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Accenture and the National Cancer Institute held a panel at the American Society of Clinical Oncology (ASCO) annual meeting to discuss personalized medicine and offer insights into more efficient clinical trials for the life sciences industry.
Two panels were convened to discuss the challenges of this new era in depth. The first panel was on diagnostic tools and how they are used in the real world, with a focus on efforts coordinated at the national level. The second panel focused on contemporary models of observational trials and their potential.
Both discussions included qualified experts from academia, industry and public agencies and were further augmented with questions and insights from the diverse group of session attendees. These conversations are summarized in the executive summary document.
Scientific advances of the last decade in the medical environment have helped identify the underlying causes of major diseases—leading the way towards more personalized medicine. In this new era, each patient requires adapted treatments, drugs and therapies tailored to his or her unique disease and medical traits. And furthermore - cancer is the most advanced arena in which personalized approaches to therapy are being are addressed.
In this age of personalized medicine, the first step is to identify patients whose cancers have specific mutations that can be targeted. Our goal is to deliver the right drug to the right patient at the right time. In order to do this, cancer centers globally must address multiple stakeholders with different needs, including patients, researchers, and treating physicians.
Payers and regulators are increasingly expecting pharmaceutical companies to prove that their drugs work not only in controlled clinical trials, but in the real world as well. As a new field of investigation, observational trials are still not completely defined and formatted. To successfully measure the impact of cancer drugs in real life settings, specific rapid learning systems, such as CancerLinq, are being developed. The goal of such databases is to bring individual patient EHRs into large data warehouses, transform the data into a common model, aggregate and de-identify the data, and then share the data with various users for different purposes.
August 20, 2014
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