As a clinician and a trials investigator, I’ve made several limited observations that are both encouraging and concerning, all at the same time. I have had the growing sense that many of the existing workflows required for clinical trials are, at best, disjointed and prone to inefficiencies and recording errors. For example, there are the current approaches used to identify and pre-qualify trial participants for study inclusion, and there are many of these. Among them is the employment of social media such as Facebook, in-house recruiters, third-party recruiters, review of databases from previous trials participation, and use of existing private practice patients (to name but a few). As a broad collection, these approaches have demonstrated a rather poor track record when one examines the percentage of screen fails that occur on the day of intended study enrollment. Also, most traditional systems for maintaining trial records are prone to mistakes (either by omission or commission) that are vulnerable to subsequent audits at many possible levels. This is especially concerning for the responsible principal investigator (PI) who is most at risk. Sometimes the demographics have not been thoroughly examined. Other times, the prospective participants come to the investigator with obvious disqualifications based on their inability to meet the study’s inclusion/exclusion criteria.
Then there is the large issue of sustainment of study participants, which has been cited by many as one of the two or three greatest causes of the inability of a study site to meet its target numbers. Each dropout and the failure-to-finish participant is costly, both to the sponsoring pharma entity and the site conducting the trial.
At this point, I wish to thank my own passionate development team at Circlebase for creating an elegant solution that addresses these issues. They’ve appropriately named this solution, the “Circlebase ACTM” (“Automated Clinical Trial Matching”). I will leave it to the reader to explore more of the power of this solution at our website, Circlebase.com. In a word, it is a collection of functionalities that automatically improve the rapid selection of a sustainable cohort for virtually any study. In doing so, each potential participant is evaluated by comparing his/her own health information against the study’ specific inclusion/exclusion criteria. It does so by completing a simultaneous automated review of the patient’s health record, including both structured and unstructured data drawn from virtually any EHR.
Doing so makes for optimal accuracy in identifying specific participants with the highest match score, thus reducing the number of patients who fail their actual enrollment encounter or later drop out. The beauty of this solution is that because it utilizes NLP (natural language processing), ML (machine learning), and the requisite ontologies, it becomes ever more “intelligent” as it is used. Most importantly, our ACTM product accomplishes these improved recruitment and sustainment functions using an automated platform, and its inherent efficiencies are reflected as increased revenue for the customer.
Most importantly, our ACTM product accomplishes these improved recruitment and sustainment functions using an automated platform, and its inherent efficiencies are reflected as increased revenue for the customer. I have only introduced our ACTM in this blog. However, our team is zealous about the power of this product, and we would appreciate the opportunity to further demonstrate this at a time convenient for you. To schedule a brief introduction and demo, please contact us at firstname.lastname@example.org
All the best and more to come!
Lanny Turner, M.D.
Lanny Turner, M.D.