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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.

Webinar

SDoH Insights and Analytics – Delayed Care

Hear Insights shared by HDMS and esteemed partner Integrated Benefits Institute (IBI) in this On-demand webinar: SDoH Insights and Analytics – Delayed Care in 2023.

This was originally presented as part of an AHIP webinar series.

What trends do we see in delayed and deferred care since the pandemic?

Let’s dig even deeper… how do social factors influence care utilization and what will that mean for costs in 2023?

What can we do about it?

View the slides




 
Webinar

Hidden Costs of Mental Health

On-demand webinar: Measuring Mental Health Costs: Insights That Will Blow Your Mind! – originally presented as part of an AHIP webinar series.

HDMS experts share a number of hot new analytic approaches that reveal additional costs of mental health.  Take a deeper look into mental health and also investigate how social determinants of health relate to care needs and costs. 

View the slides



Watch the webinar


 
Trending now

One more field can make a difference: Diversity, Equity, and Inclusion.

We’re used to looking at a lot of healthcare metrics – utilization, costs, outcomes.  Even just a little more data can tell us a lot more about people in context.

 

Check out how Plan Sponsors are surfacing measurable differences within their populations, by adding just a little more data into their analytics. 

 


DEI initiatives need vision

born out of facts.

REAL and SOGI data as well as the HDMS social determinants of health (SDoH) enrichments allow a much deeper investigation into health patterns and costs.

 

Measuring these differences allows us to take what we anecdotally see or suspect, and support it with facts.

Collegaues focused on Diversity, Equity, and Inclusion (DEI) agendas are wonderful partners. Share these insights with them.  The numbers give your organization a brilliant set of facts to help drive decisions aligned to company goals.

 

 

We’ll help you surface these insights at your organization.  Ask to hear more about the possibilities.

 


 

Join the movement.  We’ll help you get started on measuring how healthcare needs and patterns change across different subpopulations at your organization.


Get deeper DEI insights using SDoH capabilities in HDMS Enlight.

See how easy it is to look at how social determinants of health influence your population. Find where inequities exist and track progress of program efforts.

Webinar

Predictive Analytics: Drive Affordability and Better Health

Predictive models are an amazingly powerful use of data. And we have so many new reasons to use these advanced approaches to offset rising costs of care and challenging health conditions: deferred care, the increased need for mental health care, and virtual care adoption are disrupting historical patterns. How do predictive models work? Where is it best to consider using predictive analytics? What should you be doing with the results?

Watch this webinar to learn about how predictive analytics can fit into an overall analytic strategy. Invest in capabilities that allow you to act upon results, instead of sitting in reports in your inbox. Predictive analytics, when put to purpose, can be an instrumental part of a broader strategy to drive down costs and improve health. Make sure you have the big picture so you get the most from these investments.

We’ll share:

  • A framework to use to help discern where and how predictive capabilities are highly useful
  • Insights into how you can couple predictive capabilities and leading indicators
  • Approaches other health insurance providers are taking to act upon what their data is telling them

Webinar hosted by AHIP.


Speakers

Rani Aravamudhan, MBBS
Senior Clinical Consultant
Health Data & Management Solutions (HDMS)

Rani Aravamudhan joined HDMS as a Senior Clinical Consultant. She is a physician, specializing in 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.

Prior to joining HDMS, Rani worked for Philips Wellcentive as a Services Leader, where she led teams of program managers, clinical specialists and training staff along with heading their Data Governance committee. Before that, Rani was leading EMR/EHR implementations, workflow and process optimization consulting at McKesson Corporation and was their resident subject matter expert for all CMS quality reporting programs. Rani earned her medical degree at Grant Medical College in Mumbai University, India.


Keith Wilton
Vice President of Product Management
Health Data & Management Solutions (HDMS)

Keith Wilton is the Vice President of Product Management, with more than 15 years’ experience in Product Management and an emphasis on creating and deploying complex software applications. Keith joined HDMS in March 2016 after serving as Vice President of Product Management at Backstop Solutions, a leading player in the Alternative Investment space. Prior to Backstop, Keith ran product management for an arm of Morgan Stanley, and for other organizations. Keith received his B.S. degree from the University of Illinois Urbana-Champaign.

Uncategorized

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

SDoH analytics – Insights we can trust.

Using SDoH insights means we understand and trust the data we use in our analyses.

How do we do that?

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 apply what we know in a way that delineates 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.

  1. How is the data integrated?
  2. How specific and granular is the underlying data?
  3. What is the social determinant being analyzed?
  4. Can you clearly understand the definitions and data sources used for insights?
  5. How trusted is the health data itself?

Let’s dig into some more details on each.


#1 – The data model: How is the data organized and connected together?

  • 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.

#2 – Granularity: What level of detail characterizes the data sources used?

  • 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.

Here are two tips for building out a new solution:

  1. TIP: Find out the options you have around individual member address data. Ask questions about the quality and completeness of these fields. Ideally your solution will have the flexibility to use or assemble the most complete collection of member addresses possible.
  2. TIP: The best solutions offer a member-level integration to at least census block group level.  That associates people to the social and environmental factors known to a neighborhood level of insight.


#3 – Specificity: Which factor are you investigating?

  • 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.

Here are two tips for building out a new solution:

  1. TIP: Make sure your solution offers data and SDoH indices that meet broad investigative needs.  Most organizations have many questions and require multiple SDoH indices. In a discovery phase – a few options let users understand opportunities to act impactfully based upon different criteria. 
  2. TIP: Consider ways to allow analytic journeys to mature. Composite indices can be great for initial analysis. As a team starts to work on designing for a barrier or opportunity, a more specific SDoH indice will reveal important nuances or details.




HDMS offers over 25 SDoH indices and dimensions.

Start with composite indices that allow you to look broadly across a number of factors at once.  Use focused indices to support very specific or nuanced investigations, like food access or social isolation.  They can also be used together – for instance the transportation index and the technology index example we shared above.

#4 – Transparency: What are the definitions behind the numbers?

  • 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.

#5 – All the data: What’s the quality of your core health data sources?

  • 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.

One last tip:

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.



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 offers the most comprehensive out of the box SDoH analytics on the market.

Learn more and contact us with any questions.

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.

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