Case Study: Pharmaceutical Manufacturer Integration Project

Problem:

A large Pharmaceutical manufacturer with one of the nation’s leading specialty drugs had recently faced new competition from an approved competitor drug. With the new found competition, the company had to ensure their sales force had the most accurate data to be as effective as possible.

A data and research company was approached to help with the project but quickly found several technology hurdles they could not overcome. They contacted us to help with data management, analytics, and systems integrations. We put together a team of our experienced programmers who had worked with Veeva systems for the integration, as well as data architects and analysts to design a data warehouse to store all this data.

Affiliation data refers to the relationship between doctors, their practices, and the larger hospital and health systems that acquire them. As doctor’s practices are merged and acquired, and doctors move on from residency to new locations, this data quickly becomes stale. Once outdated, sales teams may waste time visiting an office that has been acquired by a company they already visited. Understanding and tracking this affiliation data is critical to a sales force.

The sales team was using Veeva to store their affiliation data, which required an integration to extract the data. There are third party data companies that maintain this data as well, but it is also not always up to date either, and is often late to report mergers and acquisitions. The company purchased the HCOS© and Exponent© datasets from IQVIA (formerly IMS) to supplement the data being maintained by Veeva.

Lastly, they had a third dataset of affiliations they derived using an automated claims analysis, which was used for reference and quality assurance. With three dynamic sources of data, the client needed them normalized and analyzed to create a dataset with validated and best quality data, with a process moving forward to keep the data verified and up to date.

 

Solution:

We came up with complex scoring system to give every record in all three datasets both a quality score and corresponding confidence score. We were able to verify a majority of the data using a computer analysis, and complemented the data with a quality assurance team to validate the results. For records with a low confidence score or where the computer could not determine the best record, we managed a team of business analysts who did primary research and manually verified the correct records.

 

Outcome:

After JB Consulting implemented our solution, the manufacturer had a validated dataset that was cleaned up, normalized and provided significantly more value than the previous three datasets by themselves. We developed a systematic process to continue validating new data and continue to keep their dataset up to date. We trained their quality assurance team and business analyst teams on best practices to continue in the future. We were good data stewards of the datasets from IQVIA and warehoused them in a secure cloud.

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