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. 

 

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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.

 

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

Download some examples

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.

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.

#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.

Read about Enlight and contact us with any questions.

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. 

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.

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?

2. 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?

3. 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.

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.

What makes you trust someone?

Maybe they act upon facts and share these openly with you?
Even when it’s not great news?

While everyone works to meet Transparency in Coverage regulations, we see the chance for you to leap ahead. Anticipate where the market is going and offer more than traditional plan sponsor reporting – bring your plan sponsors business transparency, strategic plan performance transparency. You’ll earn their trust; you’ll be rewarded with retention.

With major industry changes, new care options, and changes in care needs, people have lots of new questions. Be the health plan that easily gives plan sponsors answers, even to hard questions.

Employers benefit because health benefit satsifaction is a contributing factor to employee retention. With the right analytics, it can be easy to find opportunities to improve, maybe by introducing additional plan options. Yes, that’s right. Design analytics that introduce the potential value of your buy-ups. With better plan performance everyone wins as health care costs lower overall.

In an industry built upon trust,

Embrace it, lead with trust

Increase plan sponsor trust with an analytic strategy that delivers better plan transparency, too.

You’ll deepen relationships, earn loyalty, and retain your customers.

How are organizations approaching SDoH in their analytics? What are they doing given the insights and measurements they find?

Here’s some specific examples of work going on within HDMS clients. You can use these projects to understand the analytic possibilities available with our SDoH capabilities. And even more importantly, see how organizations are taking action upon insights and driving innovation.

Click here to see some examples

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Score big for communities

HDMS clients – have your team walk you through available possibilities. There’s so many new options. Where will you take this next?

We’ll help tailor new analytic views to any specific needs you have.

Read more about SDoH here

How are organizations approaching SDoH in their analytics? What are they doing given the insights and measurements they find?

Here’s some specific examples of work going on within HDMS clients. You can use these projects to understand the analytic possibilities available with our SDoH capabilities. And even more importantly, see how organizations are taking action upon insights and driving innovation.

Organizations are using SDoH Analytics to tackle:

• Diversity, Equity, Inclusion initiatives
• Removing systemic or community barriers
• Prioritizing areas of investment
• Tracking progress in closing equity gaps
• Designing for people in context

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See how others are using SDoH

Here is a typical SDoH dashboard in Enlight.

Get an immediate understanding of the basics – things like care gaps by Socioeconomic index

And more focused investigations like mental health needs for disadvantaged segments.

HDMS clients – have your team walk you through available possibilities. There’s so many new options. Where will you take this next?

We’ll help tailor new analytic views to any specific needs you have.

Read about SDoH analytics

See how easy it is to look at how social determinants influence your population.

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

Member programs encouraging mental health and 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.