UPMC Sinks $100MM Into Big Data

Posted on November 6, 2012 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

The University of Pittsburgh Medical Center has announced plans to spend $100 million over five years to create a massive data warehouse, a move which puts it well at the forefront of hospital “big data” efforts.

According to Information Week, UPMC’s data warehouse will bring together clinical, financial, administrative, genomic  and other information. The health system has targeted more than 200 data sources across the Medical Center, UPMC Health Plan and other affiliates.

I’ll let Information Week describe the technical set-up:

To collect, store, manage, and analyze the information maintained in the data warehouse, UPMC will use the Oracle Exadata Database Machine, a high-performance database platform; IBM’s Cognos software for business intelligence and financial management; Informatica’s data integration platform; and dbMotion’s SOA-based interoperability platform that integrates patient records from healthcare organizations and health information exchanges. These tools will manage the 3.2 petabytes of data that flows across UPMC’s business divisions.

As to how UPMC plans to use these tools, they’re hoping to do all of the things you might imagine, including genomically-tailored prescribing, population analytics and sophisticated tracking of individual patient data to make predictions about possible risks.

As I see it, UPMC’s efforts highlight both the importance of big data efforts and the downside in making the investment.

On the one hand, you’ve got the benefits. For example, patients will clearly see better outcomes if doctors can use top-drawer analytical tools to predict how treatments will work or know well in advance if a patient’s condition is about to go south.  And hospitals will clearly run better if execs get insights into issues that cross clinical and administrative boundaries, such as ED or OR utilization.

On the other, you’ve got the reality that big data projects are prohibitively expensive for all but the best-funded of healthcare organizations, and probably won’t produce returns on investment for several years at best.  Average community hospitals won’t be consolidating and analyzing their data this way anytime soon.