Finding insights in pharma commercial analytics

By January 18, 2019 January 24th, 2019 Case study
Pharma Commercial Analytics

Pharma Commercial Analytics

Two core questions that constantly come up in pharma commercial analytics:

  1. How big, and what is the profile of our patient population?
  2. How many patients are there out there that could be treated with our drug but aren’t?

Companies are frequently using claims data to help answer these questions. This use case describes how we used Galileo Cosmos™ to provide some interesting answers.

Claims Data:

In tactical pharma commercial analytics, the most common data source is health insurance claims. There are multiple sources for claims data and it is not unusual for a large pharma company to purchase all (or nearly all) of them. This means that you have the best data available. Merging data from different sources is not permitted, completing similar analyses across multiple datasets is very informative.

You have to also analyze the extent to which there is overlap between the datasets. This way you avoid double or triple-counting. Galileo Cosmos™ supports this need by allowing rapid analysis across all your data to identify the likely overlap. To be clear, this is not an exact science, but getting an estimate of overlap to within 5% is valuable in ensuring accurate projections for forecasting.

Insights Driving Better Segment Definitions

Profiling your current patient population means getting a solid handle on the characteristics of your patient segments. With Galileo Cosmos™ we explore the following elements:

  • Disease profile and co-morbidity analysis of our drug vs the competition
    • What are the profile differences between patients on our drug and the competition?
    • How do co-morbidities affect drug usage?
    • How does our “typical” patient compare to what we would expect?
  • Patient flow characteristics of our patients vs the competition
    • Where does our drug fit into the treatment process vs our competitors?
    • What impact does the sequence of diagnosis have on treatment?
    • Are there other procedures that affect treatment processes?

From these and similar types of analysis, we are able to define and characterize the real patient segments for our drug and the competition – without relying on assumptions about what those segments should be.

Insights Enabling Better Forecasting

In pharma commercial analytics you want better forecasting. In this case, combining our estimates for data overlap with accurate calculations of segment size allows us to accurately size the three core groups of patients:

  1. The population of patients taking our drug;
  2. Patients no longer taking our drug who should still be on it;
  3. Patients being treated by the competition that should be on our drug;

The forecasts of these three groups, along with detailed characterizations of the groups provide us with a detailed view of how we should be modifying our marketing activity. Improving sales – right down to territory level and often by customer is a major benefit of pharma commercial analysis. The core requirement of tactical pharma commercial analysis is that the results can be used at the most granular level, and this is achieved by Galileo Cosmos™.

Integrating Data with Targeting Creates Results

Integrating these results at the territory and physician level allows us to generate targets that have high validity and high accuracy. By monitoring this on a regular basis we can use Galileo Cosmos™ to continually update and improve our targeting and forecasts.

Galileo Cosmos™ gives you

  • Cross-dataset analysis to establish segment size
  • Detailed patient flow patterns to identify targeted patient segments
  • Specific targeting to redirect resources
  • An integrated approach to update, monitor and modify the targeting system

Galileo Cosmos™ is a novel tool that has been designed to support these complex tasks. Contact us to book your demonstration and a discussion of your specific needs.

Galileo Cosmos™ – Turning Raw Data into Insights in Real Time

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