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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.

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.

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Speakers

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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.

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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.

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.

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.

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?”

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.

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

Learn more and contact us with any questions.

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.

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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?

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Let’s dig into some more details on each.

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.

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.

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.

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. 

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.

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.

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?”

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.

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?

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.

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?

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.

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.

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.

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.