At a Glance:
- Importance of Patient Segmentation in Healthcare: Patient segmentation allows healthcare organizations to tailor care delivery and policies for different groups, helping identify high-risk, high-cost patients and customize interventions.
- Process of Patient Segmentation Analysis: The process involves creating patient groups based on shared characteristics, utilizing EHR data and analysis tools for effective segmentation. Health systems should set relevant goals for segmentation, such as improving outcomes and identifying high-performing providers.
- Benefits of Patient Segmentation: Not only does patient segmentation facilitate personalized care and targeted interventions but it improves health outcomes and reduces preventable spending.
What is patient segmentation?
Patient segmentation is the practice of grouping patients into distinct cohorts based on shared
characteristics: clinical condition, demographics, behavior, risk tier, or payer. Healthcare organizations
use segmentation to personalize communication, allocate care management resources, and design
targeted interventions for high-risk groups. Patient segmentation differs from healthcare market
segmentation, which focuses on buyer personas for commercial and marketing purposes.
Patient segmentation is how health systems move from one-size-fits-all communication
to targeted care that improves outcomes and controls cost. This guide explains what patient
segmentation is, the 5-lens framework to use, demographics examples, how it differs from healthcare
market segmentation, and how to run your first segmentation analysis.
The Patient Engagement Playbook
Artera™ recently created “The Patient Engagement Playbook,” which offers six best practices for patient engagement to enable you to foster lasting connections with your patients that will ultimately lead to greater results for both your patients and your organization. The playbook will walk you through six critical steps to increase patient engagement – the second of which covers patient segmentation analysis.
But before we dive into patient segmentation, let’s define what engaged patients are:
- Informed – They understand their health status and the recommended treatment.
- Heard – They communicate with their providers and participate in shared decision-making.
- Empowered – They believe they can change their health outcomes.
- Active – They take action on their health based on personal learnings and overall understanding.
Although engaging patients – and keeping them engaged – can be challenging, it is a vital process for attracting and retaining them. Effectively engaging patients in their care is essential to improving health outcomes and staff efficiency, increasing patient satisfaction, and reducing costs while driving revenue.
“The Patient Engagement Playbook” discusses how greater patient engagement can:
Improve health outcomes: Strong patient engagement increases adherence to treatment regimen recommendations among patients, which in turn leads to fewer complications and re-hospitalizations. Several U.S. studies recently reported coordinated care trials that actively engaged patients with chronic disease resulted in significant mortality reductions compared to a control group who merely took appropriate medications. The studies suggest chronically ill patients who are engaged in their care live longer than unengaged peers who otherwise receive similar treatment, meaning health and well-being are fostered by engaged and activated patients. [1, 2, 3]
Enhance staff efficiency: Patient engagement strategies can help reduce front-end staff workload by reducing time spent on phone calls which take up a significant portion of time and can lead to support staff burnout. With more valuable time back, staff can spend more time on direct patient care.
Increase patient satisfaction: Personalized and unique patient engagement enables patients to feel heard and seen and empowers them to make decisions about their care. This can enhance overall satisfaction, and facilitate longer-standing relationships with providers while improving patient experience measures.
Reduce cost and drive revenue: Patient engagement directly contributes to outcomes affecting hospital costs and reimbursement for health systems. Executing effective patient engagement strategies also means better patient retention and referrals.
If care is to be truly centered on the patient, the patient’s specific care needs and other characteristics must be addressed. While it can be challenging and quite expensive to meet the unique needs of every individual, programs can be developed for groups of patients with similar characteristics. This process refers to patient segmentation, also known as healthcare market segmentation.
Patient segmentation vs. healthcare market segmentation
According to Health Affairs, “Patient Segmentation divides a patient population into distinct groups—each with specific needs, characteristics, or behaviors—to allow care delivery and policies to be tailored for these groups.”
Patient segmentation and healthcare market segmentation are often confused. They are related but
distinct:
| Dimension | Patient segmentation | Healthcare market segmentation |
| Audience | Existing patients | Potential buyers (patients, providers, payers) |
| Purpose | Improve care delivery, reduce cost of care | Inform marketing, sales, product positioning |
| Inputs | EHR clinical data, demographics, SDOH, utilization | Survey data, market research, firmographics |
| Example cohort | Diabetic patients on insulin in post-op 30 days | Health system CIOs at 500+ bed hospitals |
| Deployed by | Clinical ops, care management, population health | Marketing, sales, product strategy |
Segmentation is both person-Centric (an individual is assigned to just one category based on their top health need and hierarchical (a range from healthiest to highest clinical need).
The 5-lens framework for patient segmentation
Most high-performing health systems segment patients using five lenses. Each lens answers a different
operational question.
| Lens | Question it answers | Typical Segments |
| Clinical condition | what is the patient’s primary medical need? | Diabetes, CHF, COPD, oncology, behavioral health, maternity |
| Demographic | who is the patient in basic terms? | Age band, gender, language, zip code |
| Behavioral | how does the patient engage with care? | Portal user, no-show rate, adherence score, channel preference |
| Risk tier | how likely is a costly event? | Healthy, risking-risk, high-risk, complex care |
| Payer | how is care financed? | Commerical, Medicare, Medicaid, self-pay |
High-need, high-cost patients
As a healthcare organization, it’s crucial to segment your patient populations as it will help you identify high-risk, high-cost patients, understand the complex care needs of various patient groups, and tailor care delivery or engagement efforts based on those specific needs. These distinct groups can be based on various factors, such as risk of morbidity or mortality, usually defined by a long-term condition, pain, discomfort, functional status, as well as demographics, predicted behavior, or social determinants of health.
Addressing high-need, high-cost patients is increasingly pressing. Today, payers and providers alike are focused on finding more meaningful ways to deliver care for high-cost patients – those defined as having three or more chronic diseases and who account for a large portion of healthcare spending – to improve outcomes and decrease spending. To do so effectively, organizations must not only target patients on the basis of cost alone but must also consider their differing personal characteristics and needs.
According to research by The Commonwealth Fund, high-need adults “average annual per-person spending on health care services and prescription medicines topped $21,000, nearly three times the average for adults with multiple chronic diseases only, and more than four times the average for all U.S. adults.”
How to perform a patient segmentation analysis
Follow this four-step analysis any time you are building a new cohort or refreshing an existing one.
Regardless of how it’s done, improved patient segmentation can improve quality metrics, target resources or communication strategies, reduce healthcare spending, and drive better outcomes. To help you get started with this process, we’ve outlined the four critical steps to conducting a patient segmentation analysis:
- Create patient segmentation groups
There is no one right way to create patient groups. In fact, the segmentation logic may be “completely data-driven” or “non-completely data driven,” depending on the types of data available and the specific goals of the health system. Using a data-driven approach, data from a variety of sources are pooled and statistical clustering is performed, whereas with a non-completely data driven approach, expert input is used for defining segmentation criteria.
Ultimately, the first step in any patient segmentation analysis is to identify the shared patient characteristics and insights on patients’ behaviors and attitudes you want to collect. Determine the factors you need to create segment groups such as long-term conditions, demographics, age, geographic location, etc. Segmenting patients with complex needs into smaller, targeted subgroups can help further the focus on which interventions may be more effective for specific people.
According to Researchers at the Center for Medicare and Medicaid Services (CMS), segmentation based on patients’ “health prospects and priorities” enables an element of “patient-centeredness” – a goal that many providers increasingly strive to meet. What’s more, this approach provides the strongest potential for improved outcomes. When dividing patient populations into segments, the same researchers cited three “conditions” or useful principles to consider:
- The number of segments must be limited – each segment should represent a relatively substantial portion of the population
- The segments need to include everyone meeting the segmentation criteria
- The people in each segment should have similar healthcare needs, rhythms of needs, and priorities—and segments need to be sufficiently discriminatory
The ultimate goal of patient segmentation is to build behavioral profiles and collect meaningful data for each patient so providers exactly when, where, and how to reach them. By identifying patient needs and risks, health systems have the opportunity to design unique care plans or service packages, ensuring that each individual’s health needs are met effectively and efficiently.
- Use EHR data and patient segmentation analysis tools
With no one proven model, some health systems develop their own approaches to segmenting patients while others use big data to help divide a patient population into distinct groups, as previously stated. Despite varying models, data from administrative systems or well-designed electronic health records (EHRs) can be critical in helping to group patients into segments based on pre-determined factors. Ideally, the EHR has a comprehensive record of a patient’s healthcare journey from various sources such as hospital and clinic records, along with a history of patient communications to help identify which segment a patient fits. The EHR information, in conjuction with a patient segmentation analysis tool, may very well enable a triangulation of patient healthcare need variables from various sources.
Health systems also have the choice of using various off-the-shelf patient segmentation data analysis tools. A sampling of available solutions include:
- The 3M Clinical Risk Groups (CRGs), a population classification system that uses inpatient and ambulatory diagnosis, procedure codes, pharmaceutical data, and functional health status to segment patients among 272 groups for detailed risk analysis.
- The Johns Hopkins Adjusted Clinical Groups System offers a patient segmentation tool, Patient Need Groups (PNGs), which groups individuals based on their specific health needs, individual characteristics, and behaviors.
- The Community Assessment Risk Screen (CARS) is a simple method for identifying elderly patients who are at higher risk for health service use and increased costs by allocating patients to one of ten risk levels.
- Decision Point Healthcare Solutions developed a set of patient personas that address behavioral patterns beyond age, gender, and disease state – a more modern approach viewing patients’ highly variable lives and behaviors as defining risk states.
Another segmentation method health systems have employed is to use geographic data software called geographic information systems (GIS) to better understand patients’ needs. GIS tools can map around 10,000 data points regarding consumer behaviors, and provide information on healthcare spending per capita in a specific location. This data is combined with other information such as demographics, health services consumption, and disease prevalence information to create health profiles for specific communities.
For example, Loma Linda University Health wanted to find out why they consistently were getting large numbers of people brought to their hospital for 72-hour psychological holds. They used the Esri GIS software to map where the patients were coming from and discovered they were all from certain communities. This information prompted a further investigation which revealed that law enforcement in those neighborhoods needed to be appropriately trained on how to identify people who needed hospitalization and those who did not.
- Identify patient segmentation analysis goals
Below is just a sampling of the many positive outcomes a patient segmentation analysis can provide. When setting up your patient segments and analysis framework, ensure you identify key goals and measures within your organization.
- Once specific groups are segmented and analyzed, interventions, care, and patient engagement strategies can be tailored to the particular needs of each population group.
- Segmentation can help health systems identify those patients who are high-cost and high-need, along with why they become this way. This can ultimately help limit preventable spending, for example, by cutting down on high-cost utilzation services such as emergency department (ED) visits.
- Segmentation can help determine the return-on-investment (ROI) for specific populations by identifying which interventions or treatments work for patients with complex needs.
- Segmentation and analysis can help identify the providers in a health system who are the most effective and high-performing.
- Segmentation can improve quality metrics and drive improved health outcomes. Troy Long, M.D., executive consultant on population healthcare for PMG Healthcare Solutions, explains it as such: The top and the bottom of any set of quality benchmarks may sit 15 points apart. That “flat part of the curve” is where patient segmentation makes a difference, even just by “making a smidgen.”
- Health systems adopting cohort-similarity approaches to patient segmentation can more readily incorporate a wide variety of patient-centered, whole-person approaches to care, such as focused outreach and communication, targeted digital programs, integrated practice units, and more.
- The process of patient segmentation can help providers better understand the community they’re serving, gaining insights that can improve access to care and eliminate any barriers or roadblocks in the way.
- Health systems may even use segmentation to make sure they’re not missing patients who don’t seek out care but may nonetheless be high-risk, ensuring no patients are left behind.
- Sharing patient segmentation analysis
Using segmentation, health policymakers are better able to match healthcare services to needs, thus improving whole population health outcomes and even reducing healthcare spend. Take a detailed look at how patient segmentation impacted your patient outcomes and system in general and make sure to document the progress. When these changes occur to fit a more personalized, strategic approach to care, it’s important to share the analysis more widely so others can learn from your processes.
Another promising trend is the sharing of information – specifically social determinants of health – across communities. For example, The Parkland Center for Clinical Innovation (PCCI), a nonprofit that was spun off from Parkland Health & Hospital System, is building the Dallas Information Exchange Portal, a platform that will allow hospitals, clinics, homeless shelters, food aid groups, and other service organizations to share information about the social and economic needs of vulnerable patients. Ideally this will help inform providers of the various types of patients and social parameters within the community.
Patient demographics examples
| Segment name | Definition |
| Spanish-preferring Adults | Primary language is Spanish; serves 15-25% of patient population in many US systems |
| Medicare-eligible (65+) | Age 65+; eligible for Annual wellness visit and preventive screenings attribution |
| Post-surgical ortho, first 30 days | Patients within 30 days of joint replacement or spine procedure |
| High-utilizer ED patients | Four or more ED visits in trailing 12 months; rising-risk cohort |
| Pediatric chronic | children under 18 with asthma, diabetes, or other chronic condition |
| Rural zip codes | Patients in rural census-designated areas; higher transportation and access barriers |
Patient segmentation in pharma
Pharma companies segment patients differently than health systems. They typically segment by
indication (primary diagnosis), stage of disease, treatment history, adherence profile, and eligibility for
clinical trials. This informs patient support programs, adherence outreach, and prescriber-targeted
outreach. Pharma segmentation data is often combined with provider segmentation data to design
effective launch and commercialization strategies.
Patient Segmentation Can Lead to More Personalized Care
Overall, with more clarity, understanding, and intentionality, comes focused opportunities for health improvement – especially for patients who may require more complex care management. If you could get ahead of an individual’s needs, and design and execute a care plan, you have the opportunity to hopefully keep them out of the hospital – saving time, money, and potentially, a life.
“We want to understand what are the issues they face, what are the barriers that they face, who are they, where are they going, what’s happening to them. And we can use that information to drive strategy,” says Pete Knox, executive vice president at Bellin Health.
For example, a 2019 study found that food insecurity – which can lead to diabetes, heart disease, and other chronic conditions – costs the health system an additional $53 billion a year. By proactively identifying people living with, or at risk of, food insecurity, and then engaging with them early on, we could significantly reduce the health consequences that it causes, improving overall outcomes.
As health system reform shifts from fee-for-service to value-based care models, the incentives to focus on and improve care for high-cost, high-need patients will only continue to grow. With no single proven model, however, health systems will need to get creative in terms of developing their own approaches to patient segmentation. What’s most important is to gain a deeper understanding of your patients and their behaviors so you can not only improve outcomes and advance personalized care but also reduce costs and identify areas of opportunity.
Why healthcare market segmentation is the next step after patient segmentation
Healthcare market segmentation is essential in today’s rapidly evolving industry. By dividing the market into specific groups based on demographics, needs, behaviors, or preferences, healthcare providers and organizations can deliver more personalized care, improve patient outcomes, and enhance overall efficiency. In practice, patient segmentation informs the care delivery side, and healthcare market
segmentation informs the marketing, product, and growth side. Top-performing health systems do both
in parallel.
Segmentation helps identify underserved populations, tailor communication strategies, and allocate resources more effectively. As patient expectations grow and competition increases, prioritizing market segmentation allows healthcare organizations to stay ahead by addressing unique needs and fostering stronger patient relationships.
Optimized patient engagement strategies are essential for healthcare providers looking to properly address patient needs, which have shifted dramatically as a result of healthcare consumerization and the COVID-19 pandemic. Now more than ever, providers need to consider the six strategies in The Patient Engagement Playbook to facilitate greater patient engagement, connection, and understanding.
Resumen en Español
La segmentación de pacientes en salud es la práctica de agrupar pacientes en cohortes distintas según
características compartidas: condición clínica, datos demográficos, comportamiento, nivel de riesgo o
pagador. Las organizaciones sanitarias utilizan la segmentación para personalizar la comunicación,
asignar recursos de gestión de la atención y diseñar intervenciones específicas para grupos de alto
riesgo. Se diferencia de la segmentación del mercado sanitario, que se centra en los perfiles de
compradores con fines comerciales. Los sistemas de salud de alto rendimiento utilizan cinco lentes
(clínico, demográfico, conductual, nivel de riesgo, pagador) para construir segmentos efectivos que
mejoren los resultados y reduzcan los costos.
Behavioral patient archetypes: the segmentation Deloitte uses
Clinical and demographic data tells you what a patient has and who they are. Behavioral archetypes tell
you how the patient engages with their care. Deloitte’s research on 4,530 healthcare consumers across
158 variables identified four behavioral archetypes that consistently predict healthcare engagement
patterns. Adapted for patient communication:
| Archetype | Behavioral signal | Best communication approach |
| Digital-first Self-Manager | Books online, uses portal, prefers SMS, pays bills digitally | Self-service tools, minimal-touch reminders, mobile-first |
| Family caregiver | Often the spouse, adult child, or parent making decisions for someone else | Include caregiver explicitly in communication, share permissions |
| Cautious Traditionalist | Prefers phone, asks lots of questions, wants reassurance from a human | Voice-first outreach, longer reminders, clear escalation to human |
| Disengaged Patient | Misses appointments, low portal use, low message response rate | High-touch SMS reminders, multiple channels, simplified messages |
Build these archetypes from EHR data plus communication-engagement signals from your messaging
platform. They sit on top of the clinical and demographic segments, not instead of them.
The risk pyramid: high-risk, rising-risk, low-risk
The most-cited industry shorthand for risk-tier segmentation is the three-level pyramid:
| Tier | Share of population | Definition | Intervention Strategy |
| High-risk | 5 to 10% | Multiple chronic conditions, high utilization, complex needs | Care management, frequent clinician touchpoints, automated check-ins between visits |
| Rising-risk | 20 to 25% | Trending toward complexity (early diabetes, hypertension, recent hospitalization) | Proactive outreach, lifestyle interventions, SDOH screening to prevent escalation |
| Low-risk | 65 to 70% | Generally healthy, preventive- care focused | Light-touch outreach, annual wellness, screening reminders |
Most health systems already segment the high-risk tier. The rising-risk tier is where most of the savings
actually live, because it is where you prevent the high-risk tier from growing.
Data-driven vs. clinical/expert-driven segmentation
There are two ways to build segments:
- Data-driven: feed EHR clinical data, demographics, claims, and communication-engagement
signals into a clustering algorithm (commonly K-means, hierarchical clustering, or DBSCAN). Let
the data surface natural groupings. Best when you have rich, clean data and the segments are
not obvious. - Clinical/expert-driven: clinical leaders and care managers define segments based on care
pathways, condition stages, or known cohorts. Best when the segments are well-known (HRRP
six conditions, AWV-eligible Medicare, T1D vs. T2D). Faster to deploy.
Most production segmentation programs use both: clinical experts define the obvious cohorts; data-
driven clustering surfaces the non-obvious ones.
Expanded segmentation criteria: 10 dimensions to consider
The 5-lens framework above is the starting point. In production, most systems use closer to 10
dimensions:
- Clinical condition (primary diagnosis, comorbidities)
- Risk tier (high, rising, low)
- Demographic basics (age, gender, zip code, language)
- Behavioral archetype (digital-first, caregiver, traditionalist, disengaged)
- Appointment frequency (annual, episodic, frequent)
- Allergies and contraindications (drives content gating in messaging)
- Therapeutic department or specialty
- Acute vs. chronic disease status
- Communication channel preference (text, voice, email, portal)
- Lifecycle stage (pediatric, adolescent, adult, geriatric)
Patient segmentation in retail and specialty pharmacy
Pharmacies segment patients on a different set of triggers than clinical providers. Where pharmacy-
specific segmentation focuses:
- Refill triggers: patients due in next 7 days, patients at risk of running out (adherence flag).
- Therapy class: chronic medications (diabetes, hypertension, statins) vs. acute (antibiotics, pain).
- Specialty drug cohorts: oncology, MS, rheumatology — high-cost, high-touch.
- Adherence profile: gold (95%+ MPR), silver (80 to 95%), bronze (under 80%).
- Plan transition cohorts: patients changing payer or formulary status.
Meet Maria: a rising-risk segmentation in action
Maria is 52, recently diagnosed with prediabetes, Spanish-preferring, books her own appointments via
SMS, and missed her last lab draw. Her segments: rising-risk tier, primary-care setting, behavioral
archetype Digital-First Self-Manager, Spanish-language, behavioral health risk flag pending.
Without segmentation, Maria gets a generic ‘reminder to schedule your annual physical’ English-
language email blast. With segmentation, Maria gets a Spanish-language SMS that acknowledges her
recent prediabetes diagnosis, offers her a video-visit option that fits her work schedule, and includes a
one-tap link to schedule the missed lab. Maria responds. The system books the appointment, schedules
the lab, and adds her to a rising-risk diabetes prevention outreach cadence.
That is what segmentation does in production. It is the difference between an email blast and a
conversation.
Frequently asked questions
What is patient segmentation?
Patient segmentation is grouping patients into distinct cohorts based on shared characteristics (clinical
condition, demographics, behavior, risk tier, or payer) to personalize communication, allocate resources,
or design targeted care interventions.
What are examples of patient segmentation?
Common examples include: diabetic patients on insulin vs. oral medication, pediatric vs. geriatric
populations, high-utilizer ED patients, post-surgical ortho patients in first 30 days, and English- vs.
Spanish-preferring populations.
What is the difference between patient segmentation and healthcare market segmentation?
Patient segmentation groups existing patients clinically or behaviorally to improve care. Healthcare
market segmentation groups potential buyers commercially to inform marketing and product strategy.
How is patient segmentation used in pharma?
Pharma segments patients by indication, stage of disease, treatment history, and adherence profile to
design more effective clinical trials, patient support programs, and targeted outreach to prescribers.
What data do you need for patient segmentation?
Typical inputs: EHR clinical data (ICD codes, labs, medications), demographics, social determinants of
health, utilization history, communication preferences, and optionally claims data.
