In a serious commitment to take some of the hit-and-miss out of current practices that impact the rollout of new drugs, Diaceutics and BioReference Laboratories have signed a five-year collaborative agreement.
The agreement will give Diaceutics access to 50,000 patient samples a day through BioReference’s network of 30,000 healthcare providers. The goals of the just announced partnership are far reaching beyond just improving patient outcomes. These include:
The partnership will address a major challenge in the sector.
“It has been determined that 70% of healthcare decisions are enabled by testing. However, the primary economic focus for precision medicine is the financial return surrounding treatment, not testing,” Peter Keeling, CEO at Diaceutics, told CenterWatch. “As diagnostic companies do not financially benefit from therapy revenues, they are unable to invest the right amount to understand patient testing journeys and optimize that information.”
The company believes that there are significant patient benefits to creating a more balanced partnership between testing and treatment. And, Keeling explained, they aim to achieve real-world analysis through the laboratory partnership to highlight gaps in diagnostic care. This will better inform the investment required for improvement.
For example, Diaceutics estimates that pharmaceutical companies are missing out on more than 12% of cancer patients every year in the U.S. alone due to poor diagnostic planning. With 70% of new drugs in the next five years expected to be test-dependent, Diaceutics expects that percentage will increase.
Kevin Little is chief scientific officer at 3Dsignatures, which has developed a proprietary platform to scan clinical test results in search of more refined and targeted profiles that predict cancer progression and responses to treatment. He said he has seen a growing trend in such collaborations between analytics and clinical providers—and it’s overdue.
“I belong to the professional group Association of Strategic Alliance Professionals and have observed that traditionally big pharma and tech companies have a tough time understanding each other,” Little told CenterWatch. “What I find interesting is that at our annual conference this past year ... I finally had the realization that the two groups are in the same business and need to start working together.”
Big Data and predictive modeling—often generically referred to as artificial intelligence (AI)—make the collaboration between the two companies possible.
“The data files are both significant in size and complexity, and we have a dedicated Big Data team with the task of integrating each of the lab’s data together,” said Keeling. “Adding geographic and claims data, and analyzing for trends and statistical observations, predict the total testing narrative across the U.S.”
The announced project between the two companies is part of an emerging trend linking AI and healthcare delivery. AI collaborations are expanding rapidly in healthcare as companies find new Big Data and predictive capabilities to make better healthcare decisions. These partnerships range from large, well-known players to smaller entities.
For example, IBM Watson Health and MedyMatch, both AI players, recently announced an agreement for the launch of a platform addition to help ER doctors find brain bleeds sooner in reviewing trauma and stroke victim CT scans. Bayer Radiology just formed a partnership with the Connecticut Hospital Association to create a Big Data radiation dose registry to promote best and safe practices in mammography.
The goal of such collaborations is to improve patient safety, outcomes and control healthcare spending. Murt Abuwala, the founder and managing director of the Orbytel Group, which offers solutions to the pharma and biotechnology industry, believes that such partnerships are a step in the right direction.
“This collaboration allows for pharma companies, via Diaceutics, to have a more granular understanding of where there may be educational opportunities in diagnostics to improve patient outcomes,” said Abuwala. “If companies are aware of a specific potential gap and are equipped to act quickly, they will be able to influence patient outcomes.”
Applying AI to daily operations offers a new dimension in solving problems. Keeling said that, for example, almost all key biomarkers that guide key treatment decisions have issues ranging from slow turnaround times and false negatives to reimbursement issues and inter-lab viability.
“By mapping this out in the real world, as opposed to the controlled clinical trial setting, we can identify those gaps that impact or hinder access to getting the patient on the right therapy,” said Keeling.
He noted that pharma is willing to invest in education and support programs for labs, but they need to understand where the actual gaps are in the specific biomarker/drug combination.
“Our data helps illuminate the gaps in each of the diagnostic journeys for the patient,” Keeling said.
This article was reprinted from Volume 21, Issue 17, of CWWeekly, a leading clinical research industry newsletter providing expanded analysis on breaking news, study leads, trial results and more. Subscribe »