Health IT

How one hospital is using personalized medicine and secondary data to improve cancer care

MedCity News is providing in-depth coverage of HIMSS2012 as part of a special series sponsored by Hyland Software.

It’s well-known that lots of medications only work for a fairly small percentage of the patients who are treated with them.

Treatment often follows a familiar pattern: diagnose, select a drug, switch to another drug when the first doesn’t work, then switch to a third drug when that one doesn’t work.


Personalized medicine, in which drugs are prescribed based on what’s been shown to work on patients with similar genetic profiles, holds the potential to change that, and move healthcare beyond reactive treatments, said Mark Hulse, chief information officer with the H. Lee Moffitt Cancer Center in Tampa, Florida.

The problem is that today’s electronic medical records systems aren’t designed to support the level of data needed for that to happen. To arrive at the true value of personalized medicine, a large amount of data and deep analytics to make sense of that data are needed, Hulse said in a presentation at the annual HIMSS health IT conference.

The good news for hospitals is that much of this data, known as secondary data because it’s originally collected for other purposes, is available and able to be mined. The challenge for hospitals is to bring together this disparate data — clinical, financial, human resources and supply chain data, for example.

With the help of IT giant Oracle, H. Lee Moffitt has done just that. To date, more than 78,000 patients have agreed to be followed throughout their lifetimes and allow the hospital to study their tumors using molecular technology. The hospital has collected data on more than 28,000 tumors, Hulse said.

To help the hospital achieve its goals of using secondary data and personalized medicine to improve cancer care, Hulse shared the following best practices:

  • Establish a flexible data integration, management and analysis platform to enable research on patient-specific outcomes.
  • Provide the capability to perform detailed queries on that large amount of data.
  • Create an architecture that supports localized disease research through a central data hub.
  • Enhance the hospital’s ability to repurpose the data for research by linking key domains like clinical, molecular and epidemiological data.
  • At the point of care, provide doctors and patients with all available treatment options and their comparative effectiveness to allow for personalized decision making.

 [Photo from flickr user Sean MacEntee]

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