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Across measures of health care innovation, the United States is consistently ranked as a global leader. It has more Nobel laureates, more clinical trials, more patents, a robust research university system, and some of the highest per capita expenditures on research and development. As demonstrated by the COVID-19 pandemic, these investments can pay off and enable the creation of innovative new therapies for previously untreated diseases. However, despite research highlighting the direct benefits innovation has on health outcomes, the United States regularly lags behind other high-income countries on outcomes such as life expectancy, maternal mortality, heart disease, and health care access and quality. Additionally, the system is fraught with racial and ethnic health disparities. As our research and policy communities continue to develop and implement new innovations and policies to improve population health, how can we ensure that these are developed and implemented in ways that ensure they reduce, and don’t exacerbate, existing disparities?

Fundamental to any innovation is the data that’s used. At AcademyHealth’s 2022 Health Datapalooza and National Health Policy Conference, data and policy experts across the health services research field came together to explore critical questions related to the intersection of health data and policy innovation. In the rapid-fire session “Accelerating Technology to Navigate the Balance between Privacy, Interoperability, and Disease Tracking,” panelists discussed how they are leveraging equity-focused and patient centered approaches to improve overall health outcomes.

Leveraging Data Algorithms to Understand Populations and Disease Burden

“Health equity is not complete without representative data justice,” emphasized panelist Guleer Shahab.

Data is at the core of research and development. As Shahab underscored, race and ethnicity are often misclassified in health data resulting in inequitable policies and poor understandings of disease burdens among certain communities. Although there has been extensive research and advocacy efforts aimed at improving data quality documenting Black, LatinX, Asian American and Pacific Islander (AAPI), and Native American communities, there are still large data gaps and policy and investments needed to improve data collection and infrastructure. Shahab is currently researching race and ethnicity misclassifications among the Southwest Asian and North African (SWANA) community. The SWANA community is a very diverse group that makes up approximately 2.5 percent of the United States population and has been growing in recent years. However, more often than not, SWANA patients are mis-coded as white in health data. As a result, the disease burden among the SWANA community is often misunderstood or ignored. To understand and quantify rates of common cancers in the SWANA community, Shahab developed a surname algorithm that matches patient records to a list of common surnames. She found that 4.4 percent of all cancer patients included in her analysis were identified as SWANA, with two thirds of them having been previously miscategorized as white.

Another panelist, Dr. Amanda Petrik, is leveraging patient data to look at common characteristics among sub-groups. Petrik and her colleagues used electronic health records to identify and compare adolescents who received vaccination for COVID-19 and HPV across a variety of demographic characteristics. Their findings indicated that younger males and individuals living in rural areas were more vaccine hesitant in both cases and suggested more targeted outreach to those groups to improve vaccination rates.  

Employing algorithms such as these can help researchers and policymakers better understand and respond to specific community needs. However, these research methods must be paired with community partnerships and equity-centered design to address the real needs of the communities they seek to help.

Considering the Patient Throughout All Parts of the Design Process

As more and more innovators look to health data to inform their research and development, the sector has seen increasing attention paid to health data sharing and interoperability, patient access and consent, and privacy regulations. In response to these trends, panelist Gautam “G” Shah presented a series of best practices for sharing health data.

“As we make this data available” Shah explained that “we have to make sure that this data is used in the right way and that we, as patients, have control over where that data goes.”

Similarly, panelist Vanessa Vogel-Farley emphasized the importance of supporting all patients to become research participants. Research needs to include patient perspectives and be conducted in a way that is useful and beneficial for patient participants. As a result, Vogel-Farley explained that patients should be active partners in the data ownership and sharing process.  

Considering the patient perspective should expand beyond gaining consent for data sharing and use. All panelists agreed that communities contributing the data or using the ultimate technology should be considered throughout the design process. New technologies and innovations should be accessible across demographics and include considerations such as broadband internet access, primary language, and data literacy of a community. This means not only partnering with patients but with the local public health workforce as well. Implementing these partnerships would reduce siloes between the research and development community and the populations their innovations serve.

While the live schedule of activities has ended, Health Datapalooza and National Health Policy Conference registrants have access to the recordings of 13 breakout sessions and all plenary sessions through May 6, 2022. Registration for the 2022 Annual Research Meeting taking place this June is currently open. Learn more here.

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Rachel Campbell-Baier

Research Associate - Mathematica

Rachel is currently a Health Research Associate based out of Mathematica’s DC office. Prior to starting at Mat... Read Bio

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