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Article

How to measure your wellness program’s ROI

Published in BenefitsPro
Authored by Rani Aravamudhan, Senior Clinical Consultant, HDMS


While many employers are willing to invest in wellness programs, they aren’t always clear on the goals for these benefits.

Rather than jumping on the wellness bandwagon or adding a program just to expand the suite of benefits, employers would be better served to evaluate and make decisions based on data.

Read how HDMS recommends employers approach this, published in Benefits Pro.
or just read below – we’ve copied the article to this page.

Achieving maximum ROI from wellness programs comes from changing behaviors, especially of those who are most at risk for adverse health events and consequently would benefit the most from these initiatives. (Photo: Shutterstock)

Wellness Programs

Wellness programs have become a staple of employer benefits offerings. According to one KFF trends report, nearly 9 in 10 employers with a workforce of 200 or more offered some sort of workplace wellness initiative in 2019.

While many employers are willing to invest in wellness programs—which often are offered through third-party vendors—they aren’t always clear on the goals for these benefits. Multiple surveys and studies across the industry attest to this. No clear goals mean no systematic approach to defining and consequently measuring ROIs.

Most employers rely on wellness vendors’ claims about the potential to improve health outcomes and reduce health care costs. They do not necessarily have the means and/ or expertise to independently verify the proposed advantages either prior to or after implementing them.

Metrics used by vendors to illustrate their successes are not always applicable to all populations or groups. For instance, let’s say a vendor’s “expected outcomes” include a 20% increase in smoking cessation rates. Is that 20% over three months or over three years since the last smoking incident? Or, is it based on a one-time pledge by the participant? What was the size of the overall smoker population in their sample data? Then there is always the question, “Is that the right metric for your population?”

Data analytics can provide objective insights to evaluate such partnerships before beginning, renewing or expanding a wellness program.

Prepare for the evaluation

Like any strategic endeavor, effective ROI measurement requires diligent groundwork before actual data analysis can begin. It is essential to ensure that the right metrics are chosen for measurement of “before” and “after” states.

Concrete objectives will vary by employer as well as by program—but beware of setting goals focused solely on short-term “dollars-in” vs. “dollars-out.” An effective wellness program aimed at promoting better rates of preventive care with active engagement may actually increase expenses in the immediate and short term. In such cases, the true long-term objective should be to shift health care services from unpredictable, high-cost settings like the emergency department (ED) to more predictable, lower-cost settings like primary care physicians’ offices.

Today’s wellness programs tend to be more holistic in their approach to employee health than the offerings of just a few years ago. Many employers are looking for more than isolated reductions in smoking rates or ED visits. They are starting to understand the overall health and financial benefits to their businesses possible through programs that integrate physical health with mental health and well-being. This adds obvious layers and complexity to the ROI conversation.

Preparing for any ROI measurement requires assessing all the data sources at your disposal. It’s critical to obtain as close to a 360-degree view of the entire employee population as possible, which typically requires melding multiple disparate data sources. Medical and pharmacy claims, lab values, biometric and clinical data from electronic health records (EHRs) are some examples. Data warehousing and analytics solutions can help this process by aggregating, integrating, enriching and normalizing data along with consultative services to provide the right insights.

Finally, a realistic timeframe for measuring program outcomes is a must, especially when claims are part of equation, to allow for the time lag between services rendered and paid out. Hence 12 to 18 months serves as an optimal window to gauge discernible changes to patterns of care experience and member behavior. That said, periodic measurements throughout this time are essential for tweaking and adjusting workflows and processes to ensure proper data aggregation (e.g. presence of required code sets, uniform cadence in receipt of various data types, etc.).

Data-driven ROI analysis

Be aware of employee engagement factors

Achieving maximum ROI from wellness programs comes from changing behaviors, especially of those who are most at risk for adverse health events and consequently would benefit the most from these initiatives. For example, employees with chronic conditions who struggle with medication adherence or with managing stress due to work and family obligations. Promoting and maintaining engagement in such groups is challenging, but key to the success of the program itself.

On the same token, initial engagement tends to be high among members who are healthier and would likely gain little from a wellness or similar program, especially when there are participation incentives involved. Engagement typically tends to decline once the incentive requirements are met or phased out.

Setting up cohorts of participants with these factors in mind is critical because the metrics chosen to measure success levels in each will vary. Leveraging the expertise of data analytics vendors and consultants to define and set up such study cohorts—with and without comparable controls—goes a long way in these endeavors.

For example, employees who are engaged in wellness programs tend to also take advantage of preventive services and have a primary care provider. Consequently, data typically will show that they have higher rates of primary care and in-network utilization—whereas those who don’t participate have more ED and out-of-network services.

Establish key metrics

t is vital to ask the question, “Are we measuring the right things for each cohort for this particular initiative?” Defining the right metrics for a cohort is therefore an important aspect of the study design. Example: Establishing new primary care provider relationships and closing care gaps would be good metrics for employees who have traditionally not sought regular primary care in the past. On the other hand, keeping pertinent lab or biometric values within normal ranges, or garnering low scores on health risk assessment tools may be better suited for healthier and more engaged populations. Establishing clear baselines for each metric on day 0 is imperative for apples-to-apples comparisons.

Many employers are using non-traditional data sources to track metrics like sick time, other leave utilization, and rates of disability claims to evaluate the effectiveness of a wellness program. Data analytics and warehousing vendors offer tremendous advantages in this area by integrating disparate data sources.

Consider a pilot program

Pilot programs for a carefully chosen group with comparative control groups is always recommended, especially for new wellness initiatives. In addition to ironing out administrative and process challenges, they provide a great means of gauging the operational effort and resources required. This is an often-overlooked expense not featured in ROI calculations.

Results from a pilot program can go a long way toward determining an effective roll-out strategy. It’s essential to compare these results against the total employee population for the same timeframe. Example: An increase in the rates of flu vaccine compliance among employees in a pilot group does not mean much if vaccine compliance also increased in the total employee population due to onsite flu clinics. With successful pilots that show a definite improvement in outcomes for the participants, odds of further success are better when the program is expanded to demographically similar employees.

Let measurable results drive strategic investments

The last few months have brought renewed focus on the overall well-being of the workforce. Employers recognize the importance of the physical, mental and emotional wellness of their employees and their families. It’s not surprising that wellness program vendors, especially those that provide integrated services, are popular.

But rather than jumping on the wellness bandwagon or adding a program just to expand the suite of benefits, employers would be better served to make data-driven decisions. They would do well to engage the many data analytics vendors who provide evaluation services to answer key questions. “Is this right for our company?” and “Will this save me money on health care costs?” are the types of questions that can be answered even before the program is implemented, based on existing statistics or sample data sets.

Rani Aravamudhan

Rani Aravamudhan is senior clinical consultant at HDMS. She is a physician (specialty – General Medicine) with extensive experience in the EMR/EHR and population health industries with a focus on clinical transformation, workflow design and development, value-based care, risk management and clinical quality and performance reporting. Her strong background in clinical medicine and experience in the HIT industry make her successful in navigating payer, provider and technology vendor landscapes.

Article

Benefits Design Decisions during The Great Resignation

Trying to keep employees happy and healthy? Trying to attract new talent?

With staggering resignation rates throughout the country, employers are naturally looking at benefits. What’s the right mix to both retain employees to prevent expensive losses, and attract replenishment talent?

Navigating the “Great Resignation” to an advantage means directly addressing these unknowns. It requires a holistic approach to health benefits. It’s no longer enough to have a handful of options that seem like they should fulfill employees’ specific needs.

Think about the relationship between a doctor and their patient. Providers consider the whole patient, including their demographics, medical history, and social determinants of health. Yes, they focus on health outcomes, but also fostering better patient experience and satisfaction levels to ensure their practice maintains a stellar reputation. If you too can take a holistic view of your workforce population, you’ll nail it. You’ll offer competitive and thoughtfully designed health benefits that really resonate with employees.

How do you do this?

With data you already have access to.

Derive insights from powerful data stories that exist about your workforce.

Your health benefits will not only address your employees’ needs but anticipate them. You won’t fear the sticker shock that comes with an expansive benefits package. Having analyzed health data you will have eliminated under-utilized and costly benefits that your employees don’t need or want. You’ll get better value for what you are spending.

New ways of working, we’re all still adjusting.

A record 4 million people quit their jobs in April 2021 alone, for reasons largely stemming from dissatisfaction, whether in pay, flexibility, or work-life happiness.

Read the NPR Article

Use data to design the right health and wellness programs for YOUR population.


No guess work or finger crossing.

Happy, healthy employees – the building blocks for success and long term loyalty.

With so much data available from multiple sources, how can you interpret and utilize it properly?

Health data is key to successful transformation as a side effect of the “Great Resignation” trend. Use analytics to evaluate specific benefits and associated holisitic wellness. Do mental health apps reduce reliance on prescription pain medications or chiropractic visits? Get a better understanding of employees’ wants, needs, and what’s working. Unfortunately the individual reports you have today can’t always connect the dots for you.

Using data differently gives is broader understanding of people, wellness, and ultimately, productivity. It’s easy with connected health data and even fun (!) with a predictive analytics system. Equipped with data-driven insights, you’ll create a competitive advantage beyond just hiring, by offering benefits that really work for your employees. You’ll have happier, healthier employees bringing their best self to work everyday.

Cultivate a connected health view

A connected view of your health data makes it possible to spot emerging trends more quickly and evaluate employee behaviors as they evolve. You can monitor in real-time how your population uses their healthcare services to identify opportunities for improvement and increase employee retention.

For example, you can use prescription data to view trends in new medication for anxiety and depression as an indicator of your workforce’s overall wellbeing. This canary in a coal mine can help you implement wellness perks for your employees more quickly, such as mental health days or increased behavioral health services. Connected health data creates a birds-eye view of your population’s greatest commonalities and shared wants and needs. If many of your employees have dependents, childcare coverage might resonate more than the social benefits that young professionals may seek.

By integrating all types of employee benefits data — from traditional sources (such as medical, eligibility, and pharmacy) and non-traditional sources (such as wellness programs, disease or care management programs, biometrics, wearables, provider and lab data) — you have the power to create a benefits program specifically targeted to your employees.

More than ever, employees need to feel valued and employers need to improve retention rates with competitive and custom health benefits. Understanding the “Great Resignation,” particularly how to address it, is critical for your company’s success in the immediate and long-term future.

Won’t this pass soon?

It’s not just about retention or hiring.

Nurture happier, healthier employees who bring their best self to work everyday.

You are not alone

Arm yourself with data visualizations, cohort analysis, and other tools. Easily evaluate your workforce and adjust health programs to meet their evolving expectations.  We’ll help you deliver measured results and continued success… long after the Great Resignation. 

Your friends at HDMS

Let’s dig into connected health views.

Spotlight

Diversity, Equity, and Inclusion. Start with understanding where you really are...

Use Analytics to dig into specific parts of your population and better understand unique health concerns, emerging needs, and guide decisions for a more thoughtful benefits strategy.

HDMS clients have access to Transgender Health dashboards.
Look at some of the insights that clients may find.

Click here for just one example…


HDMS clients – have your team walk you through your dashboards. Where can you take this next? We’ll help tailor and expand this for you!

Spotlight

Five essential strategies for healthy essential employees

This year, you’ve adjusted the physical working environments in your offices, manufacturing plants, and retail floors. You’ve adopted the state, local, and CDC guidance with your own new policies for COVID-19. You’re even offering free flu shots on-site for your employees.

Wondering what else you can do to support your essential workers? By analyzing a few key metrics, you can make decisions that support employee health and productivity—now, and into the future.

Get the guide.

Spotlight

Five Tips on how to Optimize your Cost Model during COVID-19

The COVID-19 Cost Model built by HDMS is used and adapted to meet our client’s needs and interests. We invite you to download this model and use as-is – or customize it to suit your specific needs. Here are a few tips to get the most out of your COVID-19 cost model:

Use a cross-functional approach: Obtain representation and contribution from across the organization, with inputs from Finance, Data, and Clinical/ Healthcare Specialist resources, at a minimum. Clinical/Healthcare Specialists ensure your model is answering key questions, contains accurate assumptions, and reflects current thinking. Your data team, working in collaboration will create efficiencies in balancing what you want to know and what data is available to support this investigation. They can help build for a refreshable model and possibly introduce new ideas to the team based on data possibilities. The finance team will guide calculation accuracy and are usually great partners to help with organizational buy-in and create confidence in the model.

Build for ranges: Create a model framework that invites ranges both as inputs and outputs. The template provided contains both range estimates and is constructed for three case scenarios to help accommodate different organizational approaches. This increases the usability of the model, as ranges communicate a realistic spectrum. By understanding how large the range can be, you innately create context for anyone using the model outputs.

Create in parallel: Again, collaboration is key. Let data analysis help inform increasing specificity for your cost model. As you develop your model, assess the data available to support increased granularity and actionability. Bring in member demographics like age, gender, geography and use historical health insights along with HR data to consider health risks, role type and job type considerations. Health plans might also wish to analyze by industry, geography, plan type, or other business attributes for additional aggregated insights.

Build assumptions as variables: What we know is rapidly changing and evolving. By building assumptions in as variables, rather than use within formulas, you can update your assumptions more easily and completely. Showing the values as variables on the model itself also creates transparency so it is clear what is used in calculations.

Make the model self-documenting: Document within the model itself. Cite and link to sources, show assumptions as variables as shared above, and create a high level of transparency to help answer as many questions as possible. Make it easy to check sources to see if recommendations or assumptions merit updating based on evolving information.

Download the HDMS COVID-19 Cost Model to see and play with a working model. If you are an HDMS client, reach out to your customer experience team for a more detailed and customizable model that works cohesively with your HDMS implementation.

Spotlight

Predictive Analytics – Do more with data. Use it to look ahead

Getting people like Takki to the right care is critical. 

But how do you find people who most need care when they don’t go to the doctors?

 

Predictive analytics help us spot Takki.

Takki has a heart condition.  Her health history shows a number of ER visits, diagnoses without follow ups, and no PCP utilization.  We can’t always see health risks walking around the office or warehouse.  Takki looks like lots of other hard working 27-year-olds.  She’s always helping her family and enjoys time with friends.  But family and friends need her complicated condition managed so she will be there for them in years to come.

Predictive analytics – not your major in college? Know the basics to contribute meaningfully in meetings. 

Watch this 20-minute video for executives and be confident. You’ll understand the basics of how these technologies work. Now you can have a point of view on where and how your organization can make investments in this emerging hot spot.

Predict: Use the data you have, differently.  Use data about the past to look ahead, with predictive analytics.  Send population health data through predictive models to anticipate what is likely to happen.  See statistical results specific to your population.  And even more amazingly – imprint actionable member-level predictions when running HDMS predictive models. 

How?  In addition to understanding the big picture, initiate care actions towards members who meet each segment criteria.

Disrupt: Put your predictive efforts in places where you can make a difference.  Once you have data-driven predictions, what can you do to move in the direction you want?  Who is likely to have an emergency room visit?  Who is likely to become an inpatient admission?  Once you know this, what can you do to avoid these circumstances?  How can you engage a member before the prediction becomes a past event?

Marry data-driven predictions with proactive actions.  You’ll drive more positive outcomes, reduce unnecessary costs, or improve the affordability and convenience of health care.  Find the rising risk in your population and take action to embrace people to encourage a path to better health.

 

Approach predictive analytics as part of a cohesive analytic strategy

Artificial intelligence and predictive analytics are cool, but they need you.  You are the critical resource that can help your organization invest energy and effort around predictive analytics in the right places.

Great!  Um, where’s that? 

It helps to think big picture.  Approach predictive analytics as an extension of a holistic strategy and cohesive platform. 

 
Holistic Strategy

Where can you make a difference?  What resources, recommendations, offers or alternatives can you offer a person? How can you improve the predicted state (if the prediction is negative) or increase the likelihood (if the prediction is positive)?  Think about what your organization can immediately take action on.

 
Cohesive platform
  • Use your historical and trend analytics to orient and quantify where you have improvement opportunities. Uncover drivers and root causes, and determine if you can impact change effectively. 
  • Build leading indicators to provide a near term view of how market trends are manifesting within your specific population, in places where you plan to implement predictive models. 
  • Measure and monitor the effectiveness of disruptive actions that aim to positively influence predicted outcomes.
  • Evolve predictive models so predictions are trustworthy and reflective of the rapidly changing face of health care.

See an example of how predictive analytics layer into trends in mental health.  Watch this 10 minute video.

Predictive Analytics Resources

Predict and Disrupt (60 minute webinar) | Watch

What every Executive should know about predictive analytics | Watch

Key ingredients to build or buy predictive models | Coming soon

A Cohesive Strategy for Predictive Analytics – Example with Mental Health | Watch

3 Use Cases for Predictive Analytics (and ideas for intervention strategies) | Check it out

Spotlight

Infographic: Use Data to Design Effective Preventative Screening Programs