And, once a clinical trial ends, patients are no longer tracked, Hauser added.
Also hindering advances in personalized medicine is the compartmentalization of healthcare data at hospitals, private practices and even clinical trials.
Additionally, EHRs use proprietary software, meaning they don't communicate with other systems. An EHR from Meditech, for example, doesn't natively share data with one from Cerner, McKesson or Epic Systems - the four largest EHR makers in the world.
"We realize the data standards wars and interoperability issues that go on amongst EHR vendors is not something that's going to be overcome in the near future," said Josh Mann, assistant director of Oncology Technology Solutions for the ASCO.
There is, however, an industry-wide effort under way to break the logjam.
For example, the non-profit Health Level Seven International this month released standards and guidelines that enable hospitals to exchange medical information, including radiological images.
Beginning in March 2010, $564 million in federal funds were allocated to states to develop health information exchanges, which allow for the sharing of health information electronically through data translation engines that allow EHRs to share information over secure Internet links.
The federal government has developed the Nationwide Health Information Network (NwHIN), which encompasses a set of standards, web services and policies that enable the secure exchange of health information over the Internet.
Currently, health information exchanges are being adopted at the regional, or at best, state-wide levels. Some of the most significant health information sharing networks are being deployed among healthcare providers themselves or by healthcare non-profits.
For example, the ASCO recently completed building a data analytics engine that pulls together information from more than 100,000 breast cancer patients from 27 oncology practices using disparate EHR systems. While still a prototype, the system does represent one of the largest breast cancer data sets in the U.S., according to Hauser.
Built mainly on open-source software, the ACSO's CancerLinQ project is a "learning health system" that will eventually analyze data from millions of cancer patients via their EHRs. The prototype system ingests de-identified patient data form two dozen oncology practices.
"We architected the system in such as way as to be able to accept any data in any format and then we used machine-learning algorithms to identify what was sent to us," Hauser said.
Once in the database, the data is mapped to a standardized medical vocabulary such as would be contained in the World Health Organization's International Classification of Diseases (ICD).
While the prototype was built just as a proof of concept, cancer doctors will eventually be able to consult the full-scale database like a Google search. That will allow doctors to see how patients with the same types of cancer were treated around the country, and how they fared.
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