It has long been understood that a relatively small portion of any covered population accounts for a disproportionately large share of healthcare spending. A 2025 study by the Employee Benefit Research Institute (EBRI), which examined 13.1 million individuals with employer-sponsored health coverage in 2022, found that the highest-cost 20% of members were responsible for 85% of total annual plan spending, while the top 10% alone accounted for 86%. For health plans and plan sponsors, these high-cost members also referred as high-cost claimants can introduce substantial unpredictability across both medical and pharmacy spend, often reflecting the impact of complex chronic conditions such as cancer and cardiovascular disease, as well as acute, high-severity episodes including neonatal and complex obstetric care. This cost concentration isn’t just an actuarial concept. It shows up consistently in national reporting. For example, AHRQ’s Medical Expenditure Panel Survey (MEPS) analysis for 2018–2022 finds that 5% of the population accounts for nearly half of healthcare spending in a given year, and that ambulatory care, inpatient stays, and prescribed medicines each represent material shares of spending for the highest-cost members.1 As costs continue to rise, payers are under increasing pressure to translate this reality into action: earlier identification, better care management, smarter benefit design, and clearer accountability for outcomes.
In this article, we outline what are the main drivers of high-cost claims, why forecasting high-cost claimants is challenging, what payers can do differently to identify rising risk earlier, and how analytics can translate data into action.
What are the main drivers of high-cost claims?
High-cost claims are most often driven by a combination of complex clinical needs, high-intensity sites of care, and rapidly evolving specialty drug therapies, making it essential to view medical and pharmacy utilization together. The 2026 Milliman Medical Index reports that healthcare costs for the average person rose 7.9% in 2026 to $8,460, the highest annual increase in more than a decade, excluding pandemic-era fluctuations. Outpatient facility care and pharmacy were the biggest contributors to that increase: pharmacy costs rose 14.8% and outpatient facility care rose 7.5%, together accounting for 69% of the year-over-year cost growth. In light of these spending patterns, the presence of comorbidities, or multiple coexisting conditions, can be a key predictor of both healthcare utilization and future cost trajectories among high-cost and rising-risk members.
To manage these cost drivers more effectively, payers must shift from simply describing past spend to anticipating future risk. Predictive analytics and condition-based insights can help identify which members are most likely to become high-cost claimants, as well as the factors contributing to that risk, enabling care management teams to intervene earlier and with greater precision. Comorbidities are particularly important in understanding total cost of care because they often shape utilization patterns, treatment complexity, and the likelihood of avoidable complications. However, turning comorbidity signals into actionable insight can be challenging without the right analytic framework, and many organizations still struggle to convert this data into reliable forecasts and targeted interventions.
Why is predicting high-cost plan members difficult?
Identifying health plan members at risk of generating high-cost claims is challenging because healthcare spending is inherently variable and influenced by a wide range of factors that shape an individual’s health status and future care needs. For example, one common driver of high-cost claims is cancer, which is difficult to predict. Cancer diagnoses may appear suddenly, and costs can escalate rapidly once treatment begins.
At the same time, health plans and plan sponsors are demanding greater transparency into total cost of care, including what is driving it, which factors can be influenced, and how quickly results can be measured. For high-cost members, health plan teams are increasingly focused on questions such as:
- Which members are most likely to become high-cost claimants?
- Which current high-cost claimants are likely to remain high-cost over time?
- How can an employer group assess whether the costs and trends associated with its high-cost claimants are reasonable relative to benchmarks?
- How much stop-loss coverage is appropriate?
- Where is spending occurring across the care continuum, such as inpatient services or pharmacy?
- What policies, practices, and interventions can help mitigate these costs?
- Which interventions are most effective in preventing high-cost claims from escalating over time?
How can analytics help identify high-cost members?
Effectively identifying and managing high-cost and rising-risk members requires more than retrospective reporting. Payers need analytics that can stratify risk, explain cost drivers, and target interventions where they will have the most impact on total cost of care (TCOC). By combining claims history with condition grouping, utilization patterns, and emerging risk indicators, payers can better distinguish between one-time catastrophic events and members likely to remain high-cost (or become high-cost) without timely intervention.
With this level of insight, payers can help plan sponsors understand how chronic and acute conditions affect their populations, and they can prioritize interventions that reduce avoidable utilization and improve outcomes. While unexpected health events will always occur, many high-cost trajectories include signals months in advance. Earlier detection, paired with timely outreach, appropriate site-of-care steering, and coordinated treatment plans, can reduce downstream complications and unnecessary spend.
How can Milliman MedInsight (MedInsight) help enabling better care for high-cost members?
The MedInsight Payer Platform helps health plans take a more proactive approach to supporting high-cost and rising-risk members by transforming complex data into actionable insight. By bringing together cost, utilization, and condition burden in a unified view, the platform enables health plan teams to prioritize outreach, better coordinate case and utilization management, and target network and vendor programs toward the members and services driving TCOC.
Within the platform, Employer Group Insights (EGI) makes it easier to pinpoint what’s driving high-cost claimant spend and translate those findings into employer-ready reporting and action. With EGI, health plans can:
- Flag members who exceed defined spend thresholds (e.g., $50K/$100K) to guide stop-loss planning and trigger targeted care-management outreach.
- Track how many high-cost claimants are new year over year to quantify volatility and the impact of membership change.
- Separate new vs. recurring high-cost claimants to understand persistence, root causes, and intervention opportunities.
- Surface current risk drivers and next-year projections, including the conditions most associated with high-cost trajectories.
- Measure out-of-network “leakage” to pinpoint network gaps and prioritize steerage and contracting actions.
- Quantify comorbidity burden to sharpen segmentation and tailor program design.
MedInsight also supports comorbidity-driven segmentation using Chronic Condition Hierarchical Groups (CCHGs), which can track up to six co-occurring conditions. This helps teams identify high-cost patterns that a single primary diagnosis can miss. For example, differentiating members with diabetes plus severe depression from members with diabetes alone.
In addition, Milliman Advanced Risk Adjusters (MARA) stratifies members by clinical risk and expected resource use, helping care management teams evaluate member risk based on clinical complexity and expected resources needs to support more targeted outreach. Together, our payer solutions help health plans target the right interventions sooner and measure results with consistent reporting.
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