Health Equity, Labor Shortages, Care in the Home, and Value-Based Care: Jvion Shares Predictions for How AI Will Continue to Shape Healthcare in 2022

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Trends accelerated by the pandemic will continue into the new year, presenting new opportunities for healthcare organizations to leverage synthetic data and prescriptive analytics.

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2022 will be the year of bold action to address social determinants of health, and clinical AI will play a starring role in both understanding how these barriers drive disparities and directing the right resources to the right patients and populations.

Jvion, a leader in prescriptive intelligence, today released its predictions for how AI-enabled analytics will transform healthcare in 2022. The trends accelerated by the pandemic — labor shortages, the push for health equity, and the growth of home healthcare — will continue in tandem with the longer-term trend toward value-based care. In each case, prescriptive insights unlocked by the successful deployment and use of AI will enable healthcare organizations to deploy their resources more effectively to improve outcomes, lower costs, and raise the quality of care.

“It’s encouraging to see the healthcare industry’s new focus on achieving health equity,” said Dr. John Frownfelter, Chief Medical Officer at Jvion. “2022 will be the year of bold action to address social determinants of health, and clinical AI will play a starring role in both understanding how these barriers drive disparities and directing the right resources to the right patients and populations. It’s a worthy cause in its own right, but doing so will allow us to better achieve the goals of value-based care and Hospital at Home, two other major trends shaping healthcare.”

Data will Drive Action to Address Health Inequities
In the wake of the disparities exposed by the pandemic, health equity has become a top priority for both the Biden administration and the healthcare system it governs. Addressing inequities will depend on a complete accounting of social determinants of health (SDOH) and their impact on both individual patients and their communities. AI is well suited for this rule, capable of consolidating trillions of public data points on different risk factors nationwide and linking them to patient and community outcomes. In doing so, healthcare organizations can identify and leverage resources like food banks, mobile clinics, and health literacy programs that will most effectively reduce risk for patients and populations. That said, biased forms of AI can unintentionally drive health disparities, so expect greater attention and investment in AI that actively works to mitigate bias.

AI Fills the Gaps Left by the “Great Resignation”
The stress and trauma of the last two years have taken a toll on healthcare workers, as thousands leave the profession faster than they can be replaced. It will likely get worse before it gets better. In the meantime, clinical AI will help health systems manage their increasingly scarce human resources. Anything that can be automated will be automated, freeing up staff for the human interactions that matter most. Prescriptive analytics will help direct care teams’ limited time and attention to the patients with the greatest need, and recommend personalized interventions to ease their cognitive burden.

Value-Based Care Becomes the Norm
In October, the Centers for Medicare & Medicaid Services (CMS) announced that it expects all traditional Medicare beneficiaries to be treated in value-based care models by 2030. The trend is mirrored in the private sector, where consolidation is aligning incentives to improve patient outcomes and reduce the total cost of care. The necessary shift from episodic care to long-term care management will depend on preventive care and early interventions. Although predictive modeling can help identify patients at risk, it does not help in identifying which interventions to deploy. Prescriptive analytics, on the other hand, leverage data to identify patients’ biggest risk drivers and the interventions that will most effectively address them.

AI Enables the Continued Growth of Care in the Home
The Hospital at Home model of care has seen rapid growth during the pandemic as patients seek safer alternatives to hospitals and long-term care facilities. At the point of intake, AI can help determine which patients are eligible for home care and identify potential clinical, socioeconomic, behavioral, or environmental barriers to success, mitigating the risk that home care will drive disparities if these barriers are not addressed. Once patients are situated at home, AI will ingest the data coming in from remote patient monitoring devices, separating the signal from the noise to predict patient deterioration and recommend proactive interventions to course-correct and ultimately improve outcomes.

About Jvion
Jvion, a leader in AI-enabled prescriptive intelligence, enables providers, payers, and other healthcare entities to identify and prevent avoidable patient harm, utilization, and costs. An industry first, the Jvion CORE™ goes beyond predictive analytics and machine learning to identify patients on a trajectory to becoming high-risk. Jvion then determines the interventions that will more effectively reduce risk and enable clinical and operational action. The CORE accelerates time to value by leveraging established patient-level intelligence to drive engagement across healthcare organizations, populations, and individuals. To date, the Jvion CORE has been deployed across hundreds of clients and resulted in millions saved. For more information, visit

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Sara DeMoranville
Scratch Marketing + Media
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