By Matthew Dublin
Harvard Medical School and Brigham and Women's Hospital are using IBM business analytics technology to study the effectiveness and potential safety of prescription drugs. Using IBM's Netezza data warehouse appliance, Harvard researchers are conducting pharmacoepidemiology studies to analyze data from millions of de-identified patient records that include insurance claims data to develop novel data-intensive drug safety research methods. Netezza data warehouse appliances architecturally integrate database, server and storage components into a single unit.
Netezza's appliances use a proprietary Asymmetric Massively Parallel Processing architecture that combines open blade-based servers and disk storage with a proprietary data filtering process using field-programmable gate arrays.
"We wanted a computing platform with massive analytics power, but was extremely simple to administer," said Sebastian Schneeweiss, Associate Professor of Medicine, Harvard Medical School and Vice Chief of the Brigham & Women's Hospital Division of Pharmacoepidemiology and Pharmacoeconomics in a release. "As global health care evolves toward a learning healthcare system with a need for ongoing comparative effectiveness and safety research integrated in routine care, it is imperative that research methods evolve in parallel. IBM Netezza will accelerate our ability to devise, test and publish new computationally intensive algorithms applied to ever larger longitudinal healthcare databases that we hope will become the gold standard for researchers globally."
This project will also use use the Netezza technology to study economics and outcomes research once the effectiveness of a particular medication is established. Pharmaceutical companies and health insurers will have teams of HEOR analysts then demonstrate the value of new medical products for pricing and coverage decisions to provide higher quality of care with the hope that by intelligently mining claims data with a powerful analytics platform like IBM Netezza, they may be able to provide a faster way to answer questions about the most effective therapies, which has obvious economic benefits for both patients and health care providers.
Using business data architectures and IT technologies is not uncommon in life sciences research. In the journal Bioinformatics, a paper by Lauren Boyd and her colleagues entitled "The caBIG Life Science Business Architecture Model" describes how the cancer Biomedical Informatics Grid, or caBIG has adopted "Business Architecture Models," or BAMs, models that describe what a business does and how activities are accomplished, in order to establish a "Life Science BAM," or LS BAM. The caBIG LS BAM provides a shared understanding of vocabulary and processes common in life sciences research. The latest version of the caBIG LS BAM includes 90 goals and 61 researchers within "Use Case and Activity Unified Modeling Language" UML diagrams.
