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Jul 28, 2021
Cigna Uses Data and AI to Improve Patient Outcomes

There’s no denying that data is one of the most important assets that companies have today. The New York Times has gone as far as saying that “data is the new oil.” It helps businesses better understand their customers, employees, and provides an opportunity to fine-tune business models, marketing campaigns, processes and more. 

But the use of data in health care takes things quite a few steps further. Health care is one of the most data-rich industries, and at Cigna we have found that although our work in the data and analytics space cannot change a diagnosis, it can help change patient outcomes. 

This thinking is at the heart of our work in data and analytics at Cigna and Evernorth, a wholly-owned subsidiary of Cigna Corporation. Our experts recently spoke about some of the important work we are doing with AI and data at VentureBeat’s Transform 2021 conference. Read on for the top takeaways from their conversations and learn about how Cigna and Evernorth use data and AI to improve patient outcomes. 

Whole-Person Health: Using Data to Understand a Complete Health Journey 

Dr. Doug Melton, head of clinical analytics at Evernorth, discussed industry opportunities to expand our current use of data and better focus on whole-person health. 

“If you want to understand a person’s health care outcome and their journey, you have to understand the person outside of traditional health care,” said Melton. “There are some clinical components like genetics which are helpful, but being able to understand who’s stressed out caring for a child at home with autism, who’s recently gone through a divorce, who was recently laid off, who doesn’t live within 20 minutes of a primary care provider – that type of information can be just as powerful as, say, a radiology or pathology scan.” 

According to Melton, as the industry moves towards more medical data transparency, we also must move towards a focus on identifying the social determinants of health, elements of historical stress and anxiety, and elements of home and infrastructure. “We can use this kind of data to shift the focus to whole-person medicine instead of acute care management,” he said. 

Predictive Analytics: Helping Customers Navigate Chronic Conditions 

Gina Papush, global chief data & analytics officer at Evernorth, spoke about ways Cigna uses AI to predict a chronic diagnosis before it occurs and helps customers navigate their options post-diagnosis. 

“Cancer, heart disease, diabetes – chronic conditions like these contribute to the majority of health care episodes and costs in the United States,” said Papush. To better support customers, Evernorth is using machine learning to predict a chronic diagnosis before it occurs – and providing patients and providers with intelligence to inform their decisions and lower costs.” 

According to Papush, one powerful case study is breast cancer. Dealing with a breast cancer diagnosis is often the worst moment of a woman’s life, and one out of eight women will experience it in their lifetime. Historically, health services organizations often find out about the diagnosis after the course of treatment has been decided. 

At Cigna, we are bending the historical trend and using AI to connect with patients and providers earlier on in the process to take the burden off the customer’s shoulders and help navigate coverage and more affordable treatment options. “When we do this outreach driven by new machine learning models, we found nearly 60% more patients take advantage of recommended clinical programs and specialized advice,” Papush told conference viewers. 

Post-COVID: Identifying Risks and Alleviating Health Disparities 

Papush also addressed how AI has enabled and transformed our company’s understanding of long-term COVID-19 risks and health disparities. 

“As the pandemic progresses, we’ve shifted to ongoing work on predictive modeling to understand and identify risks around COVID-19 – building and applying models to understand who may be at long-term risk post-COVID,” said Papush. “Our analysis has identified higher rates of heart disease, mental health issues, and neurological issues among customers who had COVID or were hospitalized. The data tells us that those who go to see their provider – and those who are enrolled in our care management program post-hospitalization – have faster rates of recovery, return to work faster, and have lower returns to the emergency room.” 

Importantly, our data showed that Black and Hispanic customers were disproportionally affected by COVID-19, diagnosed and hospitalized at substantially higher rates. They were also less likely to seek care if diagnosed – putting them at even higher risk. We used these data insights to focus outreach and develop community and individual engagement plans, in partnership with our clinical and customer experience teams. 

Continuous Improvement in AI: Automation and Data-Rich Insights 

Mark Clare, head of data strategy & enablement at Evernorth, spoke about Cigna and Evernorth’s continuous journey to leverage new data and find actionable insights. He said that the biggest change he has experienced in his career is that “our data is no longer confined to internal records. In fact, the health ecosystem is one of the most data-rich out there. “It’s a vast external network that we want to aggregate and gain insights from. We’re never done enriching our data and driving action off of those insights.” 

Clare also believes that AI has made, and will continue to make, a tremendous impact with automation. Companies today can use it to automate mundane tasks, freeing people to work on higher impact projects. Case in point: Clare has spent his entire career fixing data quality issues, which has primarily been a manual process (Think: Combing through Excel spreadsheets). “The exciting thing today, primarily with the use of AI, is that we can start making those manual processes more automated, scalable and intelligent.”