Skip Navigation
Article

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.

Trending now

Best Practices to Measure Point Solution Value

Have answers regarding “Point Solution Value” that your boss will love.

Point solutions have been a great way to enhance benefits and provide care for a targeted need. 

Large employers and plan sponsors have on average 9+ point solutions as part of their health and wellness benefits.  But as point solution costs add up, the pressure increases to understand, and sometimes PROVE, the value. 

Most firms have programs that help workers identify health issues and manage chronic conditions (health risk assessments, biometric screenings, and health promotion programs). 

83% of large firms offer a program in at least one of these areas: smoking cessation, weight management, and behavioral or lifestyle coaching.

Source: Kaiser Family Foundation study

So, here are three best practices to consider, to deliver business decision-ready analytics, about the value of point solutions.


Best Practice #1: Use a cohort strategy to evaluate point solutions.

  • Cohort comparisons are the ultimate analytic strategy for proving value. Without a direct comparison within the same population, there are so many factors that introduce doubt on what the numbers truly capture. Alternatively, by looking at well defined and specifically differentiated groupings of people, we can directly compare performance take away concrete and specific learnings.

Here are two more pro tips:

  1. Look at related costs across your cohorts: Determine if there is value beyond just the immediate program financials. For instance, we have looked at disability claims, to measure the influence of a point solution program.
  2. Look at related health concerns: Investigate other aspects of wellbeing to see if there are notable halo effects.  For instance, we have investigated if there are mental health differences across maternity program types, short and longer term.

Here’s a good example from our client base: This national retailer wanted to measure the value of a Center of Excellence strategy for heart conditions.  The metric strategy compared a well-defined pair of cohorts that looked beyond traditional utilization and cost metrics.  We helped them also include mortality rates (COE – lower), returns to work (COE – faster), outcomes (COE – better), and company satisfaction (COE – higher).  Yes, that’s right – employees actually reported a higher employee satisfaction rate on the survey following a major episode of care.


Best Practice #2: Ask the right analytic questions.

  • Often “What’s the value?” is the wrong question. The correct question is “Who is this valuable for?” or “What’s the incremental value?”

There will always be a portion of a population that is engaged in their health and wellness. Your data can tell you who this population is, and provide insights that help you identify more people “like them” that you can target and pull along, therefore increasing program value. Also consider if the engaged audience would have been healthy or well without the special program, in some other way. Is it the program – or the people – that are providing the results you see?

Analyze for the big picture and long term.

Choice might be the right choice. The optimal strategy may not be selecting the best performing program in some cases. Use data to confirm if similar point solution programs are engaging the same or different audiences.

One self-funded employer had two somewhat similar wellness point solutions – Solution A emphasized “exercise and feel better.”  Solution B emphasized “Eat right and feel better.”  They both showed value – which one should they keep?  A deeper investigation of the data revealed that the solutions were in fact engaging somewhat different audiences.  The self-funded plan sponsor found they increased the value of BOTH point solutions by understanding the demographic nuances, and creating more targeted communications and incentives that used these insights.

Design Early Indicator metrics. Don’t wait for results (e.g., traditionally after year 3 of data is collected and analyzed).  Design metrics that act as leading indicators.  After year 1, plan to optimize and performance tune.  Move the conversation.  Avoid “Wow – it looks like our MSK program had trouble engaging our guys in the warehouses even after 3 years,… should we look into a different solution or approach?”  Prepare for, “Wow – it looks like our MSK program is having trouble engaging guys in the warehouses – what’s our plan to tackle this as we plan for year 2?”


Best Practice #3: Use ALL the data we have available in today’s analytic world.

  • Understand how social determinants of health influence engagement and utilization.  Then optimize the point solution to meet broader needs by removing barriers.  The data can show you where actions will be impactful.

Leverage solutions that package this data for you. Data that provides insights into social determinants of health can be time consuming to assemble into an analytic environment and then align to member health data. And yet it’s so powerful for insights. Your analysts time is better spent using this data as opposed to prepping it manually.

We evaluated medical and dental claims for diabetics after the introduction of a new Virtual PCP program.  The solution was selected after seeing a statistically significant difference in PCP utilization across various household income segments.  We created a specific scope around diabetics to study impacts on utilization, medication adherence, medical costs, and co-morbidities in mental health.  Not all investigation can rely solely on data.  The task force team worked with “Voice of the Member” groups, formed based on specific demographics. They focused on understanding context and color behind the numbers.  Transportation, time away from work, and caregiving themes arose in the care access category.  Other reasons were also presented, but offered less immediately actionable solutions.

With less time prepping data, the team had more time to dig deep, address quantified specific barriers, and is now measuring impact.



Check out how easy it is to include Social Determinants of Health (SDoH) factors into an analysis.


Easy to use – more time for driving change.


HDMS Enlight makes it easy to put these best practices to work.

Learn more and contact us with any questions.

Trending now

Prepare for your health analytics implementation before you buy a thing!

Avoid buyer’s remorse.

Did you ever have a home improvement project that finished late and cost more than you expected? How about a technology implementation that finished late and cost more?

You are more likely to be on-time and on-budget if your plan is thoughtful and reflects your reality. Don’t you want to have confidence knowing what you’re really getting into?

So, here are three tips to set you up for implementation success when it comes to health analytics:

  1. One-size does not fit all. It’s unlikely your implementation is the same as other organizations.  Why?  Because the culture of your organization is a huge factor.  Dig in.  What are the details behind YOUR implementation plan?

Tip!



Discuss what will be problematic or painful based on your experience and what you are moving away from. Are those complexities appropriately addressed, cared for, or resourced? Think about metric definitions and consensus, data quality, data reconciliation, matching and integration across sources, and slowly changing history.
  1. Identify what is- and is-not in your control. If something is beyond your direct control, is there a named resource and escalation path?  What risk does that pose to the project timeline based its nature.  For instance, your health analytics implementation is reliant on data from others.  How are your relationships and service level agreements with those partners and vendors?  How does that affect your plan and what’s the back-up plan?
Tip!

Before your implementation starts, refresh your knowledge of the day-to-day contacts, authorities, and any contractual SLA’s you have in place. If there will be costs associated with establishing new feeds or data interfaces, identify those early.
  1. Top down, bottom up, or an interesting mix? Think about the approach that will work better for your organization.  What process works for you – here’s my data – what can I do with it?  Or here are my objectives – what data do I need?  There are pros and cons to each but thinking about this as you prioritize is invaluable for setting internal expectations and getting the right resources lined up.
Tip!

Use phase 1 for quick wins. Standard sources generally seamlessly populate the most common views. Users feel like they get a lot out of the gate and that helps tremendously with adoption.

Remember, you’re better off with an implementation plan that’s realistic rather than one that sounds like a dream but doesn’t work well for you in the end.

Useful Documents

Enlight

Enlight is a flexible analytic platform that unlocks the power of data. It brings data to life and reveals
connections and insights so you can make being healthy more affordable, convenient, engaging, and equitable.

Read more.

Useful Documents

Read about what we do and who we serve.

Open me
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

Spotlight

Powerful predictive analytics, easy to implement and use.

Learn more about the power of HDMS Predictive Capabilities.

HDMS provides predicitive models, pre-defined segments, model scores, historical predictions and more. Bring member-level scores and cohorts into a cross-prediction analytic view.

There’s power and sophistication wrapped up in an intuitive user experience – so you can do more than look back on historical trends.


Use data to look ahead. Then take action.

HDMS clients – have your team walk you through the latest models. What do you do next? We’ll make sure your data supports your next steps and actions.

Useful Documents

How Data is Driving 2021 Benefits Strategies

Published in Fierce Healthcare
Authored by Rani Aravamudhan, Senior Clinical Consultant, HDMS


Health plans typically design benefit offerings by assessing patterns of utilization and cost data in previous years. Thanks to disruptor-in-chief, COVID-19, this traditional approach was rendered inadequate to say the least.

With the pandemic demanding agility, many health plans turned to flexible data analytics and infrastructures capable of generating original, actionable intelligence at a moment’s notice. Having the ability to respond rapidly to their clients’ immediate data needs, despite ongoing uncertainties, is much like having a fire truck ready to put out fires whenever and wherever they arise.

Health plans needed to quickly pivot business processes to align with evolving customer needs. For healthcare benefits administrator Meritain Health, the large-scale shift in the consumption of care that followed the onset of the pandemic required swift adjustments to accommodate changes in multiple areas: fluctuating sites of service and code sets and payment structures, to name a few. Hence Meritain implemented several strategic variations to their business processes.

Read more.

Trending now

Integrated Mental Health Strategies: A Right-Sized Approach

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


Member programs encouraging mental health Data Analytics for Mental Healthand wellness have increased in popularity lately among health plans and sponsors. There is a growing consensus that a happy, healthy workforce can lead to better business results. Consequently, promoting mental health strategies is considered a “win-win” proposition.

The question is, how do we quantify the wins?

Read how.