For example, more than 60% of cancer patients are over the age of 65 and have anywhere from two to five other chronic illnesses, such as congestive heart failure or high blood pressure. Trials with younger patients would not involve the same mix of health problems.
"You get a younger adult, in the age range of 50..., that doesn't have any diseases other than cancer," said Robert Hauser, senior director of the American Society of Clinical Oncology's (ASCO) Quality Department. "So, once a drug is developed from a trial, it ends up being used on a population that wasn't evaluated on a large scale. Right now, we only learn from 3% of all adult oncology patients because only 3% of them participate in clinical trials for drug development."
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.
While currently using a NoSQL, CouchDB database backend, the ASCO is considering using Cassandra with Hadoop for the full build. That database is expected to be completed in 12 to 18 months.
Beyond helping an individual patient, big data will allow the healthcare community as a whole detect poor drug interactions quickly. "So this gives us the ability to look at that [common cancer] population and figure out the best dosages and cycles of treatment," Hauser said.
While the ASCO is among the largest cancer research organizations, it is by no means alone in its use of big data in determining best practices.
Cleveland Clinic - a 4,500-bed healthcare system - uses an EHR from Epic Systems and a SQL transactional database for retrospective data analysis of its EHRs to improve patient treatment.
"We think first about outcomes: what data can we collect and make available to clinicians so they know how well they're doing in treating their patient," said Dr. C Marin Harris, CIO of Cleveland Clinic.
Cleveland Clinic is also starting to use Hadoop, but it's still a small part of the research because data is internally confined.
"It may appear if we only analyze Cleveland Clinic data that we're doing well with regard to a patient, but in fact if the patient went to someone else's emergency room 10 times, we didn't know that," Harris said.
Cleveland Clinic is working with other state health plans to collect a broader swath of patient data. Along with Ohio's other largest healthcare provider, University Hospitals, Cleveland Clinic is preparing to share data across Ohio's statewide electronic medical records exchange, CliniSync .
Once on the exchange, Cleveland Clinic will be electronically linked to 21 other hospitals already using the system.
One chronic disease targeted by the Clinic's data analytics engine is diabetes. The analytics engine searches EHRs for the results of A1C tests, which is a long-term measurement of glucose in red blood cells. Knowing a person's average, long-term glucose level can predict their likelihood of suffering other diseases such as kidney failure or stroke.
Cleveland Clinic knows the problem is multi-faceted. Patients must follow treatment regimes and choose healthy lifestyles, and physicians must have long-term data to tailor treatment. But, as Harris notes, if the patient doesn't know how they're doing at a macro level, it's more difficult for them to change their behavior.
"...That information is used to not only send alerts to the physician but also [to] the patient," Harris said. "They can become stewards of own healthcare at some level."
To more directly engage patients, Cleveland Clinic allows them to enter their own data via glucose readers, ether manually or having it automatically entered via a mobile device to a personal health record (PHR). Cleveland Clinic uses Microsoft's free HealthVault cloud service as its PHR. The HealthVault application can then transfer that data to the clinic's EHR for physician and data analytics use.
"The best way to correct glucose levels is to know what's happening with a patient when they're at home, not when they're in a doctor's office," Harris said.
Also being floated in the heathcare community are scalable, less expensive and more patient-centric community health record banks. Those banks are community organizations that put patients in charge of a comprehensive copy of all their personal, private health information, including both medical records and optional information added by the patient.
The patient explicitly controls who has access to which parts of the information in his or her individual account.
Voice recognition joins big data
But, before information can be shared, it needs to make it into EHRs. One way physicians and nurses can add their notes to EHRs is with voice recognition technology.
For example, the U.S. Army has an enterprise-wide license for Nuance's Dragon Medical 360 Network Edition voice recognition software for use with its AHLTA EHR and Essentris-Inpatient System. The U.S. Veteran's Administration also has 12,000 Nuance Dragon licenses integrated with VistA EHR system.
In many cases, a physician will use voice recognition to enter observations, prognosis and treatment into a patient's electronic record.
Dr. Walker, with the U.S. Army's Surgeon General's Office, uses voice recognition technology as he examines a patient to populate their record. A wide-screen monitor in his exam room allows the patient to view the data as it's being input so any errors can be corrected, he said.
Walker believes the real game changer in medicine will be an engaged patient, one who will enter his or her own data through the use of mobile devices. And that data can include not just medical information, but also lifestyle updates involving diet and exercise.
By having a full picture of a patient's lifestyle, doctors are better equipped to help patients avoid the onset of chronic illnesses. Then, once the data is in an EHR, big data analytics engines could offer physicians information about patients who may need to adjust their caloric intake, level of activity or the amount of sleep they get.
"The answer to the obesity problem is not the operating table, but the dinner table, and that's where we need to get to," Walker said. "In this country, we're putting billions of dollars into healthcare and our life expectancies are less than in countries that spend a fraction of what we do.
"We're really doing disease care and not healthcare today," he said.
This story, "How big data will save your life" was originally published by Computerworld.