CONTEXT:
In the world of drug commercialization, patient-level data is invaluable. Before launch, it helps forecast the target population, guiding everything from manufacturing to marketing strategy. Post-launch, it reveals how the drug is adopted in real-world settings, providing critical insights into its reach and impact.
This data typically comes in two forms: physician-level data and account-level data. Physician-level data, accessible through claims, gives a rich, real-time view into patient profiles, including demographics, adherence patterns, and treatment durations. These insights are essential for understanding who the drug serves best and how it performs across patient groups.
Conversely, account-level data—available when hospitals order drugs for inpatient use—lacks this patient detail. Here, drugs are ordered in bulk, with no individual prescriptions or pharmacy visits recorded. While we gain a picture of hospital demand, we lose the patient-specific nuances crucial for refining treatment approaches and supporting targeted outreach. In this space, patient-level data provides not just numbers but a true understanding of patient needs and treatment journeys. Its significance cannot be overstated—it’s the key to ensuring that the drug reaches the right patients and delivers meaningful outcomes.
CHALLENGE:
For pharmaceutical companies distributing drugs through the “buy and bill” model—where hospitals purchase and directly administer the drug—accessing timely patient-level data is a persistent challenge. These intravenous (IV) drugs are tracked only at the account level, limiting visibility into crucial patient-specific insights needed for strategic decision-making.
After launch, companies aim to track more than revenue, monitoring metrics like hospital adoption rates, prescription volumes, and the actual patient population. However, two major obstacles impede this:
- Data Gaps in Patient Reach and Usage: Without patient-level data, understanding the drug’s reach across patient populations remains uncertain. With only 30-40% of relevant data captured, companies lack clarity on which patients are receiving the drug and its real-world reach.
- Delayed Access to Critical Data: Due to data lags of up to two months, companies face delays in accessing even limited data. Additionally, buy-and-bill drugs rely on a J code for tracking claims, assigned only months post-launch. This often results in six months or more without detailed patient insights, creating blind spots in adoption and usage patterns.
- Regulatory and Reporting Pressures: Medical teams face early reporting requirements on patient adoption, often within months of launch. Without timely data, meeting these obligations is challenging, potentially impacting compliance and stakeholder relationships.
Without comprehensive patient-level insights, companies often make critical post-launch decisions based on an incomplete picture, limiting their ability to maximise the drug's impact in the market. By working to bridge these data gaps, we empower our clients to access the insights they need, enabling more informed strategies and supporting the drug’s success.
APPROACH:
To address the challenge of limited patient-level data, we developed a proprietary tool to estimate the range of patients receiving drug sunder the buy-and-bill model. By combining prescribing information, account ordering trends, and timing of recent orders, the tool provides actionable insights into real-world drug usage patterns. It continuously refines estimates as new data becomes available and by incorporating field feedback, helping clients better understand active patient populations and make informed decisions.
IMPACT:
Implementing the patient simulator tool brings multiple impactful outcomes, both for strategic oversight and practical field support.
First, the tool provides a reliable range of patient estimates based on account-level data, allowing the client to approximate patient numbers even in the absence of direct data. This enables the company to track patient volume over time, monitoring how the drug is adopted and used across different accounts.
This data also allows for regulatory reporting to medical and government agencies. By consolidating patient numbers derived from account patterns, the client meets reporting requirements and demonstrates compliance with transparency.
On the operational side, we extend this information to the sales teams, providing account-level insights tailored to each sales representative's specific territory. For example, if a rep has 10 accounts in their geography, the tool shows them the ordering patterns for each account and the corresponding estimated patient numbers. This allows reps to approach each account with a clear understanding of patient demand and opportunity. Furthermore, the field representatives serve as valuable sources of feedback. Equipped with territory-specific patient estimates, they verify or adjust the model based on their close relationships with doctors, nurses, and pharmacies in each account. Their insights help identify any new patients on therapy, refine our estimates, and adjust assumptions. This field-driven feedback loop continually fine-tunes the model, enhancing accuracy and adapting to real-world conditions.
Ultimately, this approach enables comprehensive reporting at both headquarters and field levels, supporting strategic planning, targeted outreach, and regulatory alignment. The result is a robust, data-driven foundation that maximises the drug’s impact across all operational levels.