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The coming revolution of AI analytics: Better outcomes, better scores, better reimbursement

The Coming Revolution Of AI Analytics: Better Outcomes, Better Scores, Better Reimbursement

As our Medicaid health plan clients tell us, it’s a numbers game. When it comes to reimbursement, states evaluate the performance of plans using the National Care Quality Alliance’s HEDIS and other endorsed measurements. But making measurable improvements to performance is a continuous struggle. One way to improve treatment measurements can be by targeting medication and treatment adherence. And in nearly every case, patient behavior changes can improve those measures.

AI is the key, but Medicaid programs are slow to change

The good news for Medicaid health plans is that they already have the building blocks necessary to improve their scores: mounds of data about their patients. The key to unlocking that data and making it useful is artificial intelligence. Deloitte reported in January 2018 that the same AI and cognitive technologies used in our personal lives can also improve Medicaid programs. “Given the size, scope, and cost of the Medicaid program, states and the federal government are constantly looking to improve health outcomes for Medicaid members and achieve greater program efficiencies,” Deloitte experts say. “Smart technologies could provide that possibility through the application of intelligent programs that can process vast amounts of data to understand patterns and make predictions about a member’s future health outcomes or health care utilization…”

So far, state Medicaid programs have been slow in adopting innovative technologies such AI because the types of technology platforms used by many of these programs are anything but innovative. Deloitte sums them up as “large, hard coded, and monolithic.”

But the good news is that CMS and the states, when they are replacing these old platforms, are requesting modular API and cloud solutions that are interoperable. And just like consumer sites that continuously collect data on individuals and use machine learning to learn their tastes and preferences to serve them up relevant ads and content, Medicaid systems can use machine learning to identify patients who are likely to be nonadherent, and even recommend the right intervention at the right time.

CMS wants innovation; brace for impact

If Medicaid plans overlook AI as a tool to optimize their scores, they will be doing so at their peril. CMS wants this innovation to happen. The CMS AI Health Outcomes Challenge was launched in March 2019. There are 25 participants who advanced to Stage 1 of the challenge in October 2019. Seven finalists will be announced in April and the winners will be announced in September.

CMS describes the challenge as “an opportunity for innovators to demonstrate how artificial intelligence tools, such as deep learning and neural networks, can be used to predict unplanned hospital and skilled nursing facility admissions and adverse events for potential use by the Innovation Center in testing innovative payment and service delivery models under the authority of section 1115A of the Social Security Act.”

For any Medicaid health plan, improving scores remains the ultimate yet constantly changing goal. Because some of these scores are a reflection of whether patient outcomes are genuinely improved by better adherence, finding ways to increase adherence rates has become imperative. By using AI and machine learning to improve adherence scores, Medicaid health plans can translate all their patient data into true patient care. 

About AllazoHealth

AllazoHealth is a healthcare AI solution that uplifts adherence by predicting individual patient behavior and specifying personalized interventions. Our mission is to be the leader in precision engagement and predictive analytics, revolutionizing the way our healthcare clients improve the health of their patient populations.

Find out more about how AI benefits patient adherence. Contact us today.