Care Continuity's Specialty Referral Optimization solution utilizes machine learning to improve referral prioritization and workflow by incorporating the health system's quality/financial objectives and our proprietary patient engagement predictions into the prioritization algorithms. This allows health systems to optimize their network by priortizing specialist referrals according to their organizational goals and objectives.
With our ML-enabled solution, patients are prioritized based on:
- Financial Improvement - driven by increased procedure capture and service line throughput. The right patients are prioritized to manage capacity and ensure that appointment slots are used for the highest value patients.
- Quality Improvement - driven by prioritizing appointment slots for patients at highest risk of a future acute event, helping reduce inappropriate ED utilization, IP readmissions, and performance in value-based care arrangements.
- Patient Engagement - driven by increasing patient referral completion rates. The right patients are prioritized based on their likelihood to accept navigational assistance and complete (attend) their appointments.
In addition to referral prioritzation, we leverage machine-learning to build the most effective, patient-centered workflow for navigators to follow when engaging with patients. Our software is supported by a team of trained patient navigators that do the navigating on your behalf or complement your inhouse team of navigators if needed.
Finally, we provide curated data dashboards provide key insights into the strength of your provider network giving actionable insights into appointment logjams, leakage/outmigration points, and opportunities for improvement.
