Understanding the HCP: Patient-Level Data Proves Valuable

Pharmaceutical ExecutivePharmaceutical Executive-10-01-2021
Volume 41
Issue 10

Evaluating patient data in the context of complementary information can provide comprehensive market insights for product strategy.

Key to the success of any pharmaceutical product is a deep understanding of healthcare professionals (HCPs): Who treats the indicated patients, what drives their prescribing behaviors, which messages resonate with them, how do they interact via referral pathways, and who wields meaningful clinical influence? Management and sales teams pore over prescription and provider data to glean this intelligence, but reliance on these data alone could lead them to draw flawed or incomplete conclusions about the market potential or performance of their brand. Patient-level data is a crucial component of market and HCP assessment, providing a more comprehensive foundation for a successful product strategy.

The examples below illustrate how patient-level data can complement other data types to deliver unique context, nuance, and insight around market opportunities, effective messaging, networks of care, and spheres of clinical influence.

Accelerating recruitment and site selection

A large biopharma focused on immune modulators for pediatric patients needed to identify HCPs who could support a pharmacokinetic clinical study ahead of potential therapy approval. By using patient-level data, experts created a list of target HCPs treating the relevant patients who fit the key eligibility criteria, including age range, diagnoses, and treatment status. They likewise used patient-level data to map critical HCP affiliations to inform outreach, contacting HCPs seeing screening-eligible patients. In their case, patient-level data accelerated HCP recruitment and site selection for the trial.

Appreciating true market potential

A startup developing a new potential endocrine therapy wanted to understand its market potential, knowing it would face competition within the indication. Prescribing data suggested relatively low utilization of the competing drug, throwing into question the potential market value of its own product. Analysis of patient-level data painted a completely different picture of the market opportunity, by identifying net-new target patients who were untreated but had the relevant condition. Relying on prescription data alone—in essence, using the competitor’s results as a benchmark—could have led the company to vastly underestimate the true potential of its candidate therapy.

Visualizing patient-level market share

A small- to medium-sized biotech company had gaps in understanding its market basket prior to launching its flagship second-line therapy. As its therapy is indicated at a specific point in the patient treatment journey, the team needed high-resolution data to understand the prescribing potential of HCPs. An analysis from a legacy data aggregator did not accurately capture the connection between scripts and prescribers, offering an insignificant level of visibility into the company’s patient-level market share, especially for competitive therapies with multiple indications. But patient-level data provided unparalleled prescribing resolution, capturing up to 70% more patients and their relevant clinical signals. This patient-level resolution set up a successful launch of the therapy in a competitive market.

Capturing a range of sub-specialties

Company ‘Q’ was preparing to launch a gene therapy for patients with a rare disease who seldom survive past early childhood. Given the limited time frame during which the therapy is indicated (patients <2 years of age), the field team wanted to engage as many relevant HCPs as possible. Patient-level data enabled the company to locate a wider swath of clinicians across sub-specialties, ultimately identifying seven new physicians responsible for putting 17 patients on therapy within the first three months of launch. Insights from patient-level data enabled the company to differentiate its product in a market with two other competitive therapies.

Identifying care teams

A small biotech’s ultra-rare therapy, indicated for a familial disorder, was underperforming in the first year of launch. Patient-level data helped the company identify the full care teams for patients already diagnosed with the rare disease. The company engaged those care teams, including providers that had not previously been their main targets, to identify family members who may be at risk. In one case, it recognized that an HCP was seeing up to 10 at-risk individuals from two families with this rare hereditary disease.

Streamlining patient journeys

A large biopharma company’s recently approved cell therapy is only available through certain academic settings with specialized expertise. The team needed to identify community HCPs seeing candidates for the therapy and educate them to direct those patients to the appropriate academic settings. Patient-level insights enabled the company to identify the right HCPs from community practices and to map its referral routes via actual observed patient relationships. Based on this knowledge, the company adjusted its engagement strategy to help HCPs streamline their patients’ journeys and close gaps in care.

When to intervene

A company with a late-line therapy for patients with liquid tumors wanted to engage HCPs at the right point in their patients’ treatment journeys. Using patient-level data, it could identify HCPs treating patients with competitive therapies, patients who were not responding to therapies, and patients who had already received multiple lines of therapy. The medical affairs field team connected with these HCPs to facilitate intervention at the precise stage of their patients’ journeys when this therapy is indicated

Patterns in the patient journey

A biopharma company recently launched a drug for a disorder with multiple subtypes and variable clinical presentation. The clinical complexity can cause patients to experience long diagnostic journeys involving multiple HCPs. Patient-level data enabled a top-of-the-funnel view, revealing a more complete picture of patients’ encounters with the healthcare system through time and across settings. This perspective allowed the team to see patterns and signals in the patient journey more clearly and identify the entry points and sequences by which patients interacted with their target HCPs. This facet of HCP behavior would not be visible from the provider level.

Mapping more touch points

An oncology brand in launch mode knew that its patients often saw numerous specialists and needed to understand the landscape, unmet needs, and treatment dynamics within its market basket. Prescription data alone gave the team little insight into the patient journey. But patient-level data offered a fresh perspective on the brand’s market share by illuminating HCPs’ behavior: network influence, referral directionality, prescribing influence, clinical volume, industry engagement propensity, and more. It revealed far more clinicians along the patient care pathway who were not well captured in other aggregated data. Deeper coverage of secondary and tertiary influencers provided a more comprehensive view of product utilization and treatment dynamics that enabled the team to create a robust launch strategy.

Discovering new key opinion leaders

A leading respiratory brand built its key opinion leader (KOL) list primarily based on providers’ publications, qualitative analyses, and specialty designations, which are often out of date. Using patient-level data, the team could identify new potential KOLs based on the patients HCPs saw and procedures they conducted. The team also looked at providers’ scientific and industry activities. All told, it added 1,365 HCPs to its KOL list, gained a fuller appreciation of the universe of influencers, and maximized the impact of its pulmonary therapy.


Pharma and biotech companies strive to understand HCPs so they can offer the insight and tools needed to help patients. While prescription and provider data are go-to sources, the picture they paint may be incomplete if it doesn’t incorporate comprehensive patient-level data. Starting with patients affords a much richer representation of patient journeys and helps pinpoint the HCPs who are best placed to influence them. From establishing disease burden and unmet need, to market launch, to evaluating comparative effectiveness, a better understanding of HCPs improves product strategy—and it starts with patient-level data.

Vivian DeWoskin, GM of Commercial Life Sciences, Komodo Health

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