Blog, Healthcare Analytics, Innovation

Can Data & Analytics Improve Management of High-Cost Claimants?

The Rise of High-Cost Claimants
The volume of patients with multiple chronic conditions like diabetes, obesity, depression, mental illness and complex diseases continues to rise.  As a result, high-cost claimants (i.e. costing $50K or greater per year) are growing at an alarming rate, outpacing all other drivers of health care costs [1]. A recent study by Mercer analyzed claims data for 1.6 million health plan members representing $8.5 billion in aggregate costs, and they concluded that 6% of members with high-to-catastrophic illness burden generated nearly half of all claims with an average cost 7x greater than that of members with medium to high illness burden [2].

There are several factors contributing to the rise in high-cost claimants that may drive the need for us to analyze and manage these populations differently:

• America’s aging population – Americans aged 65 and older are already a population of 48 million people and they are expected to double by 2060 [3]. A longer life expectancy significantly increases the likelihood of members experiencing conditions that are costly to treat.

• Rapid growth of specialty therapeutics – Pharmaceutical and biotech companies continue to pioneer research to improve treatment of chronic illnesses. While these therapies have the potential to transform the lives of members living with chronic / complex diseases, their annual costs often exceed the $50K high cost claimant threshold.

• The Affordable Care Act (ACA) – The passage of the ACA created opportunities for millions of previously uninsured patients to obtain health insurance and significantly changed the member demographics of many health plans. Many of these members suffered from chronic conditions that were not well controlled because of poor access to care or inability to pay for healthcare services previously. As a result, many of these members entered health plans at a heightened risk of becoming high-cost claimants.

Learning from Mistakes We Make Today to Adopt a Better Approach for Tomorrow
So how do we transform our approach to addressing the challenge of high-cost claimants?
Historically, many payers have employed a reactive approach to managing high-cost claimants. They select a handful of “preventable” chronic illnesses that are the greatest cost drivers and implement strategies such as intense care management, shifting of costs to members, or in some cases “lasering” them out of coverage altogether. But there is very little evidence that shows these strategies to be effective.

In the future, organizations need to employ a more proactive and collaborative approach to identifying populations of high-cost claimants that can be effectively managed and the appropriate solutions to implement. This is where data and analytics have the potential to transform how we approach managing high-cost claimants. By investing in data and analytics, payers, providers and employers can generate insights to better identify the members where they can have the greatest impact and tailor strategies to the unique characteristics of these populations. For example:

• Advocate Health Care developed an employee wellness program using a patient-centered approach and brought it to life with data and analytics. Advocate determined that employees managing multiple chronic conditions represented 15% of their population but accounted for more than 80% of costs. They partnered with Inspera Health to develop a personalized program informed by employee data that targeted employees with three or more chronic conditions. The program incorporated several key features shown to be effective in supporting these populations, including access to in-person help / dedicated support staff, a “whole person” approach, longer program duration, integrated behavioral care, a multidisciplinary support network and specific metrics to measure program impact. After 19 months, 80% of participants were still actively engaged and “85% of participants improved their health, 71% lost weight and 90% improved their physical activity [4].”

• Similarly, PwC utilized data and analytics to identify musculoskeletal (MSK) issues as an area of opportunity since treatment costs were increasing year over year and the majority of affected PwC employees were not receiving appropriate, non-surgical care. Since 80% of their staff was under the age of 30, they settled on a digital, coach-led program suitable for a younger employee population that went beyond physical therapy and opioid treatment, and included education, behavioral health treatment and exercise programs with sensors and tablets so participants could engage easily no matter their location. Ultimately, participants experienced a 66% reduction in anxiety, 52% reduction in depression and 53% improvement in productivity and absenteeism. PwC observed a 60% reduction in pain among participants, which is two times greater than the average opioid pain reduction and estimated that 2 out of 3 surgeries were avoided.

There isn’t a panacea or silver bullet that can completely resolve these challenges, but data and analytics can play a major role in helping organizations proactively manage high-cost claimants. With providers, payers and employers all aligned with the same common goal, together they can each play a role in reducing medical costs while improving the quality of life for individuals living with chronic conditions.

[1] http://www.americanhealthpolicy.org/Content/documents/resources/High_Cost_Claimants.pdf
[2] https://www.mercer.us/our-thinking/healthcare/high-cost-claims-by-the-numbers.html
[3] https://www.prb.org/aging-unitedstates-fact-sheet/
[4] https://www.ajmc.com/conferences/mbgh-2019/addressing-chronic-conditions-to-improve-health-of-employees-and-decrease-costs

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