Machine intelligence accurately identifies patients at-risk of severe respiratory infection-associated hospitalizations

Machine intelligence accurately identifies patients at-risk of severe respiratory infection-associated hospitalizations

Press releases may be edited for formatting or style | August 12, 2020 Artificial Intelligence Emergency Medicine
SAN JOSE, Calif., Aug. 12, 2020 /PRNewswire/ -- Individuals deemed as high-risk to develop respiratory infections by predictive machine intelligence were 18 times more likely to be hospitalized and 13 times more likely to visit an emergency department, according to a new study from researchers affiliated with precision care delivery company Health at Scale Technologies, the Massachusetts Institute of Technology (MIT) and the University of Michigan.

The study, published in the American Journal of Managed Care, also found that Health at Scale's machine intelligence could be used to mitigate infection rates in the post-acute care setting. Specifically, at-risk individuals who received care at skilled nursing facilities (SNFs) that the technology predicted would be the best choice for them had a relative reduction of 37 percent for emergent care and 36 percent for inpatient hospitalizations due to respiratory infections compared to those who received care at non-recommended SNFs.

"For months, public health officials and providers have been working to 'flatten the curve' to preserve critical care capacity in our hospitals," said Mohammed Saeed, MD, PhD, Clinical Lecturer at University of Michigan's Department of Internal Medicine and Chief Medical Officer at Health at Scale. "Our research shows that precise and proactive outreach to higher risk sub-populations may be one of the most effective ways to prevent vulnerable patients from being hospitalized."

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The study authors evaluated the utility of machine intelligence in the management of severe respiratory infections in community and post-acute settings for a population of two million Medicare beneficiaries. Using Health at Scale's commercially available machine intelligence, researchers identified individuals in a community setting who would be at risk of infections that would result in emergent hospitalization, and matched individuals in a post-acute setting to SNFs likely to reduce the risk of infections.

The results demonstrate the potential of machine intelligence to improve outcomes and reduce the overall burden on the healthcare system by accurately identifying vulnerable patients and pairing them with the right care provider based on their specific characteristics.

"Unfortunately, most people don't have access to the information needed or the expertise to make an informed choice about which medical providers are most likely to provide them with optimal care," said John Guttag, PhD, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT and Chief Technology Officer at HEALTH[at]SCALE. "However, machine intelligence enables highly-personalized recommendations--based on a patient's medical history and the performance history of providers--that can lead to better outcomes, including reduced ED visits and hospital admissions."

To date, there have been more than 20.1 million coronavirus cases worldwide, with more than 5.1 million in the United States. While there remains limited data on the virus, the results of this study suggest that accurate care recommendations can be made from existing data about other viral and bacterial respiratory infections.

Health at Scale brings precision delivery to healthcare through advanced predictive machine intelligence for matching every patient to the right treatment by the right provider at the right time. The company works with leading health plans, provider systems and self-insured employers to optimize care decisions and improve the management of complex populations across complex care networks.

SOURCE Health at Scale Corporation

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