Risk stratification in healthcare is a foundational process used in population health management and value-based care models. It systematically organizes patients into subgroups based on their likelihood of experiencing a future negative health event, such as hospitalization or rapid disease progression. This process provides a quantitative measure—a risk score—to represent an individual’s potential health needs and future healthcare utilization. By grouping patients according to their projected risk, providers can proactively manage patient populations rather than simply reacting to sickness.
Defining Risk Stratification and its Core Goals
The central purpose of risk stratification is to ensure that limited healthcare resources are applied where they can make the greatest difference. This targeted approach focuses on predicting who is likely to become sick or have an expensive health event tomorrow, moving beyond simply identifying the sickest patients today. Health systems use this systematic sorting to design care models that match the intensity of support with the patient’s individual needs. A common stratification method segments patients into three general tiers: low, moderate, and high risk.
The low-risk tier consists of patients who are generally stable or healthy, with minimal or easily managed health concerns. The goal for this group is to keep them engaged in preventative care and maintain their current health status.
The moderate-risk tier, sometimes called “rising risk,” includes individuals with multiple risk factors who could transition into the high-risk category without intervention. This group is often targeted with preventative education and monitoring to prevent the onset of more serious conditions or complications.
The high-risk tier often includes a small percentage of the population, sometimes less than five percent, yet accounts for a disproportionately large share of total healthcare spending. These patients often have multiple complex illnesses. The goal is to manage their conditions intensively to prevent costly emergency and acute care services.
Data Sources and Modeling Used in Stratification
Assigning a patient a risk score involves the collection and analysis of vast amounts of data. Risk stratification relies on predictive modeling, which uses algorithms to look for patterns within these datasets. These models analyze a patient’s historical data to generate a numerical score that predicts the likelihood of future events, such as a hospital readmission.
One of the most comprehensive sources is the Electronic Health Record (EHR), which contains detailed clinical data. This clinical information is often supplemented by medical claims data, which provide a history of services utilized and associated costs. Pharmacy records also contribute information regarding medication adherence and the complexity of a patient’s drug regimen.
Increasingly, models incorporate data on Social Determinants of Health (SDOH), which are non-clinical factors that profoundly influence health outcomes. These factors include:
- Socioeconomic status.
- Physical environment.
- Access to transportation.
- Housing stability.
Integrating SDOH data helps create a more accurate and holistic assessment of a patient’s total risk. Healthcare systems employ statistical techniques, including machine learning models, to process these diverse data streams. The resulting risk score translates complex health and social factors into an actionable number for care teams.
Translating Risk Levels into Personalized Care
Once a patient is categorized into a risk tier, their score is translated into a specific plan of action designed to optimize their health outcomes. This defines the intensity and type of intervention the patient will receive. The actions taken differ significantly across the risk levels, reflecting the principle of matching resources to need.
For patients identified as high-risk, interventions are intensive and highly coordinated, focusing on preventing acute events and managing complex conditions. This often involves assigning a dedicated care manager to coordinate care across multiple specialists and address social barriers. High-risk individuals may also be enrolled in specialized disease management programs or receive coordinated home visits to ensure adherence to their care plan.
Patients in the moderate-risk tier receive more targeted, proactive support aimed at preventing their health from worsening. Interventions for this group include focused outreach for specific preventative screenings, such as mammograms or colonoscopies, or disease education programs. Support may also involve assistance with medication adherence or lifestyle coaching to help manage early-stage chronic conditions.
Low-risk patients, the healthiest segment, generally receive standard preventative care and less resource-intensive interventions. Their care is often maintained through automated communication, general wellness reminders, and educational materials. The goal for the low-risk group is to encourage continued engagement with the healthcare system and reinforce healthy behaviors.