health officials can track the rate of COVID-19 vaccinations by ZIP code, age, race and gender by capturing demographic data at the point of registration. It’s a move that helps officials pinpoint inequalities in vaccinations by neighborhood and population and tailor intervention efforts to help eliminate gaps in care.
However, the city has no way to track residents who are vaccinated outside city limits. While Philadelphia’s data-based view is imperfect, it demonstrates the importance of a complete and trusted patient record in supporting efforts to erase health inequities.
Demographic Data Matter
During the early months of the pandemic, we saw the impact of a limited data view on COVID-19 testing and treatment. The Centers for Disease Control & Prevention (CDC) discovered that social determinants of health (SDOH), such as: access to education, economic stability and access to stable housing, significantly affect COVID-19 risks and health outcomes. Discrimination against racial and ethnic groups also was a key determinant in who was tested for the virus and, if they were to get sick, the severity of their symptoms, the CDC found.
In Chicago, for example, Black residents disproportionately suffered from the virus. CDC data shows that Black residents accounted for 36% of COVID-19 cases and 52% of deaths during the first two months of the pandemic, yet they comprise just 30% of the city’s population. In the state of Washington, 31% of the state’s COVID-19 cases initially affected Latinos, who comprise just 13% of the state’s population. Factors that have contributed to these and other health disparities in Washington, such as who gets access to the vaccine, include lack of trust in the health care system and the vaccine.
The ability to identify health inequities is crucial to closing gaps in care—and not just during a crisis. To do so, public health organizations, providers and health plans must have access to demographic data that is accurate, timely and complete. This includes patients’ addresses, including ZIP code; phone number; race and ethnicity; age; and gender.
But, breakdowns in demographic data capture are common. During the first months of COVID-19, when outpatient testing sites were set up quickly and hospitals and urgent care centers received a deluge of requests, a number of tests were missing patient contact information. For about half of COVID-19 cases nationally, information regarding race and ethnicity were unavailable. Errors in demographic data capture also were common.
These discrepancies make it difficult not only to contact patients with their test results, but also to determine who is sick and where. They also impede efforts to direct the right resources to the right communities to strengthen access to care and health outcomes for all.
Creating Equitable Access With Data
How can health care’s key stakeholders level the playing field when it comes to health care access? It starts by ensuring stakeholders across the continuum have the right data to assess where gaps in access exist and the types of interventions that are most likely to make an impact. Contact data also is essential for follow-through and continued engagement, now and after the pandemic is over.
To build a more solid data foundation, here are three approaches health care leaders should consider.
1. Bolster data collection requirements. Requirements that providers collect and submit data around race and ethnicity are less likely to be followed when these orders are not enforced. That’s a scenario Pennsylvania found itself in this past February, when lack of enforcement meant health officials had an incomplete picture of inequities related to vaccine distribution. For data collection requirements to “stick,” the consequences for failing to follow through must be clear, and officials must commit to enforcement.
2. Enrich demographic data with lifestyle data. When providers and health plansinvest in software that provides insight into social determinants of health such as economic stability, access to stable housing and reliable transportation, and education level, they gain a more nuanced view of the patient. They also heighten their understanding of the factors and behaviors that increase health risk and the resources needed to improve health for specific individuals and populations.
For example, one Midwest health system’s investment in data enrichment software provides the basis for highly personalized digital health experiences that help consumers access expert advice through online channels. Consumers also are paired with apps and chatbots that they can use to monitor and manage their conditions. When data points to issues of concern, clinicians can connect with patients in real time to offer informed support.
3. Invest in data analytics that pull deeper insight from consumers’ clinical, financial and demographic data. This positions health care organizations to pinpoint which individuals and populations are in danger of adverse health outcomes and determine how to most effectively deploy resources.
For example, one large health system in the Southwest uses predictive analytics to look for patterns in consumer behavior predict consumers’ health care needs. During the pandemic, data scientists use this data to forecast when the system will experience a surge, which facilities patients will go to, and how to adjust staffing and supply chain to meet consumer demand. Key to the organization’s approach: development of a single patient identifier for each of the system’s 7 million patients.
In Conclusion
By bolstering capabilities for data capture, enrichment and analysis, health care stakeholders become better positioned to address and eliminate inequities in health care access and care that threaten vulnerable populations.