Re-engineering cancer clinical trials at scale | Jobs Vox


In his book, Applied Minds: How Engineers Think, Guru Madhavan explores the mental makeup of engineers. His framework is built around a flexible intellectual toolkit called Modular Systems Thinking. He says that “systems-level thinking is more than just being systematic; Rather, it is about understanding that nothing is constant in the ups and downs of life, and everything is connected. The relationships between the modules of a system form a whole that cannot be understood by analyzing only its constituent elements.

In other words, the whole is greater than the sum of its parts.

Systems engineers are taught to think holistically about all problems, and then engineer the individual components. This mindset is missing in clinical trial design and is one of the fundamental reasons why the clinical trial process breaks down. Consider this: In the past decade, 18 million cancer patients were diagnosed in the US, but only 0.1% … Offered clinical trials. At the same time, 66% of oncology clinical trials are closing prematurely because they cannot fill their trials with patients.

It makes no sense and deprives many cancer patients of hope for a better outcome. Yes

The life sciences industry will be better equipped to address the inherent challenges pervading oncology clinical trials by using engineering principles to address individual components by considering their impact on the entire trial from the outset. Nowhere is this more evident than in cancer patient-trial matching, recruitment and enrollment. Today this process is like finding a needle in a haystack.

finding a needle in a haystack

Identifying patients for oncology trials appears to be an insoluble problem for clinical researchers, but this is because they are unaware of all the processes needed to identify, engage and guide patients through enrollment and participation. Not thinking about it holistically. Just as engineers don’t design for just one process without considering the entire system—i.e., building the cockpit of NASA’s Orion without considering how it affects the entire spacecraft—clinical researchers must Consideration should be given to how patient enrollment affects the entire value chain from admission to recruitment. Retention of results.

To solve problems, engineers dig deep into all possibilities of failure, taking into account every possible outcome for each decision. It is also critical to success in clinical trials, where there are many potential points of failure. Companies will drive transformational changes in clinical research when they apply an engineer’s mindset, thinking horizontally throughout the entire testing process as well as deeply analyzing all potential points of failure.

New Mindset + New Technology = Scalable Solutions

As science advances cancer treatment, clinical trials are increasingly designed around very small, genetically defined subsets of cancers, making it difficult to find eligible patients. Additionally, oncology trials typically require patients to be relapsed/refractory after standard cancer therapies or have relapsed at least twice before they can be considered as candidates. If a patient passes these first hurdles, they face rigorous pre-screening. Oncology trials are extremely stringent; In fact, according to one industry report, 40% of patients with available cancer trials are not eligible to enroll because of eligibility requirements.

In fact, a recent study found that nearly 80% of patients with advanced non-small-cell lung cancer did not meet the criteria for the trials included in the study. As a result, 86% of those trials failed to complete recruitment within the targeted time. Clinical researchers are also tasked with enrolling patient populations that reflect a diversity of cancer demographics, further complicating patient identification.

Combined, these barriers make patient identification and enrollment one of the greatest barriers to oncology clinical research. Trial sponsors are grappling with this challenge despite investing in a variety of solutions, including many new and unproven approaches.

Some sponsors, for example, employ digital patient recruitment specialists who work to identify potential trial participants using extensive social media advertising to reach a large pool of candidates. It’s effective… to a point. It addresses only part of the problem and does not take into account what happens after the patient is identified.

Other researchers attempt to employ advanced technologies such as data science and artificial intelligence (AI) to mine patient databases and medical records based on trial eligibility criteria. Again, these technologies are powerful but don’t consider what happens to patients once they are identified.

By thinking about this problem like an engineer, we can develop a more complete solution that not only addresses patient identification but also considers whether patients are through multiple pre-screening requirements for participation. How to best bring These requirements, such as gathering medical records and obtaining various laboratory tests, can be complex to navigate and burdensome, especially for the sickest cancer patients we are trying to help.

Next, there is the challenge of actively engaging patients throughout trial enrollment so that they do not drop out before completing screening. Engineers analyze and solve these potential problems that other people are not thinking about, while clinical researchers are focused on trying to prove a hypothesis. The engineering-minded researcher does both – addressing all the pain points of patient enrollment, including:

  • patient identification – Analyzing all direct and indirect patient acquisition channels in real time and channeling to a centralized location for further evaluation. Direct patient acquisition channels typically include call centers, patient advocacy groups, leads identified through digital advertising, mobile application leads, and public awareness programs such as webinars and educational sessions. Indirect patient acquisition channels include referrals from providers, payers, next generation sequencing vendors and specialty pharmacies.
  • patient records management Identifying specific requirements for trial eligibility and ensuring patient data is accurately extracted from medical records to meet these criteria. AI can make this process faster and more accurate.
  • Comprehensive Test Identification Considering all available tests while pre-screening cancer patients if they are rejected by their first option. AI also plays a role here by automating search across multiple test databases that are challenging to navigate manually.
  • feedback capture Understanding why a patient was accepted or rejected can inform future patient recruitment efforts. New technologies provide transparency, empower patients to be reconsidered for testing if they can later meet criteria and drive long-term improvements in overall population health as this transparency is applied across patient groups .
  • ‘Last-mile’ patient assistance Providing high-touch care for patients who are often overwhelmed with tests while also exhausted by their treatments and side effects of the disease. In this “last mile”, one-on-one patient handholding can also serve to sensitively identify and eliminate any participation barriers, such as travel logistics and costs, and actively support them until the final dose of their investigational treatment. Partnership can be maintained.
  • Monitoring and Feedback – Understanding the success of clinical trial enrollment and continuing to receive patient feedback on the implications of clinical trial participation such as disease progression, clinical trial process and side effects.

Engineers see everything as a system, know how to design under constraints, and recognize the need for trade-offs. Adopting an engineering mindset in oncology research can fix all broken component processes, from patient enrollment to organizing clinical trials. Combined with the ingenuity of scientifically minded physicians, this new approach could help more patients get better drugs faster.

Photo: Varchi, Getty Images


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