The Application of Patient Segmentation In Healthcare

Patients come from varied backgrounds, each with unique medical histories, cultural beliefs, and lifestyle factors that significantly influence their health needs and decisions. 

Categorizing patients into distinct groups based on common characteristics helps clinics deliver more personalized and effective care while optimizing the allocation of healthcare resources. 

Let’s see how.

What is patient segmentation?

Patient segmentation refers to the process of dividing a patient population into distinct groups based on demographic details, clinical needs, health behaviors, or other shared characteristics. 

By grouping individuals with similar health profiles, clinics can fine-tune healthcare services and interventions, leading to:

• More personalized care: Understanding the specific needs of different patient segments enables healthcare providers to offer more personalized care.
• Better resource optimization: Clinics can prioritize resources and interventions where they are most needed.
• Improved health outcomes: Tailored healthcare interventions lead to better management of diseases, more effective treatments, and overall improved health outcomes for patients.

With the basics out of the way, let's turn to the various types of segmentations and their role in improving patient care.

Types of patient segmentation

Patients can be grouped together in many different types. Healthcare providers need to understand these categories in order to effectively tailor their services and interventions.

In 2018, Deloitte Center for Health Solutions surveyed 4,530 US citizens about their attitudes, behaviors, and preferences when making personal health care decisions.

After analyzing the data, they categorized individuals into four distinct groups.

Based on this categorization, the report also presented suggestions for interacting with each group.

While that kind of research is extremely useful to gain a broader perspective about patients’ needs and preferences, both public and private healthcare organizations still have a plenty of use for standard patient segmentation types we discuss below.

Demographic segmentation

This is the most basic form of patient segmentation. It involves categorizing patients based on demographic variables such as age, gender, ethnicity, income, and education level. 

Here are a few examples of how clinics can use demographic segmentation to improve treatment outcomes:

1. Age-based programs: Implementing geriatric care programs for older adults, focusing on chronic disease management, fall prevention, and medication management.
2. Gender-specific health services: Offering women's health services or men's health clinics, addressing gender-specific health concerns like prostate or breast cancer screenings.
3. Ethnicity-centered health education: Tailoring health education programs to specific ethnic groups, considering cultural beliefs and language preferences to improve patient understanding and engagement.
4. Income-sensitive care options: Providing sliding scale fees or cost-effective treatment alternatives for low-income patients, ensuring affordability doesn’t impede access to care.
5. Education-level adapted communications: Adjusting health communication materials to match varying education levels, ensuring that all patients understand their health conditions and treatment plans.

Psychographic segmentation

This type of segmentation goes beyond basic demographics to consider the psychological aspects of patients, including their attitudes, values, lifestyles, and motivations. 

Psychographic segmentation allows healthcare providers to understand the underlying motivations and preferences of different patient groups, leading to more effective communication and care strategies.

Here are a few examples of how clinics can use psychographic segmentation to improve treatment outcomes:

1. Lifestyle-oriented wellness programs: Offering fitness or nutrition classes for patients who prioritize wellness and preventive health.
2. Mental health support for high-stress occupations: Providing targeted mental health services for patients in high-stress professions.
3. Personalized motivational strategies: Utilizing different motivational interviewing techniques based on patients' attitudes and values to encourage treatment adherence.
4. Holistic health approaches: Integrating alternative medicine options for patients who prefer holistic treatment approaches.

Behavioral segmentation

Behavioral segmentation focuses on patients' behaviors, including their utilization of healthcare services, their response to various health interventions, and their general health-related behaviors (like exercise habits, diet, and medication adherence). 

By understanding how different groups of patients interact with the healthcare system and manage their health, providers can develop more effective patient engagement strategies and interventions. 

Here are a few examples of how clinics can use behavioral segmentation to improve treatment outcomes:

1. Chronic disease management programs: Creating specialized programs for patients with specific behavioral patterns, like poor medication adherence in chronic disease management.
2. Frequent ER Visitors: Developing care plans for patients who frequently visit the emergency room, focusing on preventive care and chronic disease management.
3. Appointment reminders for non-compliant patients: Sending extra reminders or engaging in follow-up calls with patients who have a history of missing appointments.
4. Technology-driven solutions for tech-savvy patients: Implementing advanced telehealth options or health apps for patients who are comfortable with and motivated by technology.

Needs-based segmentation

Perhaps the most patient-centric approach, needs-based segmentation categorizes patients based on their specific health needs and conditions. This type of segmentation looks at the severity of the condition, the complexity of care required, comorbidities, and other health-related needs. 

By focusing on the specific needs of each patient group, healthcare providers can optimize care plans and resource allocation. 

In 2022, Johns Hopkins ACG system (population health analytics software) introduced a segmentation model called Patient Need Groups (PNGs). The goal was to group patient in a way to provides the most actionable insights to healthcare providers.

Here are a few examples of how clinics can use needs-based segmentation to improve treatment outcomes:

1. Integrated care for multiple chronic conditions: Providing coordinated care plans for patients with multiple comorbidities, involving various specialists and care managers.
2. Post-surgical follow-up programs: Designing specific follow-up protocols for patients recovering from surgery, ensuring they receive appropriate care and rehabilitation.
3. Prenatal and postnatal care: Offering comprehensive care programs for pregnant women, addressing their unique health needs before and after childbirth.
4. Palliative care services: Providing specialized palliative care for patients with terminal illnesses, focusing on pain relief and quality of life.
5. Telehealth for remote or mobility-impaired patients: Utilizing telehealth services to reach patients who are in remote areas or who have mobility challenges, ensuring consistent access to care.

Challenges in implementing patient segmentation

Implementing patient segmentation effectively can be as challenging as it is beneficial. Let's explore some practical ways to navigate these challenges:

• Data management: Clinics must not only gather large amounts of patient data but also analyze and safeguard it efficiently. Strong data management systems ensure that the insights gained from patient data are both accurate and secure.‍
• Ethical considerations: It’s crucial to strike a balance between categorizing patients for better care and avoiding any form of discrimination. Clinics should constantly evaluate their segmentation strategies to ensure they promote equitable treatment for all patients.‍
• Integration with healthcare systems: Clinics will often use platforms like InsiderCX in order to actively collect patient feedback. For efficiency, such platforms should be able to seamlessly forward data to your existing healthcare system.‍
• Patient privacy and consent: Respecting patient privacy and obtaining consent for using their data is paramount. Clinics must find the sweet spot where segmentation benefits both the patient and the healthcare system, without infringing on individual rights.

Understanding and predicting these issues is how you avoid common pitfalls.

The future of patient segmentation in healthcare

As technology advances, patient segmentation is becoming a pathway to more personalized, efficient, and effective healthcare. 

For example, advancements in predictive analytics and artificial intelligence (AI) enable the analysis of large datasets of genetic information, lifestyle factors, and previous health records — to identify patterns and predict their future healthcare needs.

Similarly, genomics and personalized medicine analyze a patient's genetic makeup, helping doctors predict the risk of certain diseases, understand individual responses to different treatments, and tailor medical care accordingly.

As patient segmentation becomes more integral to healthcare, changes in policies and regulations are likely to follow. This could include the development of guidelines for the ethical use of patient data, the standardization of segmentation methodologies, and the promotion of data sharing among healthcare providers. 

Until those technologies become mainstream, healthcare providers will have to rely on the more conventional ways to gather patient data and segment their patients.

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