Many pharma forecasts today are based on academic studies and manually collected primary market research data. The problem is that this data is often outdated, developed from a small sample size, or may not answer the precise research question. Until recently, this was the only way for commercial forecasters to make their predictions. And because the data was offline and manually collected, users trying to query assumptions faced a lack of reliability and transparency, especially during times of rapid change.

Fortunately, there are new ways to collect data for forecast assumptions. Today’s forecasting platforms can access real-world data from a vast number of global healthcare databases to provide more reliable and accurate results.

Real-World Data Changes Everything

Real-world data has transformed forecasting from an inconsistent and opaque process to an accurate and transparent solution that can deliver better, more targeted commercial strategies.

There are many advantages to using real-world data over the manually collected datasets of the past:

• Up-to-Date: Real-world data sources can be integrated into a forecast and updated on a regular basis, allowing live access to large-scale transaction, patient, and epi data.
• Reliable: Numbers are based on real data collected by physicians and patients – not theoretical estimates, creating a solid basis for the core forecast assumptions around patients and epidemiology.
• Have Breadth and Depth: By analyzing layered real-world datasets, forecasters can track the entire patient journey and answer questions relevant for marketing, brand teams, sales teams, and researchers to deliver the most value.
• More Accurate Forecasting: Large real-world datasets allow forecasters to leverage modern machine learning and trending algorithms, which can provide unparalleled insights compared to using static and smaller clinical datasets.

The questions that can be answered by real-world data range from simple ones about population size and treatment duration, to highly complex questions that provide insights into specific patient and physician behavior, persistence trends, dosing requirements, and how a competitor’s products will impact the market landscape. These insights make it possible to define specific market dynamics and estimate the true potential value of a product or opportunity with a high degree of accuracy.

This changes the role that forecasts (and forecasters) can now play in the drug development and commercial lifecycle.

Real-World Data Solves Real-World Problems

We’ve worked with many clients who have been able to generate highly accurate insights to shape more effective commercial strategies. 

Client Case Study #1 

IQVIA recently worked with a client to design a comprehensive oncology-asset forecast. This forecast was needed to inform a variety of cross-functional business decisions, from manufacturing through promotion. To be successful, the forecast needed to:

• Provide a detailed understanding of current and future market dynamics
• Predict product performance versus the competition
• Assess when and how patients would become eligible for the client’s product

Our team used medical claims data to conduct a detailed patient segmentation analysis. Patient segments were defined based on unique pathways to eligibility, and patient flow was recreated using proportional longitudinal data. This was aligned with preference shares determined in primary research to model market uptake. The various datasets were integrated into the forecast using IQVIA’s Forecast Horizon platform.

The final forecast was able to define unique values for dosing and duration by line of therapy. The insights from this analysis not only provided a highly accurate view of the market landscape, but also identified opportunities and potential leakage points, which informed the client’s patient retention mitigation strategy.

Client Case Study #2

In another project, our client was assessing the attractiveness and commercial potential of switching a late-lifecycle asset from prescription to over-the-counter. It would be a first-in-class move that was complicated by potential co-morbidities in the key patient population, making it difficult to clearly define the population that could use the product off-the-shelf.

The IQVIA team needed to determine the optimal label to pursue based on a precise understanding of the patient population and revenue potential. Our team analyzed electronic medical records (EMR) and National Health and Nutrition Examination Survey (NHANES) data to understand the percentage of patients who would qualify and likely use the product off-the shelf. This allowed us to precisely define the market potential of three different label scenarios providing a clear understanding of the opportunity and how to pursue it.

It’s up to you

These are just a few examples of how real-world data and advanced analytics has made it possible for companies to build commercial forecasts within even the most complex market scenarios with great precision. But you can’t generate these forecasts if business leaders don’t realize what’s possible.

Forecasters have an opportunity to elevate their role in strategic market planning by delivering complex, highly reliable forecasts using real-world data. The technology and data exist – it’s up to you to leverage it to deliver better commercial results.

To learn more about how we leverage real-world data to build better forecasts, click here.