During open enrollment, millions of American employees and retirees evaluate whether to stay with their current health plan or choose a new one for the coming year. Unfortunately, many consumers find this to be a confusing process, especially those that have exited the workforce and are now choosing from a wide range of Medicare Advantage plans rather than a select few employer-sponsored plans.
The degree of difficulty in assessing plans, as well as overall low satisfaction with plans, for those that are age 65 and older is reflected in the fact that 22 percent of currently enrolled Medicare Advantage members planned to make a switch during this open enrollment period–many after just one year with their current health plan. This is particularly problematic for the Medicare Advantage population, which tends to have high rates of chronic conditions and age-related health issues that require continuity of care.
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Meghan Gaffney Meghan Gaffney is Co-Founder and CEO of Veda, an artificial intelligence (AI) and machine learning platform that saves healthcare payers and providers up to 90 percent by automating healthcare administrative data processing and its associated administrative costs. Veda enhances data processing speeds and accuracy and is working to solve a $1 trillion problem […]
While it’s easy to attribute a lack of member loyalty to poor customer service experiences with certain health plans, that reason alone is too broad to be helpful in solving the issue and reducing member disenrollment. I submit that the actual root cause of movement is bad data. More specifically, bad provider data that makes it difficult for Medicare Advantage members to figure out which providers are in-network and which plans can help them reduce their out-of-pocket spend.
Top MA member concerns—surprise billing and provider availability—can be traced back to poor data quality
According to a Medicare Costs and Satisfaction Survey from February 2020, the top enrollee concerns when selecting a plan include surprise billing and provider network availability–both of which can be traced back to the quality of a plan’s data. Sixty-three percent (63%) of respondents said getting a surprise medical bill was their top concern when receiving medical care and 38% stated they worry their doctors are not in-network when they are selecting a plan.
These concerns are particularly top of mind for Medicare members, many of whom are living on fixed incomes, while also managing one or more chronic conditions. They live in fear of opening the mailbox and finding a medical bill that they weren’t expecting and can’t afford to pay.
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A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
So how do we fix these issues? The solution is actually baked into the No Surprises Act (NSA), a piece of federal legislation designed to address these very concerns. While most of the media attention the NSA has drawn has focused on provider billing, there are also requirements for health plans under this law. In fact, there is a provision of the NSA that requires plans to make updates to their provider directories within 48 hours of receiving new provider data. As plans work to be in compliance, they will make it easier for their members to get accurate information about which providers are in-network, reducing both surprise bills and out-of-pocket costs. In other words, as the data becomes more accurate, members’ top concerns are addressed.
The typical initial member interactions with plans reinforce their concerns and set the stage for a failed relationship
The very first interaction that most members have with their health insurer is trying to find a doctor in the plan’s directory and making an appointment to receive care. On average, however, the data powering these directories is such poor quality that consumers may go through up to 40 entries before they find a provider that is actually in-network, in their geography, and has availability for an appointment.
In addition, there are many barriers to consumers doing their own research to vet the accuracy of provider directory entries—ranging from lack of familiarity with new technology, to unreliable Internet access, to commitments that don’t allow them to make calls during business hours when provider offices are open, to being non-native English speakers.
These members naturally turn to their plans’ call center for help when they strike out online. However, the call centers rely on the same data as the online directories. That means that this second touchpoint with the plan often provides no further value and actually creates even more frustration for consumers. It’s easy to understand why 22% of those enrolled in a Medicare Advantage plan are frustrated enough to make a major change in their healthcare.
When we look at the demographics of who is switching plans most often, it’s groups who are considered “high needs” and therefore especially vulnerable. That signals that bad data isn’t just inconvenient and a trust eroder; it also perpetuates a negative cycle of health inequities.
What health plans can do to improve data and by association, member satisfaction
A study on the accuracy of Medicare Advantage directories found that between 45-52% of provider directory listings had errors, with some individual plans having error rates as high as 98%. This consistent level of errors, despite gating mechanisms such as those in the NSA, fuels member dissatisfaction and thus drives member disenrollment. So why do plans “allow” bad data to persist?
Plans receive provider data from myriad sources; data comes in quickly and frequently, and in different formats across the care continuum. Entering that information into a plan’s system is often a manual process that takes up extraordinary resources while perpetuating a high level of errors.
That being said, recent advances in data automation solutions mean that it doesn’t have to be so challenging to process this data—or to do so with greater levels of accuracy. New tech platforms that leverage automation both “clean up” provider data and speed its processing, ultimately leading to more initial touchpoints that feel satisfying to members (not to mention driving compliance with the NSA).
Bringing in new systems may sound overwhelming or disruptive, but it doesn’t need to be. In fact, there’s some low-hanging fruit that plans can take advantage of immediately. Starting by correcting a few pieces of core data, such as physician practice addresses, phone numbers, and specific specialties, can have a huge impact on accuracy and keep more members happy.
Parting thoughts
As this Open Enrollment season closes, plans that witness high levels of churn—particularly Medicare Advantage plans—shouldn’t panic. There are other points during the year when these members are making healthcare choices, so plans will have other opportunities in 2023 to increase member loyalty. The key is to understand that bad data is the primary driver of disenrollment, and to create a plan to improve the quality of data.
Meghan Gaffney is Co-Founder and CEO of Veda, an artificial intelligence (AI) and machine learning platform that saves healthcare payers and providers up to 90 percent by automating healthcare administrative data processing and its associated administrative costs. Veda enhances data processing speeds and accuracy and is working to solve a $1 trillion problem within the healthcare industry. Meghan has over 15 years of experience working with elected officials and impact organizations, as well as consulting on technology opportunities. She is a passionate advocate for artificial intelligence and machine learning and believes these technologies will create unprecedented economic opportunities for the United States and the world.
