Patient segmentation in cardiology helps hospitals and clinics group patients based on health risks, behavior, and outcomes.
This allows care teams to provide better, faster, and more personalized treatment.
With the rise of chronic heart diseases and growing data from EHRs and diagnostic tools, segmentation is now more important than ever.
What is patient segmentation in Cardiology?
Patient segmentation in cardiology categorizes individuals into subgroups based on clinical characteristics such as age, comorbidities, genetic markers, and severity of cardiovascular disease.
This process helps tailor treatments, optimize patient outcomes, reduce hospital readmissions, and support preventive care strategies by matching interventions to specific risk profiles.
What are the types of patient segmentation analysis?
1. Risk-Based Segmentation
Patients are grouped based on their clinical risk of developing a disease or complications. This includes risk scores, comorbidities, lifestyle factors, or history of cardiac events (like heart attack or stroke).
How it helps:
This type is key in cardiology to prioritize care for high-risk patients, such as those with congestive heart failure or a history of arrhythmias. It allows doctors to give more attention to patients who are most likely to need urgent care or hospitalization. Risk segmentation also supports preventive efforts for high-risk groups by identifying early warning signs.
2. Demographic Segmentation
Patients are segmented based on demographic attributes like age, gender, ethnicity, income level, education, and location.
How it helps:
Demographic traits often influence disease risk, treatment response, and health-seeking behavior. For example, older male patients may be more prone to heart disease. Segmenting by region also helps tailor outreach in areas with higher cardiac case rates. This type is useful for designing community health interventions and identifying underserved groups.
3. Behavioral Segmentation
Patients are grouped based on how they interact with healthcare systems. This includes:
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Frequency of visits
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Type of services used (preventive, emergency, chronic)
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Treatment adherence
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Drop-off rates after initial visits
How it helps:
Behavioral segmentation helps identify patients who need more engagement. For instance, patients who only come in during emergencies may miss routine cardiac screenings. This segmentation also supports re-engagement strategies, loyalty programs, or digital follow-up tools to keep patients on track with medications and appointments.
4. Psychographic Segmentation
This type classifies patients based on lifestyle, values, personality traits, attitudes toward healthcare, motivation, and preferences.
How it helps:
Some patients are proactive and seek regular care; others avoid it until emergencies. Understanding these attitudes allows for targeted communication. For example, younger cardiac patients who are tech-savvy may respond better to app-based coaching, while older patients may prefer phone calls. Psychographic segmentation is useful for improving adherence, education, and patient experience.
5. Disease or Condition-Based Segmentation
Patients are grouped by specific diagnoses, such as:
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Coronary artery disease
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Atrial fibrillation
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Hypertension
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Diabetes with cardiac complications
How it helps:
This is the most common type in specialty care like cardiology. It allows for disease-specific treatment plans, medication protocols, and specialist referrals. It also supports clinical research, drug targeting, and bundled care models for each condition.
6. Outcome-Driven Segmentation
Patients are segmented based on their treatment results, such as:
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Recovered fully
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Developed complications
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Required surgery
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Had readmissions or ER visits
How it helps:
This analysis helps healthcare providers evaluate which treatment plans work best for which patient types. It also helps pharma companies learn how patients respond to medications over time. Segmenting by outcome enables performance benchmarking and supports value-based care models where payment is tied to results.
7. Data-Driven Segmentation (AI/Machine Learning)
This approach uses algorithms like k-means clustering, neural networks, and decision trees to automatically group patients based on multiple variables: lab results, EHR data, lifestyle, outcomes, and more.
How it helps:
AI reveals hidden patterns that doctors may not notice. For example, a machine-learning model may find a cluster of patients with borderline glucose and mild hypertension who later develop heart disease. This allows care teams and pharma companies to build proactive intervention plans. It supports predictive modeling, early diagnostics, and drug response prediction.
Common Patient Segmentation Strategies used in Cardiology
1. Risk-Based Clustering Using AI and Clinical Markers
This strategy uses machine learning to segment cardiac patients based on lab test results and risk indicators. Algorithms like k-means clustering, PCA, or UMAP process large datasets of biomarkers such as troponin levels, glucose, CK-MB, heart rate, and even patient demographics (age, gender).
The model finds patterns and forms clusters—each with different risk levels for cardiac events.
How it helps:
This method identifies at-risk patients who don’t show obvious symptoms. For example, someone with slightly elevated troponin and glucose may belong to a high-risk cluster, even if they feel fine. Early detection means early action reducing ER visits, hospital stays, and mortality. It's especially useful in large hospitals or health systems managing thousands of patient records.
2. CRM-Based Segmentation Using EHR Integration
This approach connects a healthcare CRM platform with the hospital’s EHR system. The combined data enables real-time segmentation based on diagnosis (e.g., coronary artery disease, arrhythmia), treatment history, appointment frequency, demographics, and follow-up needs. Patients can be grouped into healthy, rising-risk, and high-risk segments.
How it helps:
With CRM automation, cardiology teams can automatically send reminders for checkups, manage post-surgery follow-ups, and offer customized health education. For example, a patient with chronic heart failure will receive timely care instructions and alerts.
This not only improves care quality but also reduces no-shows, boosts engagement, and keeps chronic cases under control.
3. Behavioral and Service Usage Segmentation
This strategy focuses on how patients use healthcare services. It analyzes appointment frequency, care type (acute vs chronic), and service drop-off patterns. Patients are segmented into groups like:
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Rare users
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Acute care–only users
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Regular chronic care users
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High-spenders (loyal patients)
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Drop-offs after a single visit
How it helps:
This segmentation gives insight into patient behavior. For example, if many cardiac patients only visit once during an emergency, that signals a gap in long-term care engagement.
Providers can then re-target those patients with check-in calls or wellness offers. Loyal, high-spending patients can receive personalized benefits to maintain trust and satisfaction.
4. Segmentation by Social Determinants of Health (SDOH)
This method uses social and environmental data—such as income level, transportation access, neighborhood health trends, education, or living conditions—to divide patients into groups. For example, GIS software can map heart disease hotspots in underserved communities.
How it helps:
Many cardiac patients skip care not because of clinical issues but due to social barriers. By identifying these segments, hospitals can offer telehealth, transport support, or community care packages. This improves equity in cardiac care and ensures no patient is missed due to non-medical issues. It’s a strong strategy for value-based care systems.
5. Imaging-Based and Outcome-Driven Segmentation
This advanced strategy segments patients using cardiac imaging data (MRI or CT scans) combined with machine learning models. Tools like LA‑CaRe‑CNN segment left atrial scars in atrial fibrillation patients. Other tools like MultiFlowSeg analyze flow data to group congenital heart disease patients based on exercise tolerance, oxygen levels, or cardiac workload.
How it helps:
This strategy supports personalized surgery planning and targeted therapies. For instance, patients with similar flow restriction patterns may respond better to the same intervention. It also links visual data with long-term outcomes, helping specialists design more accurate care plans and measure results more effectively.
How this impact the pharmaceutical industry
1. Smarter Drug Development and Clinical Trials
Pharma companies can use cardiology segmentation data to recruit the right patients for clinical trials based on disease stage, biomarkers, comorbidities, or social factors. This improves trial efficiency, reduces dropout rates, and leads to more accurate drug efficacy data.
Impact:
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Faster and more targeted clinical trials
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Improved drug safety and effectiveness
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Reduced trial costs
2. Personalized Medicine and Targeted Therapies
With clear patient groups (e.g., high-risk heart failure, early-stage hypertension), pharmaceutical companies can design and promote drugs for specific segments. This aligns with the shift toward personalized treatments.
Impact:
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Increased demand for specialized cardiac drugs
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Opportunities for companion diagnostics
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Stronger doctor-pharma alignment on treatment plans
3. Better Forecasting and Market Planning
By understanding the size and needs of each patient segment (e.g., rising-risk vs chronic heart disease patients), pharma companies can forecast demand more accurately and plan production, distribution, and inventory accordingly.
Impact:
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Reduced inventory waste
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Optimized supply chain
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Data-driven sales projections
4. Stronger Patient Adherence Programs
Pharma companies can build segment-specific support programs such as apps, SMS reminders, or educational content for cardiac patients based on their risk level, lifestyle, or behavior.
Impact:
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Better health outcomes through consistent use
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Lower discontinuation rates
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Stronger brand trust among prescribers and patients
5. Improved Collaboration with Healthcare Providers
Pharma firms can work with cardiology departments and payers to create bundled care plans or value-based drug reimbursement programs tailored to specific patient groups.
Impact:
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Shared risk and reward on drug outcomes
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Long-term partnerships with providers
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More patient-centric business models
💡 Further resources
Final Words
Effective patient segmentation helps cardiology providers move from general treatment to smart, targeted care. Whether using lab tests, behavior patterns, CRM tools, or imaging data, these strategies help improve patient outcomes and optimize resources.
Hospitals that use segmentation well are better prepared for preventive care, chronic disease management, and value-based health delivery.
