The predictive analytics partnership will help identify high-risk patients for targeted care.
California’s largest nonprofit health data network, Manifest MedEx which exchanges data for over 10 million patients, has announced its selection of HBI’s Spotlight Analytics Solution to bring real-time predictive analytics to its participant organizations.
The insights will assist them in identifying high-risk patients for targeted care. The company was chosen based on their track record for providing analytics and predictive risk services to the healthcare industry.
“Through the use of advanced analytics, our goal is to provide our participating organizations with the ability to create and manage high-value networks and enable them to do the hard work to reduce costs, provide a better patient experience, and improve outcomes,” said Erica Galvez, Chief Strategy Officer of Manifest MedEx. “Our mission is ultimately to get care teams the health information and insights they need to help their patients live longer, healthier lives. Our work with HBI is an example of how breaking down information silos and working collaboratively across the ecosystem creates real value for care providers and their patients.”
Over 200 healthcare organizations in California are connected to MX. That includes 60 hospitals. LabCorp, Quest, Radnet, and four health plans. All generate data for the MX network 24/7. HBI uses machine learning and community social determinant data to create real-time algorithms to generate predictive insights on cost, admissions/readmissions, mortality and ED visits. The new partnership will also offer participants network management and quality/performance measures through HBI Spotlight Analytics.
HBI’s robust applications and risk models process real-time predictions on the large breadth and scope of patient data for MX participants. The new service provides value to MX participants by supporting care management in their efforts to improve patient outcomes and reduce costs through early identification of patients at risk for serious health events.