Care Continuity, a leading provider of patient navigation solutions, has announced the launch of Specialty Referral Optimization, an innovative AI-driven approach to specialty referral management and patient navigation. This new product is designed to streamline and enhance the specialist referral process, ultimately improving patient experiences and clinical outcomes.
1. Key Features of Specialty Referral Optimization
- AI and Machine Learning Integration:
- Advanced AI algorithms prioritize patients for navigation based on clinical needs and their likelihood to engage with navigational assistance.
- Results in more effective and efficient patient navigation teams.
- Customized Machine Learning Models:
- Each model is tailored to the strategic goals of the health system, incorporating factors such as system size, service line growth goals, and quality improvement opportunities.
2. Expert Insights
Brad Prugh, CEO at Care Continuity, commented on the complexities health systems face: “Managing specialist capacity and referral throughput is increasingly complex. Prioritization within EMRs is limited, leading to a first-in, first-out approach in most situations. Our Specialty Referral Optimization solution improves service line throughput and enhances patient satisfaction while improving clinical outcomes.”
3. Addressing Capacity Challenges
- Specialist Capacity Issues:
- Increased complexity in managing specialist capacity leads to poor patient experiences and missed financial opportunities.
- Specialty Referral Optimization provides a quick-to-implement solution that enhances efficiency in patient navigation and referral management.
4. How Specialty Referral Optimization Works
- Patient Prioritization:
- The Navigator Predict system ingests referral information, including clinical history, demographics, and health system objectives (e.g., social determinants of health, readmission risks, service line capacity).
- Weighted Navigation Variables:
- Utilizes over 50 weighted navigation variables, gathered from navigating more than 2 million patients for leading health systems over the past decade.
- Machine Learning Models:
- Hundreds of machine learning models run against the patient cohort to find optimal navigational outcomes for both patients and health systems.
- Navigation Score Assignment:
- Each patient receives a Navigation Score on a scale of 0.01 to 1.00.
- Scores closer to 1.00 indicate a higher need for navigation and a greater likelihood of accepting assistance.
- While all patients receive assistance, the score enables better prioritization and tailored navigation workflows, making this approach 3.5 times more effective than traditional methods.
Care Continuity’s Specialty Referral Optimization represents a transformative advancement in specialty referral management and patient navigation. By leveraging AI and machine learning, this solution not only enhances operational efficiency for health systems but also ensures that patients receive the personalized care they need.
