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Artificial intelligence isn’t just a tool for pure tech — health care providers can use it too. Clinical practice and AI go together, three top health care leaders at national enterprises agreed during a panel at Transform 2021 hosted by VentureBeat general manager Shuchi Rana.

Using data to reduce medical waste and over-testing can help hospital systems save money, said Dr. Doug Melton, head of clinical and customer analytics at Evernorth, a subsidiary of insurance giant Cigna. “Before, we had unsupervised learning, and it was harder to do. You had to be prescriptive in your hypotheses,” Melton said.

AI has the potential to help clinicians improve patient outcomes, said Dr. Taha Kass-Hout, director and chief medical officer at Amazon Web Services. Medical records can be a great source of data to develop algorithms, speech recognition, and decision-making tools that could help doctors and nurses identify risk factors for serious illnesses such as congestive heart failure.

Early breast and lung cancer detection is another outcome that not only helps patients, but also benefits enterprise leaders. At Evernorth, Melton’s team used machine learning to analyze pre-certifications for radiology and past claims data, identifying who was at higher risk of developing more serious health issues down the line. ML improves prevention and holistic management, Melton said, and improves cost savings for both the patient and provider by as much as 3 times.


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Data analytics are also key to reducing other hospital costs, said Dr. Joe Colorafi, system VP of clinical data science and analytics at Commonspirit Health. By crunching the numbers, researchers can find which hospital stays last too long and when clinicians are over-assigned to a patient.

Collecting additional data from users can also help providers determine a holistic health care plan, Melton said. For instance, information on stressors in patients’ lives and other social determinants of health, such as access to fresh food and stable housing, can anchor plans to improve health outcomes. “When we do that, I think we can have whole-person medicine instead of acute care management,” Melton said.

Think of AI as a toolbox to understand the information presented to health care providers, Kass-Hout said. Using machine learning to narrow down symptoms and diagnoses also means building a repository of information to improve health systems. For instance, the accuracy of Amazon Web Services’ model to predict congestive heart failure increased by 4% as the algorithms took in notes about how physicians were treating the condition and monitoring patients for symptoms.

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