Enhancing Healthcare by Mapping Influencers and Referrals
Amgen, one of the world’s biggest biotechnology companies, is focused on the cutting edge of molecular biology and biochemistry. The company’s goal is to provide a healthcare business based on recombinant DNA technology.
The Challenge
Amgen’s data deals with doctors, patients and the interactions between them, as well as the drugs and diagnoses for the patients.
The Solution
The company quickly saw the benefits of using graph database and analytics to manage its data, but quickly ran into problems scaling with its initial graph database vendor.
The Results
After upgrading to TigerGraph, Amgen was able to load the data a lot faster and identified referral networks in a massive dataset in terabytes
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The Challenge
Amgen’s data deals with doctors, patients and the interactions between them, as well as the drugs and diagnoses for the patients. The amount of information available to the data scientists at Amgen runs into terabytes and finding the connections among the data was challenging. In particular, Amgen wanted to create a social network of providers and, though the company had claims data and provider data, it lacked the ability to know which patient was referred from one doctor, perhaps a general practitioner, to another doctor, such as a specialist. “We deal with a humongous amount of data, EMRs (electronic medical records), and CHRs (comprehensive health records): according to Vishnu Maddileti, Amgen’s Director of Data Sciences and Analytics. “It’s in the terabytes- and combing through that in RDBMSs (relational database management systems) would be a nightmare.”
The Solution
The company quickly saw the benefits of using graph database and analytics to manage its data, but quickly ran into problems scaling with its initial graph database vendor. Loading the data took a lot of time and once it was loaded, computing either didn’t finish or was extremely slow. TigerGraph provided Amgen with the speed and scale they were looking for.
The Results
After upgrading to TigerGraph, Amgen was able to load the data a lot faster and identified referral networks, common patients and the most influential providers in a massive dataset in terabytes. “With 5 billion vertices and 20 billion edges, it’s huge data and finding inferences in that data is not easy, but TigerGraph has scaled for us.” says Maddileti.
The data sciences team at Amgen is now able to support its colleagues in marketing and branding with the insights they need about the referral networks and influential doctors and ensures everyone is fully informed about the medicines that can positively affect the quality of care of millions of patients.
The data sciences team at Amgen is now able to support its colleagues in marketing and branding with the insights they need about the referral networks and influential doctors and ensures everyone is fully informed about the medicines that can positively affect the quality of care of millions of patients.
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