Optimizing a Life Sciences Organization’s Data Strategy for Rare Disease

Summary

We assisted a pharmaceutical manufacturer in assessing and optimizing its investments in rare disease claims datasets.

Client Type

Mid-sized pharmaceutical manufacturer

Challenge

A mid-sized pharmaceutical manufacturer sought our assistance in conducting a strategic review of its investments in rare disease claims datasets. Its existing investments included several datasets with significant overlap, requiring detailed parsing to count the total number of patients in each dataset with relevant diagnoses and cross reference those datasets to identify unique patients. The client asked us to assess the strategic value of each dataset, evaluate other datasets that may be worth purchasing, and offer recommendations on how the client might update its investment strategy for purchasing claims datasets for rare disease.

Solution

We conducted a three-phase strategic review to help the client optimize its rare disease data investments. In phase one, we assessed the client’s existing data ecosystem by interviewing 15 internal stakeholders to determine what data sources they had access to and how they used those data sources, and then applied the insights we gained to map the data ecosystem and document how it was being used under the client’s existing rare disease strategy. As we mapped the client’s data ecosystem, we also identified gaps to be filled in the data strategy refresh.

In phase two, we developed a request for information (RFI) to solicit information about other datasets that the client had not purchased previously and shortlisted 11 data vendors to participate in the RFI. We crafted the RFI as a comprehensive tool to collect as much information about each dataset as possible, and worked closely with each vendor, with regular input from the client, to ensure that we collected all standard and vendor-specific information that may be relevant to the client’s new data strategy for rare disease. For each dataset, we counted the number of open and closed claims as well as the number of claims by indication, and then used those counts to confirm the number of unique claims within each dataset for indications relevant to the client’s portfolio. These steps provided key intelligence the client ultimately used to reassess its investment strategy for rare disease datasets.

In phase three, we chose six of the RFI respondents to participate in a claims volume comparison by relevant disease area coverage. To ensure consistent inclusion criteria for claims across the vendors’ datasets, we provided each vendor with a standardized cohort definition for the indication of interest. We secured data use agreements and third-party agreements from all involved stakeholders, which enabled us to ingest the vendor data into our internal data environment. Using a probabilistic matching approach, our analysts compared the six datasets to determine how many unique patients and novel claims were contained in each one and summarized the results of these analyses for the client to support its reassessment of its investment strategy.

Outcome

The client plans to use the insights gained from our strategic review to optimize its data strategy for rare disease products, including by refining its investment strategy by adding or dropping datasets based on our assessment of claims overlap among those datasets. Our work also provided the client with a comprehensive overview of its own data ecosystem, which the client can use to more effectively integrate any new data assets into its technical infrastructure and its rare disease evidentiary and analytical strategy.

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