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Whether positioning a drug effectively for a trial, launch, or market expansion, the risk of an inaccurate or incomplete view of patient cohorts has far-reaching implications.

Risks of
inaccurate
or incomplete
patient profile


  • Diminished clinical efficacy of a therapy, especially in real-world settings
  • Messaging ineffectiveness to physicians and patients
  • Challenging reimbursement negotiations with payers
  • Misallocated field resources of a salesforce
  • Opportunity cost with slower than predicted adoption
  • Unrealized patient outcomes by missing segments with high barriers to treatment

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Pharmaceutical teams are acutely aware that sub-populations vary greatly by disease-specific and environment factors, behaviors, treatment pathways and a host of other attributes.

The challenge is to determine how many significant patient sub-populations are present within a single disease, and which populations can be most effectively reached, using what approach.

Often, there are more significant variations between patient subpopulations than pharmaceutical teams initially suspect.

 

Reframing the Approach to Patient Segmentation

Two critical trends have converged to make advanced segmentation both necessary and possible:

A Changing Healthcare Industry
  • Shift from blockbuster therapies to precision medicine
  • Lean, agile and highly targeted biotech upstarts are entering the market
  • Health systems are increasingly fragmented by region
  • Patients have more options available to them, and are influenced by many factors
Major Advances in Data Science
  • Emerging data sources now available to us: EHR, claims, social intelligence
  • Novel ways to harness insights, like machine learning
  • New patterns uncovered with the convergence of data sources and methodologies

Treatment decisions are informed by many other factors than just diagnosis. Pharmaceutical teams also need to take into account the impact of patients’ cost burden, access issues, demographics, co-morbidities, emotional state, compliance issues, challenges with site of care, and much more.

And all the while, markets are shifting all around us. Patient segments are always moving. Every disease area has nuances to consider.

So how can a pharmaceutical team achieve a higher degree of clarity and confidence in their market assessments?

Building a More Complete View of Your Patients

The best commercial planning starts with understanding the range of real-world and traditional data sources you might use for advanced segmentation:

“A patient population isn’t a single average patient with one set of comorbidities or pill burden. It’s thousands or millions of individuals with their own unique combinations of factors affecting their access to treatment. We need to reach and engage them as such.”
– Simon Andrews
Vice President, Analytics, DRG

Sample data sources to integrate for more complete patient profiles and meaningful sub-populations
CLAIMS / EHR
  • Patient demographics
  • Procedure orders and lab test results
  • Geographic location of care
  • Doctor/hospital visits and frequency
  • Diagnoses and co-morbitities
  • Segmented treatment cascades
  • Out-of-pocket-costs along the care continuum
  • Government regulations
  • Employment status
  • Insurer data
EPIDEMIOLOGY
  • Overall disease burden
  • Incidence
  • Prevalence
  • Disease segmentations (stage/ severity)
  • Recurrence/ progression modelling
  • Sizing commercially relevant drug-treatable populations
ONLINE AND MOBILE BEHAVIORS
  • Search engine info-seeking
  • Websites and apps used for health
  • Content and message exposure and response
PRIMARY MARKET RESEARCH
  • Resource adoption and preferences
  • Attitudes, opinions and influences
  • Recall behavior
  • Custom characteristics

 

Tailoring Your Segmentation Strategy:

Examples of How to Dive Deeper into a Patient Group

 

Each patient segmentation study is as unique as the disease area.

Refine your hypothesis by asking key questions, for example:

 

How can I quantify and understand the best segment for a therapy?

Pharma teams may want to focus their investments on reaching providers and patients that are the most appropriate and have the best chances of uptake.

For example, a brand team may use real-world data to quantify and gain a deeper understanding of the population of rheumatoid arthritis patients who are younger and more likely to use a new therapy, in addition to other key segments – and tailor strategy accordingly.

 

How many meaningful segments require tailored marketing?

Most broad patient populations have diverse demographics, behavioral traits, socio-economic conditions, emotions and lifestyles. Their disease may be the least important characteristic about them.

For example, a team planning a launch strategy for a new NASH treatment would find patient types vary greatly by demographics, socioeconomic factors, insurance type, sites of care, co-morbidities, messaging response, and more.

An advanced sub-segmentation strategy helps a brand quantify the opportunity with key groups, justify a fine-tuned engagement approach, and identify and connect with patients in a more targeted way.

 

Which segments are struggling with treatment compliance and why?

Many factors influence a person’s decision to abandon their treatment plan – from side effects to lifestyle issues to cost to location of care.

For example, a cardiology team can analyze real-world data to quantify and qualify the dynamic issues causing patients to struggle with compliance. And then look at patient characteristics across segmentations, including co-morbidities, burdens of the disease, and location of care.

From there, a pharma team can design supportive outreach and resources to address the real underlying barriers in a far more effective way.

The old way is asking “Are patients switching?”

Now we need to ask “What is it about the patient that’s making them switch?

– Nishant Kumar

 

The Halo Effect of Advanced Segmentation

Asking hard questions and finding meaningful answers takes time, investment and analytical expertise. But drilling deeper benefits every stakeholder in our healthcare ecosystem.

When pharmaceutical teams understand their patients at a much deeper, nuanced level, roadblocks can be addressed and patients can be approached with the level of personalization they expect.

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