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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 what’s really happening.

 

Check out how a few Plan Sponsors were able to surface measurable differences within their populations, by adding just a little more data into their analytics.

 

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’d love to help you surface these insights at your organization.  Just ask and we can share 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.


 

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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.

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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.

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Beyond Transparency in Coverage - Embrace plan sponsors with strategic analytics

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.


Powerful plan sponsor analytics. Go beyond reporting.

What could controlled plan sponsor plan performance transparency look like?

Self-service analytic front-ends are what people want, to explore data. But the secret is the data itself. If you want to focus on using data for plan performance improvements, your analytic views will naturally be very specific to your business.

Take a peek at HDMS Enlight™. Imagine plan sponsors with access to data and analytics you choose or design. See teams working side by side with accounts, helping them to optimize and get the most out of your thoughtfully designed plans and networks.

Customers will love you for it.

Trust us.

(yet verify – it’s ok, we would too.)

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Social Determinants of Health – Analytics

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


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

Seven SDoH Indices, ready for you.

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Social Determinants of Health – Analytics

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


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

Seven SDoH Indices, ready for you.

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Integrated Mental Health Strategies: A Right-Sized Approach

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


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

The question is, how do we quantify the wins?

Read how.

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Improving Plan Performance by Measuring Diversity, Equity, and Inclusion.

Numbers talk. They are a powerful language in business.

 

These numbers let us think differently and specifically – design strategies and plans that improve health engagement and drive down costs.

With digital transformation improving health experiences, we now have even more Big Data in healthcare to use for deeper insights in population health.

When it comes to health analytics and DEI efforts, what data do you need? How do you get it? How can you get started? What happens next?

 

We’re so glad you asked – hear from our experts.

 

 

Watch this webinar to learn about ways you can actively measure health outcomes, costs, and utilization through the lens of diversity equity, and inclusion.  Most importantly, then what?  Come listen to what some plan sponsor companies are doing and dig into the perspectives of a health plan…

 

Download the slides

Webinar hosted by AHIP.

 

 

 

The Speakers

Rani Aravamudhan

Rani Aravamudhan, MBBS

Head of Clinical Advisory Services
Health Data & Management Solutions (HDMS)

Dr. Rani Aravamudhan leads HDMS Clinical Advisory services. She is a general medicine physician who cares for individuals yet connects experiences to population health perspectives using her 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 gather context and scale across the HDMS client base.

Jason Elliott

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.

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Use Cases for Predictive Analytics in Healthcare

Predict, then act!

Companies are using predictive analytics to get the right people to the care they need.

Think about it - predictions aren't truely valueable unless we act upon them to either avoid a negative prediction, or accelerate a positive prediction.

Read these 3 use case examples to help inspire new ideas about how your organization can be using predictive analytics.

 

What could you do differently to help members be on a path to better health?

 

Download use cases and ideas for intervention strategies

Want help finding rising risks?

Use Predictive Analytics to look ahead