Making the Connection for Clinical Trials – We Need to Do Better

By Ken Faulkner, PhD, SVP Clinical Trial Solutions, ConcertAI

The technology revolution has impacted almost every aspect of our lives. We have seen advances in communication, finance, education, transportation – even spaceflight. What seemed unattainable just a few years ago has become a part of our daily lives.

However, in our industry of clinical trials, the adoption of new technologies has been much slower.  Many of the technology and techniques used today are not significantly different from what we have been using for decades. This is particularly true when it comes to data collection and analysis systems.  More than two decades ago, it was a big step for our industry when Electronic Data Collection (EDC) systems were introduced. Yet we continue to see paper data collection methods being used in many studies. Imagine if our financial institutions, airlines, and universities persisted in the use of paper data systems.

The current global pandemic has brought to light many of these technology deficiencies in our clinical trial industry. Without the ability to conduct clinical trial visits at the clinic, we have been forced to rapidly pivot our procedures to support virtual visits and procedures. Many within our industry have praised the “rapid” shift to virtual visits. But to be honest, how revolutionary is this change? Video calls and online interactions have been for many years routine in most industries. While the pandemic has forced us to use these systems and tools, the truth is we should have moved in this direction a long time ago.

Clinical trials have improved in their use of electronic systems and advanced technology, but not as quickly as other industries. There have been efforts at standardization, but in many cases clinical trial data are collected and stored in a wide variety of different and nonintegrated systems. In effect, we have an “alphabet soup” of data systems in clinical trials – EDC, CTMS, RTSM, IVR, TMF, COA, RWE… the list goes on. As a result, we often see distinct systems each storing an instance of study data that may disagree with other systems. While each system fills a unique function within a clinical trial, it fails to exchange and synchronize key data, resulting in data discrepancies (which must be adjudicated) and loss of the ability to easily analyze data across multiple systems.

We have seen other industries adopt integrated systems, data standards, and analysis tools that are in common use today. The finance industry has taken the lead, with automated fraud detection tools, advanced security measures and systems which effectively communicate across companies and countries. The reality for clinical trials is quite different, with each study typically utilizing a unique set of technology tools with variable levels of integration.

We need to improve our ability to connect data from many different sources into meaningful outcomes. Patients, investigators, and study sponsors need more efficient clinical trials with access to advanced analytics based on fully integrated datasets. This is particularly important in oncology studies, where efficient studies are directly tied to saving lives. There are several areas where we can improve, including:

  • Faster identification and enrollment of study subjects: Finding qualified patients, gauging participation interest, performing and documenting informed consent, and performing screening and baseline visits is always challenging. These activities are some of the most expensive and time-consuming tasks, often leading to delays and cost overruns. Delays in recruitment and enrollment are directly related to prolonging study duration. Investing in systems that can access EMR systems, apply protocol inclusion/exclusion criteria, and direct prospective patients to additional information and consent documentation prior to their first visit can have a significant impact on study efficiency.
  • Use of External Control Arms: External control arms use real-world data (RWD) to enhance data from clinical trials. By reducing or eliminating the need to enroll patient controls, recruitment efforts can be targeted to active treatment arms, accelerating enrollment. This also reduces site costs, patient burden, and risk by eliminating clinic visits and patient management for the study control arm.
  • Expanding endpoints beyond progression and survival: As we have made progress at reducing cancer deaths, we now have more patients living with their cancer. This means moving our clinical trials beyond survival and tumor progression endpoints to assessing how patients can expect to survive with their cancer and cancer treatment. This will require us to expand our data collection techniques, including endpoints beyond survival, and develop tools that help assess what is important to the patient.

Such tools, systems, and procedures are available for us today to change the way clinical trials are done – we just need to put them to use. Stay tuned for future posts on how ConcertAI is transforming the clinical development paradigm.