Most drugs in the United States undergo ten to fifteen years of clinical testing before they are approved by the Food and Drug Administration (FDA). Usually, clinical trials involve four phases designed to test the efficacy of the drug in human populations. Clinical trials are also a notoriously time-consuming process, often involving the mobilization of large sums of monetary investment.
copyright by njitvector.com
Specifically, some estimates put the clinical trials market as worth almost 65 billion dollars. The laborious process of clinical trials, while designed to ensure the safety of a drug before allowing it to enter the market, can also inadvertently delay a patient’s access to potentially life-saving medication. Therefore, the clinical trials market is a space in need of a streamlined overhaul. Into this space enters the seemingly boundless potential of ().
Much has been reported regarding the diagnostic potential of to assist physicians with parsing massive amounts of data, including historical presentation of symptoms of different diseases from thousands of patients. However, can also prove to be an advantageous addition to the clinical trials process. Given that clinical trials include several phases, preceded by a massive mobilization of matched volunteers, there are several spaces in which can be integrated to streamline the process.
Part of the delay and costs associated with drug research and development is identifying and matching volunteers for each phase of the clinical trial. The need for a more effective matching system is evident, as a report from Cognizant reveals that approximately 80% of clinical trials are unable to meet enrolment deadlines. In this space, can prove enormously helpful, as the algorithmic nature of can easily process copious collections of medical records, and sort and match those patients best suited to particular clinical trials. The magnitude of the impact
read more – copyright by njitvector.com
1 Comment