Care Continuity introduces an AI-driven solution to optimise specialty referrals and enhance patient navigation.
- This new approach uses AI to streamline the specialist referral process in healthcare.
- The system prioritises patients based on clinical needs, improving efficiency.
- It provides customised navigation workflows for patients based on predictive scores.
- The initiative aims to improve clinical outcomes and patient satisfaction.
Care Continuity has launched an innovative approach known as Specialty Referral Optimization. This system employs advanced AI and machine learning technologies to enhance the management of specialty referrals. It is tailored for health systems aiming to streamline the referral process, ensuring that patients are effectively prioritised based on their clinical needs and their likelihood to engage in the navigation process.
The advanced AI system allows hospitals to better manage their specialist capacities by prioritising patients more efficiently than the traditional methods. As Brad Prugh, CEO at Care Continuity, stated, “Managing specialist capacity and referral throughput is increasingly complex for health systems. Our 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.”
The solution is equipped with a unique machine learning model customised to the strategic goals of each health system. It considers various factors, such as system size and service line growth, to create a model that aligns with the institution’s objectives. This enables the health system to meet quality improvement opportunities while offering efficient patient navigation.
Upon the initiation of a specialist referral, Navigator Predict evaluates the referral details, including a patient’s clinical history and demographics, alongside the health system’s network objectives. It employs over 50 weighted navigation variables—honed from the navigation of over two million patients—to generate hundreds of machine learning models that determine the best outcome for both patients and the health system.
Each patient is assigned a Navigation Score, rated on a scale of 0.01 to 1.00. The score reflects the patient’s need for navigation and their propensity to accept help. This scoring enables a more tailored approach to patient prioritisation and workflow management, resulting in a process that is reported to be 3.5 times more effective than traditional methods.
The results from this advanced navigation process are analysed to identify and address network strengths and weaknesses, bottlenecks, and other areas for improvement, ultimately aiming to enhance the overall patient experience, clinical outcomes, and financial performance of the health system.
Care Continuity’s innovative use of AI in healthcare promises significant improvements in patient navigation and referral efficiency.
