Vital Data Technology Announces Agreement with Mid-State Health Network to Provide Predictive Models for Mental Health and Substance Use Disorders

Written by Vital Data Technology | Dec 10, 2024 4:32:09 PM

NEWPORT BEACH, Calif., December 10, 2024 – Vital Data Technology, a leading provider of AI-driven health data analytics and medical management solutions for health plans, announced an agreement with Mid-State Health Network (MSHN) to provide predictive data models as part of a grant from the Michigan Health Endowment Fund Behavioral Health Initiative. This project specifically aims to improve the total quality of care and timeliness of that care by proactively identifying potential health risks using real-time data and predictive models within MSHN’s day-to-day clinical workflows.

By deploying Vital Data Technology’s predictive models for improved identification and risk stratification of at-risk populations, the project will help increase access to high-quality, integrated mental and physical health services for Michigan residents, while strengthening MSHN’s initiatives to promote health equity within the population that are currently underserved. These advancements in proactive, integrated care will also help bolster caregiver collaboration to achieve gains in service delivery, health outcomes and overall patient satisfaction.

“Vital Data Technology’s predictive models provide MSHN with the opportunity to focus outreach efforts in areas of highest need, to identify residents most at risk, and ensure appropriate services and supports prior to emergent need,” said Joseph Sedlock, CEO of MSHN. “We believe Vital Data Technology’s predictive models will help us reach more of the right people at the right time, thereby prioritizing high-impact interventions for beneficiaries.”

The predictive models will focus on identifying three main populations for proactive engagement: 1) people at highest risk for in-patient psychiatric hospitalization; 2) residents at highest risk for substance use disorder (SUD); and 3) people at highest risk of undiagnosed depression and anxiety.