IMS Health’s Longitudinal Access and Adjudication Data (LAAD) has revolutionized how companies view data, and structures the information in an easier way for all parties involved. In short, “LAAD is an anonymized patient sample dataset that captures longitudinal pharmacy (both retail and specialty) and medical claims. It was used to quantify payer channel distribution, co-pay card penetration, and low-income subsidy prevalence for these products.”
The dataset includes “pharmacy data that covers 40% of the market and is comprised of multiple sources, including national and regional chains, independent pharmacies, and a switch house for a comprehensive view into all types of retailers across all geographies.”
RxDataScience utilizes data from various sources, including IMS’s dataset, in several of their products, including Patient Journey, Persistency, and Source of Business. Patient Journey analyzes insurance claims for patients and look to see how patients move from one therapy to another. Source of Business looks at patients who have just started taking specific drugs and how many are switching from one drug to another. The company ingests the longitudinal patient data and performs data exploration and visualization with it by creating key pharmaceutical applications.
RxDataScience’s Nataraj Dasgupta, VP of Advanced Analytics and Data Science, has worked on the technical side of LAAD – essentially the ETL and staging of the data in RxDataScience’s production environment. While the ETL process was relatively straightforward, Dasgupta figured out how to create multiple dimension tables in order to analyze the large sets of data available utilizing LAAD.
“The data is rich in terms of the breadth of information across both pharmacy and patient-level information,” Dasgupta said. “There are multiple “Dimension” tables – attributes relating to Plan, Prescriber, Patient Demography, Patient Activity, etc. as well as “Fact” tables that combine attributes from the dimension tables along with Claim ID to provide a temporal view of how patients have moved across therapies and the corresponding prevalence of drugs used to treat the conditions.”
This dataset can be crucial for a pharmaceutical company if it is analyzed correctly. While it can be used for market and commercial research to understand how a product is performing in the market, LAAD can also aid in the sales strategy for the company.
“The information could be used to create, update and manage how the Pharma company will be selling drugs – essentially for developing the sales/marketing strategy,” Dasgupta said.
RxDS can deliver value in analyzing these datasets for the pharmaceutical company through their years of pharma experience and knowledge of data science. According to Dasgupta, the company can provide key insights insights in an agile, self-service manner through feature-rich LAAD Data Analytics interface. Currently, this kind of analysis is only available through expensive consulting firm engagements.
