Cumulative sticker shock triggers meaningful health benefits analysis

Inflation is accelerating — not just gas and food prices, but also health care costs, and at much faster rates than years past. At the same time, employers are working hard to attract, care for and retain employees with relevant and comprehensive benefits.

Healthcare point solutions (e.g., diabetes or blood pressure management solutions, fitness apps, etc.) have exploded in popularity as part of healthcare benefits, but they also cost a pretty penny.  Hence data analytics prove to be vital now (more than in the past) in driving decisions around offering, expanding, or discontinuing various ancillary benefit programs. The rise in the availability of new data types from these solutions enable nuanced and sophisticated analyses to determine the value of the whole benefits or “total rewards” packages as they are dubbed.

Analytics evolution: Powerful revelations from digital data connections

For years, the set of metrics used to measure the success of any program was limited to simple data elements available in standard claims – the number of members that enrolled in a program or the number of members that had one visit with a provider. Enrollment metrics are no longer the only available barometer for engagement.  For instance, the aggregation of traditional claims with non-traditional digital data sets allows for connection of dots that were previously invisible or had no access, thus revealing new trends and powerful insights.

Engagement In a program has thus been redefined. Rather than tracking how many people enroll in a certain wellness or fitness program, granular metrics like the frequency of use of apps or numbers of digital visits with clinicians  are used in tandem with medical and pharmacy claims to identify discernible, meaningful, and quantifiable value. Other examples of data types frequently leveraged from various solutions include biometrics (e.g., BMI, BP), lab tests (e.g., blood sugar, cholesterol, A1C, etc.), sleep patterns, meditation and mindfulness minutes, mood changes, dietary changes, etc. It is possible to then look holistically at a program’s impact on employees’ health, wellbeing, productivity, quality of life, and other indicators — driving more effective benefits decisions.

The concept of coordinated and continuous care is not new, but clinical and digital transformation across the industry is now bringing us closer to achieving it. Understanding an individual’s interactions with care when they are healthy and not just when they are ill drives policy changes to reduce the sick times.

The ability to identify who is, or is not, engaging with a certain program — and why — gives organizations the power to build programs around their employees’ real-life needs. Questions commonly asked of our data analytics teams include:

• How often are employees using our wellness or chronic condition management programs?
• Have those programs yielded positive health outcomes?
• Are there other health gaps that we should be addressing instead?

Case studies: What employers are learning

A few case studies illustrate how data is impacting employer healthcare decision-making:

1. In the first case study, an organization has a high prevalence of hypertension. It offers a diet and nutrition program with an app that allows for personalized nutritionist consults, meal plans and more. The organization then tracks the utilization of specific app features and sees strong adoption. Their pharmacy claims show a decline in the average number of hypertension medications prescribed to each member in the group with high adoption compared to non-engaged members with hypertension. This is tangible evidence that the diet and nutrition benefit is improving health outcomes for this group.
2. All programs offer something and will benefit some people in an organization. The question is often whether the magnitude of the benefit derived is enough to offset the cost of the program itself. This in turn drives decisions around expanding or discontinuing a solution.

In the second case study, one large employer offered two separate wellness programs that promoted a healthy lifestyle with diet and exercise goals. However, one focused more on the exercise component while the other concentrated on healthy and mindful eating. When they tried to assess the effectiveness of both (to determine if one added more value than the other overall), the data showed a very interesting and unique pattern. Adoption and engagement in the two solutions differed along racial, ethnic and income lines. Different populations engaged with these solutions likely for reasons outside of health status. As a result, the company decided to retain both programs since they obviously were essential to varied groups.

3. Mental wellbeing is now universally being recognized as a critical part of overall health. Organizations are evaluating impact of mental health solutions on their employees’ medical co-morbid conditions.

Related: Welcoming Apple to the health care world: Health features and apps

For one such organization, bringing together medical, pharmacy and mental health EAP (digital) data brought to light an interesting link between anxiety and heart disease. Specifically, 30% of those with a new diagnosis of anxiety also had a new diagnosis of hypertension and/ or ischemic heart disease in the same year. They also had sought care for other indicators for acute stress. This insight helped the benefits team better align their concierge services to ensure a more holistic health model, where the mental and physical health needs were addressed together.

How can data drive value for you?

Health benefits are far too expensive and important to select based on partial insights or guesswork. Thankfully, the days of using proxy indicators to measure success are over. The growing convergence of digital and traditional data allows organizations to evaluate programs in the context of real value to support their most valuable resource — their employees.

Dr. Rani Aravamudhan leads HDMS Clinical Advisory services. She is a general medicine physician who cares for individuals yet connects experiences to population health perspectives using her deep data expertise. Rani is known for her work in data-driven transformation, workflow design and development, value-based care, risk management and clinical quality and performance reporting. Her work and team guides clients to understand what is possible with data, find answers and insights within projects and analyses, and gather context and scale across the HDMS client base.