ZeOmega Rolls out Jiva Opioid, an Intelligent Solution for Finding and Managing at-Risk Opioid Population
FREMONT, CA: The opioid is a national crisis affecting millions of people and driving billions in avoidable cost each year. ZeOmega, a pioneering provider of the technology enables population health solution, has come up with its Jiva Opioid AI to tackle with the opioid crisis and help identify and manage opioid abuse populations. When integrates with social determinants of health and more traditional claims data, Jiva Opioid AI can find at-risk individuals based on factors other than prescribed medications.
The new solution is developed using an evidence-based, machine learning algorithm, and it identifies the full spectrum of opioid abuse cases and seamlessly guides patients into the most appropriate care management workflows. Not only Jiva Opioid AI integrates social determinants of health data (SDoH) with claims and other data, and its machine learning nature means it is continually retaining to identify new overdose patterns. The platform integrates with Jiva Care Management workflows, creating a complete solution for managing opioid populations.
ZeOmega enhances health plans and other risk-bearing organizations with the industry's leading technology for simplifying population health management. Founded in 2001, the company supports more than 30 million lives with utilization management, case management, disease management, and analytical capabilities across the Jiva platform. Its mission is to deliver proven population health management software solutions that enable its clients to enhance the value of healthcare and bend the cost curve.
The world population health management solutions market is witnessing significant growth and is expected to expand further in future years. The PHM market is driven by an increase in the usage of health care IT solutions and technologies and the rise of chronic diseases across the globe. PHM tools provide real-time insights to administrators and clinicians, enabling them to identify the care management gap within risk patient population.