May 9, 2025
The healthcare industry is undergoing a seismic shift from traditional fee-for-service models to value-based care (VBC). This transformation prioritizes patient outcomes over service volume, compelling providers and healthcare systems to reimagine how they manage patient populations. At the core of this shift is patient segmentation — a powerful strategy that enables healthcare organizations to optimize care delivery, improve patient outcomes, and drive down patient costs. By grouping patients based on key factors such as chronic conditions, risk levels, and healthcare utilization patterns, providers can implement targeted interventions that proactively address patient needs and improve the overall efficiency of care.
Effective patient segmentation allows healthcare providers to implement precision-driven interventions tailored to distinct patient groups. By categorizing patients based on shared characteristics, providers can enhance risk stratification, allocate resources more efficiently and effectively, and implement proactive care management strategies. For example, if a significant portion of a high-risk patient population has diabetes, organizations can focus on deploying robust diabetes management programs, including remote monitoring and lifestyle coaching, to curb disease progression and avoid future costly complications.
Healthcare organizations utilize various segmentation approaches to better understand and manage their patient populations. The method of grouping patients may vary based on the targeted cohort, planned interventions, resources available to the provider, etc.
Below are some of the most widely used methods in value-based care, which we will be digging into more detail in upcoming posts.
Patients with chronic conditions such as diabetes, heart disease, or obesity require specialized management strategies to prevent complications and hospitalizations. Grouping patients by condition enables providers to implement disease-specific interventions that improve adherence to treatment plans and enhance overall patient well-being. Some benefits to condition-based segmentation include the ability to tailor disease management programs to target the condition and population more effectively, which leads to better patient compliance.
Example: A diabetes management program that incorporates continuous glucose monitoring, lifestyle coaching, and digital health interventions can help stabilize blood sugar levels and reduce hospital visits.
Risk-based segmentation involves categorizing patients based on their likelihood of incurring high healthcare costs. Providers leverage tools like Hierarchical Condition Category (HCC) scoring (CMS v28, HHS HCC) and predictive analytics to stratify patients into high-risk, moderate-risk, and low-risk categories. Risk-based grouping methodology can be tailored to a unique provider’s population by prioritizing specific metrics, such as diabetes, pulmonary or heart conditions, and readmissions, to name a few.
Example: Patients with multiple chronic conditions and high medication utilization may be placed in an intensive care management program that includes regular check-ins with care coordinators and remote monitoring to prevent exacerbations.
This approach groups patients based on their healthcare usage patterns, such as frequent ER visits, high medication usage, repeated inpatient stays, or readmissions. Understanding these patterns helps healthcare organizations identify opportunities to reduce avoidable costs and direct care toward lower-cost settings. This strategy helps to reduce unnecessary ER and hospital visits and lower readmission rates, which support cost-efficient care delivery.
Example: Redirecting frequent ER users to urgent care or telemedicine options can lower costs and provide more appropriate care settings. Similarly, analyzing disease prevalence in high-utilization cohorts can uncover trends requiring targeted population health initiatives. Providing alternatives in care delivery to patients also improves patient satisfaction.
Social determinants of health (SDOH) play a critical role in health outcomes. Factors like income, transportation, food security, and housing stability significantly impacts a patient’s ability to manage their health. Segmenting patients based on these determinants allows healthcare organizations to design interventions that address non-clinical barriers to care. By enhancing health equity by addressing social barriers, providers can reduce disparities in healthcare access and outcomes.
Example: Providing transportation services for patients who struggle to attend appointments or connecting individuals living in food deserts with local nutrition assistance programs can significantly improve adherence to care plans and overall health.
For self-insured employers, segmenting patients based on workforce health trends, job types, and regional differences allow for customized benefits and wellness programs. Understanding the unique health risks within different workforce segments can drive better health outcomes and lower healthcare spending. An employer can enable more effective workplace wellness programs, which helps control their healthcare plan spending by addressing their unique workforce-specific health risks.
Example: Employers with a large employee population of obesity could make benefits available that employees can utilize, such as fitness or nutritional counseling.
Advanced data analytics and machine learning are transforming patient segmentation by enabling real-time risk stratification and predictive modeling. Healthcare organizations can refine their segmentation strategies by integrating claims data, electronic health records (EHRs), and social determinants of health insights. Self-insured employer plans can benefit from monitoring claims data in collaboration with third-party administrators (TPAs) to identify emerging trends and optimize benefit design.
Once patient groups are effectively segmented, the next step is implementing targeted interventions that align with each segment’s unique needs. Key strategies include:
Ongoing evaluation and refinement of segmentation strategies are essential to ensure effectiveness. Regular data analysis, patient feedback, and adaptation to emerging trends enable organizations to optimize their approaches continuously. There is much more to uncover — watch for our next posts as we break it all down.
Patient segmentation is a fundamental component of successful value-based care. By strategically grouping patients based on clinical conditions, risk levels, utilization patterns, socioeconomic factors, and employer-specific considerations, healthcare organizations can deploy targeted interventions that enhance outcomes and reduce costs. As the healthcare landscape continues to evolve, leveraging advanced analytics and collaboration with value-based care partners will be critical in refining segmentation models and driving smarter healthcare spending. Investing in a data-driven, patient-centered approach will not only improve efficiency but also ensure better care for every patient population.
