In the U.S. for-profit, insurance-driven healthcare system a conundrum arises, which presents a complex challenge, with health plans enroll beneficiaries who are covered by both Medicare and Medicaid. These people are known as dually eligible beneficiaries.
The proportion of people who are Medicare beneficiaries and who are also enrolled in Medicaid varies at any given time, but it runs into several million people. Medicare is a national health insurance program, administered by the Centers for Medicaid and Medicare Services of the U.S. federal government. It provides health insurance for Americans aged 65 and older who have worked and paid into the system through the payroll tax.
Medicaid is a joint federal and state program that helps with medical costs for some people with limited income and resources. A common concern with the dual eligible beneficiary system is often one of confusion, and a resolution requires greater transparency such as dual-specific information in Medicare and State consumer information sources.
The new development provides a solution to this challenge and it follows on from projects looking at the potential for artificial intelligence in healthcare. A major concern with previous approaches focusing on artificial intelligence solutions for healthcare has been with a lack of person-to-person interaction, which is something that can put some consumers of healthcare off.
Change Healthcare, the healthcare technology company, has come up with a solution designed to address the dually eligible beneficiaries issue and to improve upon the weaknesses with previous iterations of artificial intelligence.
The new platform, called the Dual Enrollment Advocate, has been designed a solution to solve such tricky and time-consuming challenges in U.S. healthcare. The main aim is with how to accurately identify and pair the right people with the medical benefits they are entitled to.
To arrive at the right answers, the platform uses artificial intelligence to process data and machine learning to interpret the data. The data input relates to insurance scheme members, and consists of some twenty inputs. The outcome is that the Change Healthcare Dual Enrollment Advocate, through its use of uses artificial intelligence, can to pinpoint, with up to 93 percent accuracy, those individuals with the highest likelihood to qualify for full or partial dual eligibility. This means that Medicare Advantage plans should be able to more quickly engage with this population and potentially enroll more of them faster.
According to Keith Roberts, vice president, Engagement, Change Healthcare: “We’re applying data science, behavioral science, and consumer-experience design to solve one of the trickiest problems for Medicare Advantage plans, which is how to accurately identify people for Medicaid eligibility.”
He adds: “AI and machine learning can’t do it alone. Behavioral science can’t do it alone. And health plans and legacy systems can’t do it alone. The time has come to bring these healthcare IT and scientific disciplines together to help solve a critical business challenge for our customers.”
