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Lessons from COVID-19: Using Data to Treat Chronic Disease at Scale

Blog-COVID19 Balancing-Blog

Access to accurate, timely, comprehensive healthcare data has never been more important. The COVID-19 pandemic has shown us that when it comes to our healthcare systems, we simply cannot afford to be reactive. Rather, we need to build strong, proactive structures and public policy that use all of the information at hand to make educated decisions about the future. 

In light of this, the pandemic has put a sharp focus on the value of capturing and understanding patient data at scale in order to understand how disease is spreading, which treatments are working and for whom, and how to reach at-risk populations with key public health messaging. 

As we move towards the light at the end of the tunnel with respect to COVID, we must look ahead to tackling the many other issues facing our healthcare system, particularly chronic disease. Chronic disease has been a public health crisis itself for years: In 2018, at least 45% of Americans had one or more chronic disease, and chronic illnesses accounted for 75% of the $2.2 trillion we spend on healthcare each year and 7 out of 10 deaths in the U.S. 

And so often, by the time a patient reaches the clinician they need, the disease has already advanced to a stage where the only actionable strategies are mitigation and disease management – not a cure. With the right use of data, however, we can develop proactive, preventive approaches to chronic diseases to both improve individual lives and also lighten the heavy burden these diseases place on our country’s healthcare systems. 

New Gaps in Healthcare Created by COVID-19 

This proactive approach will be especially critical as we recognize that the pandemic has created a “gap year” in screenings for many chronic diseases, a challenge whose effects will ripple out for years to come. 

Last spring, A1C tests – a proven method of diagnosing and monitoring diabetes – were down by 61%. Lipid panel tests, used to spot cardiovascular risk, were down 65%. Mammogram volumes were down 96%, colonoscopy volumes down 91%, and cervical cancer screenings down 72%. These preventive screenings and associated care patterns continued to lag behind 2019 volumes throughout the summer of 2020, and have not made up for the ground lost during the height of the pandemic.

These diseases aren’t going anywhere; they are simply going undiagnosed. This means they are either going to be detected and treated later at a more advanced stage, or go unmanaged altogether. For instance, without frequent lipid panel testing, patients with cardiovascular conditions may not be aware that their medication dosage is no longer optimal for their lipid levels – and may be more at risk for major adverse cardiac events. 

So how will our health systems cope as latent diseases emerge? 

A strong, proactive approach requires better use of data to more fully connect the dots between patients, preventative care, treatment options, and outcomes. We need to closely track diagnosis trends as many Americans return to a cadence of health care visits over the coming year, and also use the data we already have to make predictions, shape public policy, and gird our healthcare systems for what is ahead. 

Existing Inequities in Disease Burden Exposed

The pandemic has also revealed longstanding inequities in disease burden, as Black Americans, Hispanic Americans, and American Indians faced disproportionately higher rates of COVID compared to white Americans. 

This has long been the case for some of the most challenging chronic illnesses. In 2016, according to the peer-reviewed journal Diabetes, the risk of having a diabetes diagnosis was 77% higher among African Americans, 66% higher among Latinos/Hispanics, and 18% higher among Asian Americans when compared with white adults.

And now, many of these same groups are also facing higher unemployment rates as a result of the pandemic – meaning many of the same populations who are more likely to face diabetes now are also disproportionately facing losses of income and health insurance. 

Here, too, data is key in identifying trends that lead to diagnosis, as well as what treatment and management approaches are successful for different populations. 

Mapping Data to Find and Address Trends in Chronic Disease 

By tracking full patient journeys, including demographic info, we can better understand what exactly puts a patient at risk for a certain disease or at risk of receiving inadequate care and better target resources where they are most needed to close gaps in care. 

Looking at diabetes again, a strong dataset can show us the populations and even the neighborhoods most at risk. In the short term, healthcare organizations and life sciences companies can use these insights to more accurately target resources, increase screenings for populations most affected to ensure early detection, and make treatment and mitigation tools like insulin and A1C test strips affordable and accessible. 

In the long term, a data-driven approach – effectively mapping how patterns in income, education, housing, and access to nutrition all impact the risk an individual will develop diabetes – will allow us to take the public policy steps necessary to eliminate those inequitable environmental factors. 

As it stands, our system is far too reactive – despite the fact that we know how to more effectively prevent and manage illnesses to achieve better outcomes. Only by starting with reliable data can we understand how all of a person's or population’s risk factors intersect to contribute to their outcomes and make the systemic changes necessary to manage, or prevent, these illnesses on a larger scale. With the right data system, we can efficiently answer who is most at risk for chronic diseases, who is slipping through the cracks, what treatments are working, what mitigation efforts are effective, how to get the right information to the right patients, and the other essential questions to effectively reduce the heavy burden of chronic disease.

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