Care Continuity, a leading provider of patient navigation solutions, announced the launch of Specialty Referral Optimization, a new, AI-driven approach to specialty referral management and patient navigation designed to streamline and improve the specialist referral process.
Powered by advanced AI and machine learning, Care Continuity’s Specialty Referral Optimization product allows hospitals and health systems to better prioritize the patients for navigation based on their clinical needs as well as their likelihood to engage and accept navigational assistance, leading to more effective and efficient patient navigation teams.
“Managing specialist capacity and referral throughput is increasingly complex for health systems. Prioritization within EMR’s is limited, leading to a first in-first out approach in most situations,” said Brad Prugh, CEO at Care Continuity. “Our Specialty Referral Optimization solution leverages AI and machine learning to not only improve service line throughput for health systems, but also enhance patient satisfaction and improve clinical outcomes.”
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Care Continuity’s unique approach to specialty referral management and patient navigation lies in the customization of each machine learning model that considers the strategic goals of the health system, taking in factors such as system size, service line growth goals, and quality improvement opportunities.
“Specialist capacity is becoming a bigger and bigger issue for health systems. It’s leading to poor patient experiences and missed opportunities to improve financial performance,” said Prugh. Our Specialist Referral Optimization product gives health systems a quick-to-implement solution that not only makes patient navigation and specialty referral management more efficient, but also serves as a network intelligence and optimization tool that can bolster the strengths of any health system.”
How Specialty Referral Optimization by Care Continuity Works
Care Continuity‘s Specialty Referral Optimization solution leverages machine learning to prioritize patients for navigation based on their clinical needs and characteristics as well as the health system’s goals and network dynamics.
When a specialist referral is initiated, Navigator Predict ingests information about the referral, including the clinical history and demographics, as well as details of the health system’s network dynamics and objectives (e.g., social determinants of health, readmission risks, service line capacity, etc.).
The referral details are combined with more than 50 weighted navigation variables that have been collected by Care Continuity after navigating more than 2 million patients for the nation’s leading health systems over the past 10 years.
These datapoints result in hundreds of machine learning models that are run against your patient cohort to find the optimal navigational outcome for the patients and the health system.
Navigator Predict assigns a Navigation Score to each patient receiving a referral, rating them on a scale of 0.01 to 1.00. The closer the score gets to 1.00 the more in need the patient is for navigation and the more likely the patient is to accept the navigational assistance.
While all patients who enter the Navigator Predict pool receive assistance, the prediction score allows for better prioritization and navigation workflows that are customized for each individual patient. This approach to specialty referral management is 3.5x more effective than traditional efforts.
Finally, these navigation results are analyzed to highlight network strength, bottlenecks, leakage, and other key factors that can be used to improve the overall patient experience, quality outcomes, and profit margins.
SOURCE: PRNewswire
