Risk Management: Why Biomarkers + AI Change Who Becomes High-Cost
Health insurance has always depended on predicting risk. Every premium, every care management program, and every network decision ultimately comes back to one question: who is likely to become expensive, and when?
Most models answer that question using history. They look at prior claims, diagnoses, prescriptions, age, and a handful of clinical indicators. These inputs are useful, but they share the same limitation. They describe what has already happened, not what is about to happen.
That gap matters because cost in healthcare is not evenly distributed. A relatively small portion of members accounts for a disproportionately large share of total spend. The challenge is not identifying those members after they become high-cost. The challenge is identifying them before they cross that threshold.
What risk stratification is supposed to do
Risk stratification is the process of grouping members into segments based on their likelihood of generating future healthcare costs. In practice, insurers create tiers such as low, rising, and high risk, and then allocate resources accordingly. Care management teams focus on high-risk individuals, preventive programs target moderate-risk groups, and low-risk members receive minimal intervention.
When this system works well, resources are deployed efficiently and outcomes improve. When it fails, the consequences are significant. High-risk members are identified too late, interventions are less effective, and costs escalate rapidly.
The core issue is not the concept of stratification itself. It is the timing and quality of the data used to perform it.
Why traditional segmentation falls short
Traditional models rely heavily on claims data and episodic clinical inputs. Claims data is inherently lagging because it only exists after a healthcare event has occurred. Diagnoses, by definition, reflect conditions that have already progressed to a clinically recognisable stage.
This creates a blind spot during the most important phase of disease progression, when physiological changes are occurring but have not yet resulted in symptoms or treatment.
A member can appear healthy in claims data while underlying risk is increasing. By the time that risk becomes visible, the window for low-cost intervention has often closed.
This is why insurers frequently observe members moving abruptly from low or moderate risk into high-cost categories. The transition appears sudden in the data, but in reality, it has been developing over months or years without being detected.
The role of biomarkers in closing the gap
Biomarkers introduce a fundamentally different type of signal. Instead of waiting for clinical events, biomarkers capture continuous physiological change. They reflect how the body is functioning in real time, not just when something goes wrong.
Changes in metabolic health, inflammation levels, cardiovascular stress, hormonal balance, and organ function can all be detected through biomarker data long before a diagnosis is made. These signals are often subtle on their own, but when tracked over time, they reveal clear trajectories.
A member whose biomarker profile shows gradually worsening insulin sensitivity, rising inflammatory markers, and changes in lipid levels is not simply “low risk.” They are on a path toward becoming high-cost, even if no claim has been filed.
This is the critical difference. Biomarkers shift risk detection from reactive to anticipatory.
Why AI is the missing piece
Biomarker data on its own is powerful, but its full value emerges when it is analysed at scale. This is where artificial intelligence becomes essential.
Human interpretation struggles with complex, multi-dimensional data that evolves over time. AI systems, by contrast, are designed to identify patterns across large datasets and detect relationships that are not immediately obvious.
When biomarker data is combined with AI, several capabilities emerge. Patterns across multiple physiological systems can be analysed simultaneously, allowing for a more holistic understanding of health. Longitudinal trends can be evaluated to distinguish between temporary fluctuations and sustained changes. Most importantly, predictive models can be trained to identify combinations of signals that historically precede high-cost outcomes.
This transforms risk stratification from a static classification into a dynamic, continuously updating system.
Predicting who becomes high-cost
The concept of a “high-cost member” is well understood in insurance. These are individuals who generate significant healthcare spend due to chronic conditions, acute events, or a combination of both.
What is less understood is that most high-cost cases follow identifiable pathways. Chronic diseases such as diabetes, cardiovascular disease, and kidney disease do not appear overnight. They develop through a series of physiological changes that can be measured if the right data is available.
Identifying risk before diagnosis
With biomarker-driven models, these pathways become visible earlier. A member who is trending toward metabolic dysfunction can be identified before a diabetes diagnosis. A member showing early signs of cardiovascular stress can be flagged before an acute event occurs. A member with declining organ function can be monitored before hospitalisation becomes necessary.
The ability to predict who becomes high-cost is therefore not based on a single indicator. It is based on recognising patterns across time, across systems, and across populations.
Why this matters for segmentation
When risk can be detected earlier, segmentation becomes more precise. Instead of broad categories that rely on past behaviour, insurers can create more granular segments based on future risk trajectories.
Members can be grouped not only by current status but by the direction in which their health is moving. This allows interventions to be targeted more effectively. Resources can be allocated to those who are most likely to benefit, rather than those who have already progressed to high-cost status.
This also reduces the inefficiency of over-intervening in low-risk populations while under-serving those who are quietly transitioning into higher-risk categories.
For large insurers such as UnitedHealth Group, Cigna, and CVS Health, even small improvements in segmentation accuracy can translate into significant financial impact when applied across millions of members.
The compounding effect on outcomes and cost
Early and accurate risk stratification has a compounding effect. When members are identified earlier, interventions are more effective. When interventions are more effective, progression to high-cost conditions is reduced. When fewer members become high-cost, overall claims expenditure decreases.
This creates a feedback loop where better data leads to better decisions, which in turn lead to better outcomes and lower costs.
For organisations such as Elevance Health and Humana, which manage complex populations with varying levels of risk, this compounding effect can be one of the most powerful levers for improving both clinical and financial performance.
From static models to living systems
The future of risk stratification is not a better version of existing models. It is a different type of system altogether.
Instead of static classifications updated periodically, insurers will move toward living models that continuously ingest new data and update risk profiles in real time. Biomarkers provide the signal, AI provides the interpretation, and together they create a system that evolves alongside the member.
This approach aligns more closely with how risk actually behaves. Health is not static, and neither is risk. It changes over time, influenced by behaviour, environment, and physiology.
A system that can track and interpret those changes in real time is fundamentally more aligned with the underlying reality.
The shift that is already underway
Across industries, the move from reactive to predictive systems has already taken place. In finance, fraud detection is driven by real-time signals. In manufacturing, predictive maintenance prevents equipment failure before it occurs. In logistics, route optimisation adapts continuously to changing conditions.
Healthcare is now following the same path. The combination of biomarker data and AI represents the next step in that evolution. It allows insurers to move beyond describing risk and begin shaping it.
Times are Changing
Risk stratification has always been central to insurance, but the tools used to perform it are changing. The question is no longer whether insurers can identify high-cost members. They already can. The question is whether they can identify them early enough to change the outcome. That is where biomarker data and artificial intelligence become powerful. Not because they add more data, but because they change the timing of insight. And in healthcare, timing is everything.
