Large Regional Health Plan Pairs Social Determinants of Health Data with Predictive & Prescriptive Analytics


  • Large regional health plan
  • Over 4.5M member lives
  • 3M+ Commercial lives (2M lives included in emerging risk analysis)
  • 500k+ Medicare lives
  • Coverage across 40+ U.S. states


Like the vast majority of health plans today, this large, regional healthcare insurer sought to improve care management outcomes by creating improvements across member engagement, health and cost outcomes. Their current approach provided insight into members in need of immediate care management, but lacked the ability to proactively identify emerging early chronic members.

Furthermore, the health plan had a large strategic initiative to design, build and deliver a more integrated model of care delivery. As a part of this new model, they aimed to create a shift in care management focus from highly reactive care management to a more proactive approach that encompassed managing members across the risk spectrum, including those who are yet to become high risk.

This progressive way of delivering care to populations required a new way of looking at care management. It also required a need to deeply understand members, populations and performance in order to appropriately equip their regional-based multi-disciplinary care teams. They not only aimed to create care management efficiencies, but in a novel way, understand how to effectively engage with members in order truly impact health outcomes.


In order to gain the deep insights needed for care transformation, the health plan looked to expand their current partnership with EXL. EXL’s experience and expertise in end-to-end data management and ability to deliver actionable and operational insights derived from proprietary, predictive and prescriptive analytics was exactly what the health plan needed to catapult into the next generation of care management.

EXL’s dedicated team of clinical, analytics and health plan domain experts delivered on the client need to:

  • Identify members who may become high risk members within the next 2-3 years
  • Prioritize care to achieve maximum efficiency and outcomes from care management teams
  • Suggest an intervention approach to mitigate against undesired health outcomes and increased costs


Projecting Membership Risk

As part of the strategic direction of the plan, they sought to bring in additional layers of member insights in order to adequately equip each regional care manager for appropriate intervention and engagement. EXL identified key drivers or barriers for improving outcomes based on SDoH factors, such as ethnicity, companionship, transportation, access to care, financial stability, education, etc. This analysis paired with prescriptive analytics allowed EXL to deliver a targeted population segment that was both Impactable and Intervenable – a member’s willingness and ability to comply with prescribed interventions.

Figure 2 suggests there is a greater savings opportunity for the final cohort (Impactable and Intervenable members with emerging risk) compared with any other segment of the population.

EXL identified the possible communication channels at a member level based on lifestyle and behavior factors, such as mail order preference, online/mail shopping preference, opt-out of mail solicitation, etc. By aligning preferred communication channels and recommended interventions based on the member’s clinical and social factors, the health plan can make data driven decisions around where and how to best focus care management resources.


The health plan is embarking on their journey to operationalize the insights provided by EXL. As they integrate the analytics into their current and new regional care management programs, they expect to see significant improvements across

Membership Insights

Informing what members truly need and how they need that care based on known social and clinical factors

Care Management Efficiencies and Effectiveness

Focusing on the right members most likely to engage and improve health outcomes

Risk and Cost Management

Moving down the risk funnel to mitigate potential future risk/costs

Member Engagement & Health Outcomes

Creating far deeper member views inform effective intervention and engagement strategies

Proactive Care Management

That addresses varying levels of member needs

EXL will measure and monitor defined leading indicators on a monthly basis to ensure the right cohort is selected (traditional ROI analysis typically requires at least 12 – 18 months data post program participation). Those leading indicators include:

  • Risk
  • Inflation adjusted PMPM
  • Utilization (PCP/Specialist, avoidable IP, avoidable ER, readmission, etc.)
  • Measure compliance
  • etc.

By comparing the actual outcome to the expected outcome, the program efficiencies and effectiveness can be measured continuously before performing the traditional ROI analysis.

One of the key priorities set by the health plan was the need to understand the barriers to care compliance faced by their members. It was imperative to success to be able to view social determinants of health across their membership in order to break down these barriers and effectively intervene.

As the care team deploys the suggested methods for care gap closure and member outreach, results will be shared with EXL, and EXL will then create a feedback loop to further enhance the predictive model on an ongoing basis. EXL will also estimate ROI for this offering with the health plan through the partnership.

Written by EXL Health Team

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