Re-engineering Cancer Clinical Trials at Scale


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’s about understanding that in the ebb and flow of life, nothing is stationary, and everything is linked.” The relationships between the modules of a system create a whole that cannot be understood by only analyzing its constituent elements.

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

Systems engineers are taught to think about all problems holistically, and then engineer individual components accordingly. This mindset is missing in clinical trial design and is one of the fundamental reasons that the clinical trials process is broken. Consider this: Over the last decade, 18 million cancer patients were diagnosed in the U.S., but only 0.1% were 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 denies too many cancer patients from hope for a better outcome.g

The life sciences industry will be better equipped to tackle the inherent challenges pervasive in oncology clinical trials using engineering principles, addressing individual components by considering their ramifications on the entire trial from the start. Nowhere is this clearer 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 unsolvable problem to clinical researchers, but that is because they are not thinking holistically about all the processes needed to identify, engage, and guide patients through enrollment and participation. Just as engineers don’t design for just one process without considering the whole system – i.e., building the cockpit of NASA’s Orion without thinking about how it affects the whole spacecraft – clinical researchers must consider how patient enrollment impacts the entire value chain from recruitment to retention to outcomes.

To solve problems, engineers also dive deep into all the possibilities for failure, taking into consideration every potential outcome for each decision. This is also critical to success in clinical trials, where there are many possible points of failure. Companies will make transformational change in clinical research when they apply an engineer’s mindset, thinking both horizontally across the entire trial process, as well as vertically to deeply analyze all potential points of failure.

New mindset + new technology = scalable solution

As science propels cancer treatments forward, clinical trials are increasingly designed around very small, genetically defined subsets of cancers which makes finding eligible patients difficult. Additionally, oncology trials typically require patients to be relapsed/refractory after standard cancer treatments or to have relapsed at least twice before they’ll be considered as candidates. If a patient makes it past these first hurdles, they face rigorous pre-screening. Oncology trials are notoriously stringent; in fact, 40% of patients with cancer trials available to them are not eligible to enroll due to eligibility requirements, according to an industry report.

In fact, a recent study found that roughly 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 the diversity of cancer demographics, further complicating patient identification.

Combined, these hurdles make patient identification and enrollment one of the biggest hindrances to oncology clinical research. Trial sponsors struggle with this challenge despite investing in various solutions, including many new and unproven approaches.

Some sponsors, for example, hire digital patient recruitment specialists who work to identify potential trial participants using widespread social media advertising to reach a larger pool of candidates. This is effective…to a point. It addresses only part of the problem and doesn’t take into consideration what happens after a patient has been identified.

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

Thinking about this problem like an engineer, we can develop a more complete solution that not only addresses patient identification but also considers how to best usher patients through the many pre-screening requirements for participation. These requirements, such as gathering medical records and getting various lab tests, can be complicated to navigate and burdensome, especially for the sickest cancer patients we are trying to help.

Next, there is the challenge of keeping patients actively engaged throughout trial enrollment, so they don’t drop out before they have even completed the screening. Engineers analyze and solve for these potential problems that others aren’t 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 place for further evaluation. Direct patient acquisition channels typically include referrals from call centers, patient advocacy groups, leads identified through digital advertisement, mobile application leads and public awareness events such as webinars and educational sessions. Indirect patient acquisition channels include referrals from providers, payers, next-generation sequencing vendors, and specialty pharmacies.
  • Patient record management – identifying the specific requirements for trial eligibility and ensuring patient data is extracted accurately from medical records to meet these criteria. AI can make this process faster and more accurate.
  • Comprehensive trial identification – considering all available trials while pre-screening cancer patients in case they are rejected from their first option. AI also plays a role here by automating the search across multiple trial 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, empowering patients to be re-considered for a trial if they can meet the criteria later and driving long-term improvements in overall population health as this transparency is applied across patient cohorts.
  • ‘Last-mile’ patient support – providing high-touch care for patients who are often overwhelmed by trials while they are also exhausted by the side effects of their treatment and 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 maintain their active engagement until the very last dose of their investigational treatment.
  • Monitoring and feedback – understanding the success of clinical trial enrollment and continuing to receive feedback from the patient on progression of the disease, clinical trial process and implications of clinical trial participation such as 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 the broken constituent processes like patient enrollment to systematize clinical trials. Combined with the ingenuity of science-minded clinicians, this new approach can help more patients get better medicines, faster.

Photo: Warchi, Getty Images

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