• Health outcomes
• Costs
• Utilization

through the lens of diversity equity, and inclusion.

Most importantly, …then what?

Head of Clinical Advisory Services

Rani Aravamudhan is a more than a physician, specializing in General Medicine.  She has 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.

Vice President of Employer Customer Experiences

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.

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?

• 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

Head of Clinical Advisory Services

Rani Aravamudhan is a more than a physician, specializing in General Medicine.  She has 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.

Head of Product Management

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 a leading player in the Alternative Investment space. Prior, Keith ran product management for an arm of Morgan Stanley, and for other organizations.

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?

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.

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.

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.

What’s a point solution?

Most firms offer programs to identify health issues and manage chronic conditions (health risk assessments, biometric screenings, and health promotion programs).

83% of large firms offer a program in at least one of these areas: smoking cessation, weight management, and behavioral or lifestyle coaching.

Source: Kaiser Family Foundation study

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.

1. TIP: 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. TIP: 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.

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?

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.

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.

You are more likely to be on-time and on-budget if your plan is thoughtful and reflects your reality. Have confidence and know what you’re really getting into!  Health data is very complex.

Here are three Best Practices for a successful
health data and analytics platform implementation:

One-size does not fit all. It’s unlikely your implementation is the same as other organizations.  Why?

Organizational culture is a HUGE factor.

Dig in.  What are the details behind YOUR implementation plan?

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.

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?

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.

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.

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.