The challenges of accurately identifying and enrolling patients in clinical trials are well documented. For example, 55% of clinical trials are terminated due to failure to meet enrollment targets1, and 11% of clinical trial sites never enroll a single patient2. These delays can be costly, resulting in potential estimated losses of $600,000 to $8M per day3.
While many aspects of the clinical trial process have become more streamlined, the patient identification process still largely relies on inefficient practices. Keyword searches, manual reviews, outdated reports, and other time-consuming and expensive methods are commonly used to find eligible candidates for enrollment.
These processes have recently been modernized with a new, automated way to identify patients for clinical trials and identify potential safety concerns. Introducing VigiLanz Research, a patient identification solution developed by clinicians that quickly and accurately identifies more eligible candidates for clinical studies in near real time.
The technology at the core of VigiLanz Research leverages over 20 years of transforming complex patient data into meaningful and actionable alerts. By integrating directly with a trial site’s EHR system, VigiLanz Research uses intelligent rule sets of study-specific inclusion and exclusion criteria to notify researchers anytime a match is located within a hospital network — helping to ensure that every eligible patient who walks in the door is identified.
In addition to its core technology, VigiLanz Research also offers capabilities for safety reporting connected with clinical trial patients. The solution runs safety rules in the background that automatically populate a safety report based on trial needs. This process gathers detailed information about patients, whether they were identified through VigiLanz Research or a subset of otherwise identified patients. With this feature, researchers can be confident that safety concerns are being monitored and reported accurately, without having to manually review each patient’s data.
In addition to significant time-savings and workflow improvements for research teams, sponsors and CROs gain enterprise-level visibility into patient identification status across multiple trials and locations.
The results of moving from manual to automated processes can be dramatic. When Sharp Grossmont Hospital began participating in a complex clinical trial in early 2020, it quickly became apparent that they were going to face patient identification challenges. The trial was designed to evaluate the safety and efficacy of a new drug to reduce the risk of major cardiovascular events in patients with acute coronary syndrome, and its complex protocol made clinical trial enrollment extremely difficult.
As a result of the VigiLanz Research partnership, Sharp Grossmont Hospital reduced the time needed to screen and enroll patients. The time savings and workflow improvements also positively benefited the clinical research team. Overall, the partnership increased the number of patients screened, cut the time spent screening patients in half, and identified eligible patients faster.
Kyra Rashid, BS, MCR, Clinical Trial Specialist at Sharp, said the solution helps the organization identify eligible patients more quickly, including patients who might have otherwise been missed. “The technology alerts our team as soon as eligible patients are identified, based on the study-specific inclusion and exclusion criteria,” she said. “It screens the entire hospital patient population, which saves us a significant amount of time that can then be reallocated to other initiatives.”
By empowering researchers to screen more qualified candidates, VigiLanz speeds up the clinical study process. For hospitals, this increases the likelihood for clinical trial success. For sponsors and CROs, this potentially saves millions of dollars and helps get products to market faster.
Most importantly, VigiLanz connects real people with opportunities for life-changing treatments that could help them live longer, healthier lives. For more information about finding patients in clinical trials in real time, visit our website at vigilanzcorp.com.
1. Desai M. Perspect Clin Res 2020;11:51–53.
2. Tufts Center for the Study of Drug Development. Impact Report. 2013;15.
3. Mette Brøgger-Mikkelsen MA, et. al. J Med Internet Res 2020;22:e22179.