MCO Analytics: Practical Use Cases for States

The key to good oversight is good information. So, a state’s ability to monitor the performance of its Managed Care Organizations (MCOs) relies on having strong analytics in place. Nobody questions that. But the questions I do see are about the types of things a state can—and should—measure. What are some sample use cases?

If a state invests in a configurable MCO analytics portfolio, it can get quite creative with the variety of data it is able to leverage. Here are some examples of ways you can turn interesting data into actionable insights your program can use.  

Side-by-Side MCO Comparisons

It’s true that MCO analytics can reveal which health plans are not performing as well as they should be. But, more importantly, analytics can also uncover which health plans are performing very well. This allows states to implement best practices across their network of plans, raising the level of care for their entire Medicaid population. Here are just a few targeted areas that states could compare across plans.

• Promoting treatment adherence. States are trying various measures to control pharmacy costs. But the truth is, many medications that are extremely expensive are also extremely effective—if the patient completes the full course of medication. If a patient takes only half of the course, then that money is wasted—and worse, the patient is not cured. Using targeted outreach, providers can encourage patients to finish their medication, and states can use analytics to identify the MCOs that do that well. 
   
• Controlling use of opioids. One government report has estimated that there are nearly two million nonelderly adults in the United States with an opioid use disorder (OUD), and nearly four in 10 are covered by Medicaid. With statistics like these, states need clear visibility into how their managed care networks are treating opioid patients. Which plans are appropriately controlling and monitoring opioid prescriptions? Which plans are getting people into treatment—and keeping them there—to achieve successful outcomes?
   
• Measuring population health and case management effectiveness. Without strong analytics, it can be hard to quantify the effectiveness of various case management programs. Analytics can help states home in on health outcomes to determine which MCOs are effectively managing population health. For example, states can compare the outcomes and cost data for one plan’s population of diabetics to that of the same population in other plans.  
   
• Responding to rate increase requests. When MCOs ask for rate increases, they might argue that their population is sicker than the populations other plans serve. But is this true? How can states be sure they’re making fair comparisons across MCOs that have different diagnosis mixes? By using analytics on a risk-adjusted basis, states can compare the patient mix between MCOs and determine whether Plan A actually has a higher intensity of illness mix than Plan B. States can then determine when an MCO might truly need a rate adjustment to improve patient care in the state.

True Access to Care

Having convenient access to care has long been a problem for Medicaid beneficiaries. One way states have historically tracked this is by using maps to determine how many providers an MCO has and where these providers are located. This traditional geo access reporting can provide a high-level look, but analytics can help states dive much deeper to identify and address specific barriers to care. Some examples include:

• Monitoring non-emergent ER visits. Does a certain plan have a large number of low-level ER visits? If so, this could indicate that members do not have good access to primary care providers (PCPs). Perhaps there are not enough PCPs, they’re located in difficult-to-access areas or there is simply not enough appointment availability.
   
• Reporting on provider caseloads and concentration within networks. If a plan is required to have, for example, 50 orthopedic specialists, but members are making appointments with only five of them, access is clearly an issue. Although the other 45 specialists are technically in the plan, they  may not be located where members can easily access their offices, or perhaps they’re not opening appointments to Medicaid patients.
   
• Identifying members receiving inadequate care for chronic conditions. If members don’t have good access to care, their chronic conditions can worsen. Analytics can help proactively identify problems before outcomes turn tragic. For example, analytics might reveal a large population of diabetic patients who haven’t had a primary care doctor visit, laboratory monitoring or a prescription filled within the last year. Uncovering this information enables timely intervention.

Single View of the Member

As members move among health plans, they can sometimes get lost in the shuffle—even if they’ve been in the Medicaid system for years. When their history moves with them, however, high-risk members can be moved quickly into care management with their new health plan, promoting continuity of care. Analytics makes this possible.

Risk analytics can also identify hidden and rising risks in populations based on their entire history and not just their recent experience with one payer. Having this information allows plans to intervene, preventing members from progressing down the road to more health problems and worse outcomes.

Increased Transparency and Accountability

Each state manages its Medicaid program differently, so there is no one-size-fits-all solution to MCO oversight. But with a flexible MCO analytics portfolio, states can find innovative new ways to support program integrity, improve health outcomes and ensure that taxpayer dollars are spent appropriately.