Member Engagement - The Elusive Dream

In today's healthcare landscape, member engagement isn't just a buzzword—it's the primary driver of plan performance and health outcomes. Let me say that again: engaging members is the only way to get them to be healthier. Don’t believe me? Imagine a world in which members proactively reach out, asking how to improve their health, and we always have the perfect answer ready. Talk about a game-changer—one that would let us all be home in time for dinner.

But how do we get there? The challenge lies in answering just three questions:

1. Which members do we need to engage?
2. What is the best care pathway?
3. How do we get them engaged?

The good news: It’s all a data problem, and it has become increasingly solvable with the right tools. The key is combining the right data sets and transforming them into actionable engagement strategies that move the needle.

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Understanding the Engagement Crisis

The statistics paint a sobering picture. A survey by The Hartford found that 73% of employers have a workforce that underutilizes available services and benefits. Worse, a Society for Human Resource Management article highlights that less than 10% of employees fully use available benefit programs such as Employee Assistance Programs (EAPs).

Make no mistake: lower engagement isn’t saving you money— it's creating a ticking financial bomb. Health issues don't politely wait for attention; they compound, becoming more severe and exponentially more expensive with each passing day.

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Simplifying the Complex

Remember, this challenge is tractable. That doesn’t mean it’s easy—it means it’s solvable if you have the data. As outlined earlier, the three core questions remain:

1. Which members do we need to engage?
2. What is the best care pathway for them?
3. How do we get them engaged?

1. Identifying Target Members

First, some context.

High costs come from high-cost claimants.

And we’ve found it to be largely asymmetric, meaning most cost comes exclusively from high-cost claimants. We can reliably find the 20% of claimants drive 80% of claims costs. A quarter of that group (5% of the total claimants) drive 50% of claims costs. Let’s write that down:

• Cost is largely asymmetric: a small group of high-cost claimants drives the majority of expenses.
• Often, 20% of claimants drive 80% of costs. Within that 20%, a quarter—or 5% of the total population—drives 50% of overall costs.

Therefore, our goal is to predict who’s likely to be in that top 5% this year so we focus on highest reward on investment. This leaves us with three buckets:

1. Top 5%,
2. Next 15%, and
3. Bottom 80%.

This is trivial to do for last year’s plan. The challenging part is predicting next year’s cohort. How do we do that? Well, again, we took tons of data and found that:

• 2.5% of next years high-cost claimants will be this year’s high-cost claimants.
• 2.5% of them will be new high-cost claimants with almost no previous claims.

At this point, we’ve made most of our progress identifying our members. Yeehaw, congratulations! Why? Well, we now know we have to to take two actions:

1. From last year’s top 5% claimants, we draw the line in the middle and select the members that have the highest likelihood to continue to be in this bucket and the other half that don’t. We’ve found that eliminating the bottom half is way easier because most of them expire (I know, dark term) in ways we can predict: will leave the company, will leave the plan, or will no longer need medical attention.
2. The hardest part is now being able to identify the new high-cost claimants that have almost no claims history. Here’s were we have to combine primary care engagement strategies with  powerful medically trained AI to be able to take action.

2. Establish Care Pathways

At this point, let’s assume that we have a list of ~7.5% of last year’s population that we think will make up the top 5% bucket. Now for each member we conduct the following analysis:

• Pinpoint Chronic Conditions: we identify trigger conditions. We use longitudinal data to remove episodic conditions (those that happened once and rarely happen again) and focus on high cost chronic conditions,
• Surface Best Practices: for each chronic condition surface relevant literature for evidence-based condition management,
• Engage Clinical Teams: leverage nurse case managers to make a medical assessment on how to identify what would be the most likely path to recovery

3. Testing Engagement Strategies

Now, we have a list of the members we think are going to drive this year’s cost and have medical personel identify what’s their best path to recovery.

Using demographic and condition information we determine:

• Outreach Channel: Which works best—mail, email, phone call, text, or in-person outreach?
• Messaging Tone and Content: How do we partner with each individual to optimize their health journey and motivate them to take action?

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Partner or Not? Deciding on the Right Approach

Now that you’ve seen what’s possible with an analytics-driven approach, the next question is often: Should we build these capabilities in-house or partner with an established provider? Here are a few factors to weigh:

1. Data Expertise and Infrastructure
   • In-House: If you already have robust data management systems, advanced analytics skills, and the budget to invest in top-tier data science talent, building in-house could be feasible.
   • Partnering: If your resources are limited or you want a turnkey solution, a partner with proven analytics platforms can accelerate your timeline and ensure high-quality data insights.
2. Time to Value
   • In-House: Building from scratch often requires significant time and capital investment before you see results—months or even years of planning, hiring, and training.
   • Partnering: A seasoned partner can deploy existing solutions quickly, delivering faster returns and reducing risk.
3. Strategic Fit and Focus
   • In-House: Maintaining full control can be advantageous if you prefer a self-directed approach and have unique organizational requirements.
   • Partnering: Offloading the heavy lifting to a specialized vendor lets you focus on your core competencies—such as delivering excellent member experiences—while trusting experts to handle the analytics engine.
4. Scalability and Ongoing Support
   • In-House: You’ll be responsible for updates, system maintenance, and ongoing training. Growth will require continuous reinvestment in technology and talent.
   • Partnering: Providers typically roll out new features, regulatory updates, and best practices automatically, ensuring you stay ahead of industry changes without constant reinvestment.

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Key Questions to Ask Yourself

• Do we have the necessary internal resources and expertise to build a robust analytics infrastructure?
• How quickly do we need to see outcomes or ROI?
• Is data analytics a core strategic competency we want to invest heavily in, or is it more efficient to outsource?
• How flexible do we need our solution to be?
• Will an external partner bring unique insights or data sources we can’t replicate internally?

If, after honestly assessing these questions, you find that partnering can get you where you want to go faster—and with less risk—then a collaboration might be your ideal route. If you have a big enough team, a strong data culture, and the time to build from scratch, in-house might suit you just fine.

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Best Practices for Analytics-Driven Engagement

Regardless of whether you choose to build or buy, here are universal best practices for making your engagement strategy data-driven:

1. Start with Clean Data: Build a solid foundation by ensuring your data is accurate, comprehensive, and properly integrated across all sources. This means combining claims data, member demographics, and engagement metrics into a single, actionable view.
2. Segment Strategically: Use analytics to create meaningful member segments based on health status, engagement level, and communication preferences. This enables more targeted, effective outreach strategies.
3. Enable Real-Time Response: The most successful engagement programs leverage real-time analytics to trigger timely interventions. For example, identifying a missed prescription refill could prompt immediate outreach to address potential adherence issues.
4. Measure and Adapt: Regularly analyze the effectiveness of engagement initiatives and be prepared to adjust strategies based on the data. This might mean reallocating resources from underperforming programs to more successful ones.

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The Future of Analytics-Driven Engagement

As healthcare continues to evolve, the role of analytics in driving member engagement will only become more crucial. The Centers for Medicare & Medicaid Services has already signaled this shift by incorporating member engagement metrics into various quality programs.

For TPAs, the message is clear: Investing in robust analytics capabilities isn’t just about staying competitive—it's about delivering the personalized, proactive healthcare experience that members increasingly expect and employers demand.

By leveraging advanced analytics platforms and following these best practices, TPAs can transform member engagement from a persistent challenge into a powerful differentiator. The result? Healthier members, more satisfied clients, and stronger business growth.

Ready to learn more? Contact us to evaluate a partnership and see the future of member engagement in action.

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