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KM bolsters clinical trials

This article appears in the issue June 2010 [Volume 19, Issue 6]
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The life sciences industry spends more than $30 billion a year on clinical trials in the United States, yet nearly 90 percent of trials fail to meet their timelines or budgets, researchers say. Often they fail to enroll enough patients with the condition they are studying.

More efficient clinical trials management has the potential to speed up the delivery of new drugs and medical devices, so pharmaceutical companies and academic research centers are always looking for ways to optimize their protocols and identify and enroll patients faster. It’s an area ripe for knowledge management solutions.

One positive development has been the shift from paper-based studies to the use of electronic data capture (EDC) systems from companies such as Phase Forward and Medidata Solutions Worldwide for collecting and reporting data once patients are enrolled in a study. But there are still many aspects of the clinical trials process that automation, integration and knowledge management solutions can spur.

Determining eligibility

The rapid increase in the deployment of electronic health records (EHRs) has the potential to play an important role in clinical research. As more academic medical centers develop sophisticated EHRs and data warehouses, they are seeing the potential to mine those records for possible trial participants, and several groups are working on moving data between EHRs and EDCs.

Drug companies such as Genentech have started conducting pilot projects focused on leveraging EHRs in direct support of their drug development programs. They focus on mining clinical data to better understand targeted patient populations and using real-world clinical data to determine the impact of specific criteria on the feasibility of a protocol. The pharmaceutical companies are having study sites identify potentially eligible patients directly from their EHR for proactive patient recruitments.

“Previously going through the inclusion and exclusion criteria to determine whether a patient was eligible was a time-consuming process of gathering information,” says Judy Hanover, research manager at IDC Health Insights. “Now a lot of that detail information that might rule them out of a study, such as the medications they are on, can be sorted using the EHR. It can help you find more potential patients, and it can streamline the process.”

One academic medical center seeking to use its patient data to improve its clinical research is the Mayo Clinic in Minnesota, which has spent five years building a data warehouse called the Enterprise Data Trust (EDT), which includes information on more than 7 million patients. Mayo has teamed with startup company Centerphase Solutions (site under construction) to review study protocols and identify Mayo patients who potentially qualify for enrollment in a specific clinical trial.

“We all want to find new drugs for patients. We always want to build a better mousetrap,” says Dr. Wayne Nicholson, assistant professor of anesthesiology and pharmacology at Mayo Clinic, “but if we need 20 patients for a study and only end up with two enrolled, it is very expensive both for Mayo and the industry. So we are working harder on that initial search and feasibility. We are looking at doing further cuts on the data, such as distance from Mayo, to help determine whether we will really get the right number of patients enrolled.”

Identifying sites

Relatively few academic medical centers have clinical data systems as sophisticated as Mayo’s EDT, according to Centerphase CEO Gary Lubin. “There is real value in querying that data to look at patients’ characteristics to see if they lend themselves to certain studies,” he says.

Centerphase’s goal, according to Lubin, is to help Mayo and other academic medical centers with trials administration and a structured methodology that both validates protocols and is more predictive about subject enrollment. “We use our proprietary technology to collect and analyze information to determine likely screen-out rates and give more accurate figures on what likely enrollment rates might be, as well as suggest risk mitigation approaches,” he explains.

Mayo has a minority stake in Centerphase, which will act as a middleman between biopharmaceutical customers (including contract research organizations) and academic medical center partners, with the goal of making them more successful in executing clinical trials.

“By identifying sites that are best suited for a specific study, we create the greatest chance of successful study execution,” Lubin says. “That, in turn, will lead to direct cost savings and accelerate the introduction of new treatment options for patients.”

Nicholson says Mayo would develop metrics to measure whether the increased use of analytics is having the desired impact on clinical trials. “We can feed that information back to the industry,” he says. “We can help develop protocols upfront, and if we can move that process along faster, it would be better for everyone.”

Impact of widespread EHRs

Beyond large and sophisticated academic research centers, the ubiquity of EHRs should help smaller hospitals and physician practices participate in research projects.

“Big academic research centers like Mayo, Johns Hopkins and Stanford have these great data repositories, but the potential is that now so will other smaller community hospitals, and that is where the bulk of mainstream American patients are seen,” says Dr. Harry Greenspun, chief medical officer for Dell Perot Systems’ healthcare group. As community hospitals successfully implement EHRs, they will start to build the types of connections with pharmaceutical companies that academic medical centers have, he adds.

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