This model stratifies members into various groups, then predicts the number of times members could utilize Emergency Room (ER) services for potentially avoidable reasons – with “avoidable trips” defined as things that are not actually an emergency. It enables proactive outreach to members for education and intervention programs to curb overutilization before an unnecessary ER visit occurs.
According to research by UnitedHealth Group, avoidable visits to hospital emergency departments (ED) contribute significantly to rising healthcare costs in the United States. UnitedHealth Group found that as many as two-thirds of the 27 million ED visits by patients with private insurance are avoidable. If all of those patients went to primary or urgent care facilities instead, it could save our healthcare system $32 billion a year.
The predictive ED visits model is just one part of several new machine learning capabilities within Cedar Gate’s platform. Others include the Cholesterol Screening Compliance Prediction model that forecasts the likelihood a member will receive a screening within the next 12 months. Additionally, the Prospective Allowed Amount Prediction model forecasts the potential medical and pharmacy allowed amount as well as the prospective risk score for members in the upcoming year.
“Interpretable AI allows providers to access medical data immediately, review medical history, identify patterns, and recommend interventions while keeping a focus on the patient’s well-being and quality of care,” said Stephen Zander, Cedar Gate’s Chief Analytics Officer.
Cedar Gate’s advancements in predictive analytics and machine learning algorithms allow healthcare organizations to analyze vast amounts of complex data enable healthcare organizations and synthesize key insights into actionable recommendations.
“While interpretable AI has many capabilities, it does not replace human expertise, as feedback from specialists and physicians is essential to building a connection with the patient,” said Zander. AI should serve as an extension of the care team, according to Zander, augmenting care providers’ expertise so they can be more precise in care delivery and maximize available resources.
“AI-enabled solutions are transforming the way health care is delivered,” said Zander. “Interpretable AI is poised to evolve and play a growing and essential role in supporting holistic clinical and health care operations. While delivering proven benefits today, AI’s potential to shape the future holds boundless potential in the form of a health care system focused on improved efficiencies, lower costs, and a structure that educates, engages, and empowers patients.”
