How Patient Matching Can Help Eliminate COVID-19 Disparities in Care

By Joaquim Neto

For public health officials, it’s one of the worst scenarios imaginable: knowing that someone has tested positive for an infectious disease, but being unable to contact that person to share the results.

Missing contact information on COVID-19 laboratory results prevents individuals with positive test results from quickly receiving needed treatment. It also heightens the risk that others will be exposed to the coronavirus. Yet this exact problem has played out repeatedly in 2020. Up to half of COVID-19 lab tests are missing critical contact information such as addresses or telephone numbers, a Duke University study found. Some public health experts say the number could be as high as 80% of test results.

During the first months of the pandemic, these breakdowns in data capture prevented officials from directing lifesaving resources to populations most at need during a public health crisis, such as people who lack stable housing, certain racial and ethnic groups, and those who live in high-risk ZIP codes.

Why missing data heightens risk

Certain vulnerable communities, like those who are homeless or live in high-risk ZIP codes, have seen higher rates of COVID-19 infection. But unless public health agencies and healthcare providers have access to the right data at the right time, their ability to pinpoint where higher levels of risk exist and work to contain the virus is thwarted.

For example, in Chicago, Black residents comprise 29% of the city’s population, yet by early April, they accounted for more than 70% of COVID-19 deaths, a WBEZ analysis showed. A deep dive into the data found that 30% of records were missing data describing an individual’s ethnicity or race. About 90% did not track individuals’ place of employment. Without information such as this, city officials were hard pressed to determine which populations were most at risk and where infections were emerging. The missing data also prevented officials from deploying highly targeted initiatives to stop the spread of disease, such as providing personal protective equipment to certain professions or expanding testing within specific communities. These interventions are critical to safely support economic recovery.

In the state of Washington, where the first coronavirus death occurred, efforts to detect COVID-19 hotspots initially were hampered by late, incomplete, and inaccurate reports. When data did come in, it flooded the state’s disease reporting system. The state struggled with duplicate reports, fielding 2,000 duplicate reports in a single day. Without the ability to track rates of infection within communities or identify those most vulnerable to COVID-19 illness or death, public health officials faced tremendous difficulty determining which populations needed more support than others due to factors such as:

• The impact of underlying health conditions and lack of health coverage on COVID-19 susceptibility, treatment rates, and mortality
• Social determinants of health such as lack of stable housing, food insecurity, and poverty
• The percentage of a population who work in service jobs outside the home
• Population density

Why do gaps in demographic data occur during public health crises like COVID-19? For one, the number of people who need testing has overwhelmed laboratories and health systems, prompting drive-through testing sites to be erected quickly. With the onslaught of patients and a shortage of workers, many laboratories and hospitals have found themselves without the resources to capture data for all of the contact information fields. That’s left it up to public health agencies to fill in the gaps—in some instances, using Bureau of Motor Vehicles data—if an individual tested positive, an approach that has been less reliable than officials anticipated.

Concerns for patients’ privacy also have kept laboratory staff from pressing for more contact information than they believe is necessary, given the stigma associated with a positive test.

In addition, the lack of data standards and infrastructure have made it difficult for public health agencies and healthcare organizations to share vital information with each other. For instance, a New York Times article found physician practices may not have the digital systems to communicate with labs regarding a positive result, while laboratory software may not capture the demographic information public health officials need for a robust response. In small laboratories, cost concerns prohibit investment in electronic systems that are interoperable with hospital systems. As a result, these laboratories still rely on faxes to share results.

The impact: COVID-19 has had a severe effect on certain populations, yet public health officials have been ill equipped to respond. As the pandemic has unfolded very differently in communities of color and in communities with high levels of poverty and rates of chronic conditions, holes in COVID-19 data have kept public health officials from responding quickly. Ultimately, data gaps have contributed to high rates of infection, poor health outcomes, and higher-than-necessary death rates among those most vulnerable.

And because we still don’t have a complete picture of the demographic factors that contribute to susceptibility to COVID-19, our ability to respond with agility in protecting vulnerable populations depends largely on the quality of data analysis and data partnerships in each state.

Getting there from here

How can public health officials and healthcare organizations close COVID-19 data gaps that lead to disparities in care and outcomes? Here are three approaches leaders should consider.

1. Give teeth to federal requirements for COVID-19 data collection. While the federal government released guidance for laboratory collection of demographic data this past September, the government didn’t state the ramifications that laboratories would face if they did not comply with the guidance. While federal guidance is a step in the right direction, outlining the consequences for failure to follow this mandate will be critical to securing laboratory buy-in.
2. Partner with universities around data analysis. In Illinois and Washington, for instance, we’re seeing exciting partnerships form to analyze demographic data related to COVID-19 outbreak, risk, and outcomes. For example, this past April, Washington State University researchers created a “risk index” that mapped out areas in the state that were most vulnerable to COVID-19 transmission based on mortality rates for chronic conditions. At DePaul University, data science researchers partnered with the city of Chicago to fill in missing demographic information on lab reports and strengthen the city’s public health response. Their efforts moved the needle on capturing data related to race, decreasing the number of reports where race was reported as “unknown” from 47% to 11%.
3. Pair patient databases with software for verifying patient identity. Even before the pandemic, hospitals faced an urgent need to improve patient matching. Now, with public health officials spending hours searching databases and motor vehicle records to find patient contact information, the need for a solution that eliminates manual searches is critical. During the pandemic, hospitals, state health information exchanges, and public health agencies are pairing EHRs with a master person index (MPI) that can leverage consumer data to match the right record to the right person based on real-time data—this saves time and provides the data needed for crucial epidemiological analytics.

By taking a multipronged approach grounded in data integrity, leaders can double down on efforts to help our nation’s most vulnerable populations receive the care they need during the pandemic.

Joaquim Neto is chief product officer for Verato.