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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

Keep Essential Workers Safe: Data Analytics Strategies to Guide Effective Benefits Design

Published in HR Executive
Authored by Rani Aravamudhan, Senior Clinical Consultant, HDMS


HR executives have followed the time-tested adage of past behavior being the best predictor of future behavior – evaluating utilization patterns over time to make educated projections for the upcoming year. However, given the skewed healthcare consumption caused by COVID-19, these traditional means of assessing year over year trends fall predictably short.

Read the five strategies suggested to guide effective benefit design.

Download PDF reprint

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.

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Spotlight

Infographic: Use Data to Design Effective Preventative Screening Programs

Article

Analytics are key to wellness success. Here’s why.

White Papers

Preventive Care Starts with the Annual Wellness Exam

For most people, the term “Preventive Care” suggests age appropriate cancer screenings, flu shots and childhood vaccinations. What about appropriate regular monitoring of symptoms that prevents the worsening of a chronic illness? Or timely interventions that reduce or prevent complications due to a medication or a stressful life event?

Preventive care is a much broader concept that includes (but not limited to) activities that lead to the overall reduction of adverse events (e.g. fewer life-threatening complications due to a chronic illness) and the promotion of overall health in the entire population.

The “Annual Wellness Exam” (aka Annual Physical, Annual check-up, Health Maintenance Visit, Preventive Care Visit, etc.) is perhaps one of the most underutilized benefits in a health plan even though it is available at no out-of-pocket cost to the covered individual (with most federal, state and commercial plans).

Getting an Annual Wellness Exam regularly offers two main advantages:

  • An individual who does not proactively seek care (often referred to as a “non-utilizer”) gets monitored for any new and/or existing physical and emotional problems, assessed for various risks and guided to close relevant care gaps (e.g. BMI, Mammogram, Blood Pressure check)
  • Establishes a relationship between the member (including his/ her family) and the Primary Care Provider (PCP)’s office making it more likely for the PCP to be the first point of contact for any health issue - rather than an Urgent Care or ER

From a Payer (Employer, Health Plan, other) and Provider (individual Physician, Group Practice, Health System, other) standpoint, there is also a financial advantage in ensuring all members get a Wellness Exam every year as described below.

What does the data show?

In looking at the Professional component of medical claims data for the last 3 years, HDMS saw an overwhelming trend among our customers.  We classified members into two groups:

  • Those that HAD received an Annual Wellness Exam during the reporting year
  • Those that had NOT had an Annual Wellness Exam during the reporting year

Adult members in both groups were then compared for ER Utilization, particularly for Avoidable ER usage using the NYU Emergent Status & AHRQ Prevention Quality Indicator (PQI) methodologies.

The results showed:

  • Members who have NOT had an Annual Wellness Exam within the last reporting year, consistently incurred higher overall Cost AND higher number of Visits to the ER for complaints (conditions) that are classified as: “Non-Emergent”, “Primary Care Appropriate” and “Preventable/ Avoidable.”
  • There were a higher number of members WITHOUT an Annual Wellness Exam within the last reporting year, with one or more visits to the ER for diagnoses, that qualify as “Ambulatory Care Sensitive Conditions”

What does this mean?

These reports show a clear pattern. Members who get an Annual Wellness Exam are less likely to use the ER for conditions that can be treated and/ or managed at a less expensive site of care. Hence, it is in the best interest of the organization to encourage and incent all their members to establish a relationship with a PCP and get regular Wellness exams.

White Papers

A Data-Smart Approach to Employee Benefits Management and Preventative Care

As healthcare costs continue to increase, more employers and health plans are evaluating the impact of their health and wellness benefits – including the effectiveness of preventive screenings.

Three out of five U.S. employers use health screenings and risk assessments to screen for expensive chronic conditions, such as cancer.1 Yet, 79 percent of large U.S. employers and 44 percent of mid-sized employers do not measure the effectiveness of employee wellness programs, including preventive screenings.2

With the cost of employee health benefits expected to rise 5 percent in 2019, it is critical that employers and health plans develop a data-centric approach to measuring the effectiveness of preventive screenings.3

How Data Insight Strengthens Preventive Cancer Screening Outcomes

Analytics inform a high-value approach for health benefits design by providing employers and health plans insights into opportunities for targeted interventions that reduce costs and improve health. Data analytics also help avoid “one-size-fits-most” solutions that may not be a good fi t given member and provider characteristics.

Increasingly, analytics are used to track outcomes of preventive care. For example, a recent study examined the impact of preventive cervical cancer screenings and showed these eff orts resulted in substantially lower deaths and increased lifespans.4

Analytics can also help employers and health plans prioritize preventive cancer screening offerings. Criteria might include:

  • Risk factors such as high proportions of members who are overweight, have high cholesterol or high blood sugar levels, or smoke.
  • Regional health trends that may point to potential socioeconomic-based risks for members, like higher-than-average prevalence rates of lung cancer or heart disease. For example, 6.2 percent of Ohio’s population has heart disease, even as rates across the nation dropped.5
  • Evidence of possible “hot spots” within a plan sponsor membership. For instance, analytics show certain locations where employers and health plans should focus eff orts on encouraging preventative cancer screenings (member education, onsite clinic involvement, etc.).

The analysis of claims data – as well as socioeconomic data that might be available from state and regional health organizations – can provide powerful insights in developing a high-value approach to preventative cancer screening health benefits for members that improves outcomes.

Case Study: Measure the Impact of Preventive Cancer Screenings

Employers and health plans can demonstrate success through data analytics by determining the impact of preventative cancer screenings on access to treatment, risk and costs of care.

For example, a state health plan covering around 205,000 employees and dependents set out to identify the rate at which members were diagnosed with cancer after undergoing preventive screenings for breast, colorectal and cervical cancers.

For the overall state population, new cases of colorectal and cervical cancer have been decreasing while new cases of breast cancer are increasing. However, analysis of claims data for the state health plan differs for state employees:

  • While the rate of newly diagnosed cases of breast cancer remained steady, it was higher than the state average.
  • The number of new cases of colorectal and cervical cancer among state employees increased; however, the rate of occurrences was lower than the state average.

By collaborating with HDMS experts, the state health plan created episode-based analysis groups, or cohorts, to assess compliance with preventive screenings compared to national guidelines and measure the impact of such screenings on early cancer detection and treatment.

Members in the episode-based analysis group included those who were newly diagnosed with breast, colorectal and cervical cancers as well as those who had been identified as having a recurring cancer diagnosis within two years of initial detection of the cancers. The results were enlightening:

Increased early diagnosis. The majority of new cases of breast, colorectal and cervical cancer were initially diagnosed following preventive screenings:

  • Preventive screenings were associated with 80% of new cases of breast cancer among plan members.
  • Among members who received preventive screenings, 11% received additional treatments – and not just for cancer (e.g., removal of benign tumors or polyps).
  • Cervical cancer screenings helped identify women who need additional testing to detect or rule out uterine or ovarian cancer.

Decreased risk. The study showed early diagnosis of cancer through preventive screenings was associated with significantly reduced members’ risk scores. Members who were diagnosed earlier through preventive screening had significantly lower concurrent risk scores compared to other members with the same type of cancer. Higher risk scores are typically associated with members with later stages of cancer that require more complex treatment.

Specifically, members diagnosed with breast cancer through preventive screenings had an average risk score of less than 1.00 while members diagnosed outside of preventive screenings had average risk scores from 5.88 to 6.53. Similarly, members diagnosed with cervical cancer through preventive screenings had average risk scores of 1.00 while those diagnosed later exhibited risk scores of 3.31 to 4.22.

Reduced costs of care. Analysis also revealed the impact of preventive screenings in lower costs of care. The cost of treating breast and cervical cancer for women identified by preventive screening was lower on average.6

Optimize Value Through Claims Analysis

The results showcase the power of using data to measure the effectiveness of preventive screenings. When employers and health plans leverage claims and socioeconomic data analysis to refine their approach to benefits design, they are more empowered to reduce costs and improve outcomes.

 

Download Whitepaper

 

  1. “Top 10 Health Conditions Costing Employers the Most,” Employee Benefit News, Feb. 9, 2018, https://www.benefitnews.com/slideshow/top-10-healthconditions-costing-employers-the-most
  2. Desai, P., “Why Health Screening Programs Fail and What Employers Can Do About It,” Corporate Wellness Magazine, https://www.corporatewellnessmagazine.com/worksite-wellness/health-screening-programs-fail/
  3. https://www.shrm.org/resourcesandtools/hr-topics/benefits/pages/employers-adjust-health-benefits-for-2019.aspx
  4. Kim, J.J., Burger, E.A., Regan, C., et al., “Screening for Cervical Cancer in Primary Care: A Decision Analysis for the U.S. Preventive Services Task Force,” JAMA, Aug. 21, 2018, https://jamanetwork.com/journals/jama/fullarticle/2697702
  5. “Heart Disease Hotspots: 14 States with Highest Rates,” CBS News, https://www.cbsnews.com/pictures/heart-disease-hotspots-14-states-with-highest-rates/
  6. HDMS proprietary data