And what these mean for Employers
What are some key trends impacting the business of healthcare and what can this mean for you, as a self-funded plan sponsor?
- costs are rising
- the health system itself is evolving
- DEI, SDoH and artificial intelligence invite new opportunities
In two words – EXPENSIVE + CHANGE.
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. All with optimal outcomes.
Offset increases in costs by optimizing plan performance.
Eliminate guesswork. Use health data to get the most out of partners and programs.
Create a data-driven program and repeatable model that lets you pilot new strategies or programs and compare measured results.
For example, pilot and analyze: 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.
Health care is changing quickly.
Read about the top 5 trends in health care and the analytics you can use to navigate plan design with confidence.Read Now
Connecting it all
Reverse delayed care habits
Read about key trends and what this means for employers.Connecting trends to benefits strategies
SDoH analytics requires a lot of data, and different types of data. Claims data tells us about health care visits. Digital device data tells us about daily health. And special data sets let us delineate the social and environmental factors that could influence each member.
In SDoH analytics, we understand each person as an individual and in context, but look at a community as a whole in aggregate, to see what trends and patterns emerge.
Here are 5 important aspects to consider, and tips of what to look for, so you can trust the insights in your SDoH analytic endeavors.
Wellness means care and lifestyle choices. This data is scattered across many different places. Health analytics must integrate complex claims data structures and lifestyle data at an individual person level. SDoH analytics should also be connected at a person level. This way, the data is ready to serve all the analytic questions you may ask, without additional data preparation and delays.
The more granular a data set is, and assuming it is associated at a member-specific level, the more trustworthy and usable your SDoH insights will be. Think about the variation of social and environmental factors you see across an entire zip code. Now think about the degree of variation you see within a neighborhood. A Census Block Group is akin to a neighborhood. This means if you have source data that has a Census Block Group level of granularity, you are seeing only the degree of variation across neighborhoods, not entire zip codes.
Social and environmental factors cover a broad range of influences on health. Air quality or water quality? Economic hardship or transportation access? There is so much we can do if we have lots of different SDoH indices to choose from. For instance, one HDMS client is looing at the transporation index alongside the technology index to assess the potential usefulness and impact of a mobile unit verse a virtual solution for specific care services. Locations with low transporation AND low technology indices are prioritized for mobile services, while other locations are suitable for virtual care alternatives.
Have a good understanding of which social or environmental factor you are investigating and where that index is sourced. There are a wide variety of options. Nothing will be perfect. Some indices are more complete, more granular, more recently or frequently updated, than others. As you interpret results, have transparency around the process leading to the metrics. This will help everyone interpret and apply insights better in the long run.
TIP: Enriching claim data delivers fast and intuitive investigations. This makes SDoH analytics easier too.
Enrichment can have many forms: classify claims by episode treatment groups (ETG), apply pharmaceutical classifications, and flag specialty druges. Enrichment processing also identifies gaps in care and low value care and makes it easy to surface these individual moments into analytics.
ER visits that have been classified using the NYU methodology allow you to quickly look at who visited the ER for non-emergent care, just by using a few filters. Now think how powerful it is to further see these visits by income index.
As we think about integrating new data to investigate social determinants of health, we naturally focus on the new data – the addition. But we need to link that to core health data. Let’s not forget the quality and usability of those systems or sources. The data quality processes surrounding your traditional analytics are a critical part of trusted SDoH insights.
as part of health and wellness benefits.
Point solutions have been a great way to enhance benefits and provide care for a targeted need.
But as point solution costs add up, the pressure increases to understand, and sometimes PROVE, the value.
Most firms offer programs to 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
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’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.
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?
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.
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?”
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.
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.
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.
Just read below – we’ve copied the article to this page.Read on HR.com
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.
Take a close look at how social determinants influence costs and utilization for your population.
How equitably do plans and networks meet diverse population needs?
Predictive models help you anticipate costs and identify ideal targets for specific actions.
See the value and impact of point solutions. How do they affect total costs and overall health?
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 – we’ve copied the article to this page.Read on BenefitsPro
Head of Clinical Advisory Services at HDMS.
Dr. Rani is a general medicine physician who cares for individuals yet connects experiences to population health perspectives using 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 build context and scale across HDMS clients.
Why rely solely on metrics like number of enrolled? Don’t you want to quantify health outcomes?
Are you successfully engaging individuals who would most benefit? Or are only those who are already highly engaged in their health?
Achieving maximum ROI from wellness programs comes from changing behaviors. Who is most at-risk for adverse health events and consequently would benefit the most from an initiative? How can data help?
Read this article from BenefitsPro. It shares how HDMS recommends employers approach this – we’ve copied the article below.Read on BenefitsPro
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.
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.).
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.
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.
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.
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.
Dr. Rani is Head of Clinical Advisory Services 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.
Why rely solely on metrics like number of enrolled? Don’t you want to quantify health outcomes?
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.
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.
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.
Use data to design the right health and wellness programs for YOUR population.
Happy, healthy employees – the building blocks for success and long term loyalty.
No guess work or finger crossing.
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.
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.
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
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.