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Key 2022 Health Trends – and what these mean for Employers

Healthcare is a top area of organizational spend.

What are some key trends in healthcare in 2022 and what can this mean for you, as a self-funded plan sponsor?

If you want to skip to the punchline –

  • costs are rising
  • the health system itself is evolving
  • DEI, SDoH, price transparency, and artificial intelligence invite new opportunities

In two words – EXPENSIVE CHANGE.

The big takeaway?  Take control and use the massive amounts of data available.  Health analytics will help you strategically navigate market transformation, minimize excess costs, and gain first-mover advantage to secure cost avoidance.

1. Healthcare costs are rising, and this will continue.

CMS.gov shared that U.S. health care spending increased 9.7 percent and reached $4.1 trillion in 2020.  The health “share” of our economy is projected to rise from 17.7 percent in 2018 to 19.7 percent in 2028. The most significant driver behind this is primarily driven by increases in health sector wages.

What this means for employers as plan sponsors:

As a payor, you’ll want to have the right tools and/or partnerships in place to be able to accurately anticipate and confidently strategize regarding your portion of costs.

What to do:

Offset increases in costs by optimizing plan performance.  Use analytics to surface savings opportunities.  The Great Resignation and turnover may have changed your employee base signficantly, and needs themselves are evolving.  You’ll need a refreshed and keen understanding of the workforce’s needs, utilization patterns, and engagement preferences.

Let’s assume you made solid decisions around health plans, plan administration, programs and policies.  Now, get the most out of these choices and proactively drive to the highest level of value possible.

Key TIP: Offset increases in costs by optimizing plan performance.

 

Eliminate guesswork.  Use health data to get the most out of partners and programs.

2. Market and industry disruption continues.

We’re watching the pandemic accelerate the adoption of virtual care and at-home care. We see business investment and digital innovation compliment longstanding desires for an increasingly streamlined, affordable system with improved care access and health equity.

Providers and carriers continue to explore and innovate business models. Value-based care options and accountable care organizations are growing.  There are countless new market entrants in the wellness and point solution space.

What this means for employers as plan sponsors:

New health programs and options may offer interesting visions, yet leave employers with many unknowns.

What to do:

Build a repeatable model that lets you easily pilot new approaches and comparably measure results. Find a partner to do the heavy lifting around data sourcing and management.  Focus your precious internal resource time on using insights with leadership and making strategic decisions.  Choose business partners with seasoned consultative and analytic specialists to track and compare cohorts.  Create holistic views that show associated impacts and connected costs.  Lean on your partner’s expertise to define metrics and analytic views in ways that support decisions around program expansion, change, or termination.

Key TIP: Create a data-driven program and repeatable model that lets you pilot new strategies or programs and compare measured results.

 

For instance:

Which joint replacement care approaches have the shortest associated disability duration, fewest average physical therapy visits, and lowest prescription costs?

Choose a flexible analytics technology that will let you analyze results across different plans, evolve over time, and bring in point solution data.

Work with an established vendor whose core vision is in line with providing health analytics to employers, like yourself.

Health Big Data is complex.  It requires highly specialized skill sets and domain expertise.

3. DEI and SDoH command the spotlight.

Survey results that report statistics like 83% of U.S. organizations reported implementing diversity, equity and inclusion initiatives in 2021  and political activity like The Improving Social Determinants of Health Act of 2021 (S. 104/H.R. 379), illustrate the magnitude of momentum.

What this means for employers as plan sponsors:

2022 invites enormous opportunity to drive change by partnering with emerging DEI leaders who bring passion and creativity to evolving program design in ways that accommodate a broader set of needs.  The very qualities that make us unique and special and our social environments (both home and at work) influence what works well for each of us when it relates to health.  Employers that can measure what’s working for whom are set up to make smart and responsible benefits design decisions tailored for their population.

 

What to do:

Create a data-driven, measurable DEI and SDoH framework around your benefit offerings.  This could be looking at traditional metrics by salary range or race, incorporating third-party regional SDoH data, or even looking at variances in condition prevalence based on job types. Look at a minimum of 2 years of baseline data and prioritize equity gaps and key drivers.  Host small group lunches to ask employees for context around what you see in the numbers.  Engage with health plan- and community-based resources alternatives.  With analytic rigor surrounding your efforts, and a multi-disciplinary approach, you’ll see an impressive impact.

4. Health is more top of mind for more individuals.

Literally.  The need for mental health services is increased, and mental health has a known influence on physical health and overall health costs.  A Kaiser Family Foundation (KFF) study found that during the pandemic, about 4 in 10 adults in the U.S. have reported symptoms of anxiety or depressive disorder, up from one in ten adults who reported these symptoms from January to June 2019.  One insurance company published mental health claims increased by 25% in 2020.

What this means for employers as plan sponsors:

Recognize your ability to support the whole person through benefits design.  Hit the streets and ask your employees what’s important and what is needed.

Total wellbeing?

You need connected data.

What to do:

It’s a great time to listen to what people say, and measure what they do.  In addition to adding mental health benefits, pilot environmental changes.  How do a blend of policy updates (e.g. mandatory meeting-free lunch-and-wellness hour?) and benefits expansion (coupled with a wellness program with fitness incentives) associate to employee satisfaction survey data and business goal measurement?  The data exists.  It’s just a matter of analyzing it.

 

5. Artificial intelligence, machine learning, and predictive analytics foundations bloom.

AI and ML are being applied for clinical innovation in areas like medical imaging, drug discovery, and to predict disease early.  These are areas beyond an Employer’s realm.  But these same technical innovations are useful in population health management – for planning, resource management, and targeted communications.

 

What this means for employers as plan sponsors:

You’ll see more and more talk about health Big Data being used to define health experiences that personalize care, address diverse needs and preferences, and icrease engagement.

As an Employer, you are set to take a leading role in putting health data to work using AI, ML, and predictive analytics.  You have access to significant data, control of working conditions and environment, and a trusted relationship with individuals.

Creating a culture that grooms healthier people, invites short-term and long-term advantages:

  • increase health
  • lower health costs
  • improve employee retention
  • increase productivity
What to do:

Right now – use AI, ML, and predictive analytics to plan resources, build business continuity and forecast costs more accurately.  Next, dig into rising risk groups. Identify actions that influence a more positive outcome.  Work with vendors that embrace these technological opportunities – and ask them to articulate how their strategies drive savings for you, as a payor.

Health data is special

You want to analyze Low Value Care or isolate non-emergent ER visits?

Is the data a click away OR a data-engineering-month away?

 

Processing claims data for analytics means completely restructuring data so that it is useful.  It’s not just a final, adjudicated view of each claim. Claims data is translated into a mini-health biography for a set of care services tied to a specific member across a moment.

 

SIMPLE: How many patients in Hospital A were diagnosed with COVID?

 

COMPLEX: How many employees had COVID inpatient visits? What other conditions do they have? What is the range and value of related services during stays and recovery?  How many days of missed work?

 

 

Act, take control.

The costs are high, change is certain, and you have a host of strategic decisions you’ll be making.  Everything will be easier if you have answers at your fingertips for data-driven decisions, and a trusted team that can take care of managing data for you. Getting by with the same reports you used last year is no longer enough.

Three tips to consider:

  1. Resist expanding a BI solution: The allure to apply a corporate business intelligence solution to a new domain (health data) is strong. But consider the data.  You’ll need clinical expertise, claims processing logic, coding, classification, and enrichment to create dimensionality to ask specific questions.  There’s a reason there is an entire industry around processing health Big Data.    A specialized solution will bring sophisticated health data management that does this.  A corporate BI tool will not.

 

  1. Partner with experts: Work with a trusted advisor with a strong health analytics practice that can provide you with data-driven answers. Consider a partner who will open dashboards in meetings and navigate through data in response to your ‘next questions’ as you work through a topic.  Look for firms that can translate what they see in the numbers – clinical expertise coupled with health system expertise.  What are viable alternatives for certain types of care visits?  What are special considerations for individuals with particular conditions?

 

  1. Focus on the fun part: Build data-driven expertise within your team and leave the detailed data work to a specialty partner. The highest value (and most interesting work!) comes from using the data, so spend your time on that.  Organize so the work of sourcing, integrating, and enriching data is just done for you.  Vendors like HDMS build efficiencies by performing and optimizing back-end work across many clients.  Expect trusted, reliable data without the headaches.  If needed, augment in-house expertise using Analytics Practice services, but keep at least one resource close to the work.

 

It doesn’t have to be hard.  HDMS works with many large employers that ask one or two internal resources to drive a strategic program, sometimes among other responsibilities.  HDMS takes care of the rest. Start today to build a culture and strategic competence around data-driven decisions for a top-area of organizational spend.

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Article

A data-driven approach to health care benefits that bridge equity gaps


There are tremendous point solution programs out there.

With so many diverse needs and suspected equity gaps, where should investments be made? How does one start to show evidence of the true value delivered?

Learn from HDMS clients finding answers. Read some keen insights shared by our client experience team leader, Jason Elliott, published in HR.com.


Or just read below – we’ve copied the article to this page.

Image from Shutterstock

Meaningful Health Benefits For All (HR.com)

A data-driven approach to health care benefits that bridge equity gaps

By Jason Elliott | September 9, 2022 Read time: 5 min

Any HR executive today will tell you how challenging the current environment is for attracting and retaining talent. According to the US Bureau of Labor Statistics, there were 11.2 million open job positions (vacancies) across the US at the end of Q2 2022. Hence, it stands to reason that organizations are rolling out the red carpet to try to appeal to job-seekers, offering sign-on bonuses as well as comprehensive health and financial benefits. Wellness rewards and a varied suite of ancillary health benefits have become the norm rather than the exception.   

While the impact of these comprehensive ancillary benefits can be significant, so can the cost—especially since inflation is at a 40-year high. Therefore, data analytics are more vital now than in the past for driving decisions around the offering, expanding, or discontinuing various programs. The rise in the availability of new data enables nuanced and sophisticated analyses to determine the value of “total rewards” packages.  

Analytics Evolution: Powerful Revelations from Digital Data Connections  

For years, the set of metrics used to measure the success of any health benefits program was limited to simple data elements available in standard claims. For example, the number of members who enrolled in a program, or the number of members who had one visit with a health care provider. Unfortunately, measurable health impacts usually do not occur immediately. Therefore, the success of a program cannot be determined immediately with any level of confidence.   

However, enrollment metrics are no longer the only available barometer for engagement. The aggregation of traditional claims with non-traditional digital data sets now allows us to connect dots that were previously either invisible or inaccessible, thus revealing new trends and powerful insights. 

The ability to identify how members engage with a certain program—and why—gives organizations the power to build programs around their employees’ real-life needs and make progress in delivering on the triple aim of reducing cost, improving health, and improving quality and experience. Questions commonly asked of our data analytics teams recently include:

  • How often are employees using our wellness or chronic condition management programs? 
  • Are engagement patterns different for different sub-populations?
  • Have those programs yielded positive health outcomes? 
  • Are there other health gaps that should be addressed instead? 

Engagement in a program has been redefined. For example, rather than tracking just a daily step count in a fitness program, granular metrics like the frequency and intensity of biking, running, weight training, or even dancing 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, retention, disability avoidance, 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 burden of illness. 

New Insights to Articulate Value

All programs offer something and will benefit some of the people in an organization. Often, the question is 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. 

One large employer, for instance, 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 the employer tried to assess the effectiveness of both programs to determine if one added more value than the other overall, the data showed a very interesting and unique pattern. It revealed that adoption and engagement in the two solutions differed along racial, ethnic, and income lines. Different populations engaged with these solutions at similar levels for reasons outside of health status. As a result, the company decided to retain both programs since they obviously were essential to varied groups. 

Likewise, mental well-being is now universally recognized as a critical part of overall health. Many organizations are compiling a profile of those who engage with their mental well-being solutions (e.g., EAP mental health programs) and evaluating their impact on employees’ medical comorbid conditions.

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 sought care for other indicators of acute stress, such as flare-ups of autoimmune conditions. This insight helped the benefits team better align their concierge services to ensure a more holistic health model, where the mental, emotional, and physical health needs were addressed together.

Employers are also making major changes to benefit designs in a deliberate effort to remove barriers to care access, especially since the pandemic exposed the vulnerabilities of low-income and minority communities to weather major health storms. 

As an example, several organizations have removed waiting periods (typically 30-90 days) for new employees to become eligible for health and financial benefits. Others have expanded paid sick time benefits for all workers, including hourly employees for whom paid sick time used to be rare. Some organizations that offer wellness rewards and incentives have done away with mandatory activities with incremental payouts.

Value for All 

Across health care, clinical and digital transformations are making it easier to analyze how people interact with health benefits—both mental and physical health, when sick and when well. Consequently, data also makes it possible to evaluate how the advantages of various programs differ for different subpopulations. When benefits are designed for total well-being, their value cannot be measured in silos—hence data analysts have become the new superheroes.

Jason Elliott Vice President of Employer Customer Experiences
Health Data & Management Solutions (HDMS)

Jason Elliott is Vice President of Customer Experience for Employer clients at HDMS.  A true public health enthusiast with a Masters in Epidemiology, he spent over a decade delivering dedicated clinical analytics and leadership at BCBS.  Since then, Jason has managed the managed the Employer practice area.  He brings very structured thinking into the types of problems his clients are trying to solve, and what can be done with the insights discovered.

Article

Point Solutions - Let's talk outcomes


Employers investing in wellness programs and point solutions, but the cost of the collection of programs is exacerbating inflated health care costs.

As decision-makers think about what to keep and where to reduce costs, the increasing amount of data available for insights is proving very worthy.

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

(Photo: Khakimullin Aleksandr/Shutterstock)

Cumulative sticker shock triggers meaningful health benefits analysis (Benefits Pro)

Data analytics prove to be vital now (more than in the past) in driving decisions around offering, expanding, or discontinuing various ancillary benefit programs.

By Rani Aravamudhan | July 28, 2022 at 10:15 AM

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.

  1. 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.

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.

Rani Aravamudhan

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.

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

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Let’s dig into connected health views.