You know what it’s like. You take on a clinical trial convinced that you have enough of the right patients to meet recruitment targets. But you haven’t gotten very far down the funnel when you realize that you may have greatly overestimated the cohort of patients that meet trial criteria. Its common practice for those involved in finding patients for clinical trials and research to rely on their impression of viability within their patient population; but as in/exclusion criteria gets narrower and narrower, there’s an opportunity to rely on your data to determine the likelihood of success in recruitment instead.
A recipe for disaster
Lasagna’s Law or “the incidence of patient availability sharply decreases when a clinical trial begins and returns to its original level as soon as the trial is completed,” is a phenomenon that’s been around since the 1970’s. In the last few years, we’ve seen some innovation in the recruitment space but those solutions still rely heavily on manual processes that include clinician/investigator intervention and time.
It’s estimated that over 70% of the patients that eventually enroll in clinical trials are within an investigative sites’ existing patient population and yet almost 40% of sites under-recruit and more than 10% of sites fail to recruit any patients at all. So perhaps the problem isn’t entirely about a sheer lack of eligible patients, but accessing the data you need to filter patients against trial criteria.
Say you need to enroll 100 female patients between the ages of 40 and 65 into a diabetes study. Your structured data can offer you a top-of-the-funnel idea of how close you can get to recruiting 100 patients. But you also need to know: BMI, blood glucose levels, history of cardiac conditions and incidences of corticosteroid use to know how many of the patients you’ve identified meet trial criteria. This information is stored as unstructured data or clinical narrative and when you’re able to access it, can give you the whole picture of a patient and therefore, their eligibility for a trial. But again, you’re relying on clinician time and resource to manually review copious amounts of unstructured patient records – a strategy that doesn’t get you closer to recruitment targets fast enough.
Fortunately, there’s a paradigm shift in patient recruitment. What if you had meaningful, actionable insights into your patient population at your fingertips that you could use to make informed decisions about whether to take on a study and whether you could meet enrollment targets within the stipulated timeline and budget?
Hindsight versus foresight
It’s often said that “the best time to plan a controlled trial is after the trial has finished” because all the questions you need to be able to answer before starting a trial have already been answered. But at that stage, you’ve already missed deadlines, possibly gone back to the Sponsor to ask for amendments to in/exclusion criteria, gone over budget and/or exhausted clinician resources. What you really need is to be able to answer feasibility questions with concrete data before the trial starts.
Clinithink’s CLiX ENRICH is a game changer in clinical trials because it automates the search and pre-screening of patients against trial-specific criteria. That means you can process millions of unstructured patient records in hours and be left with a list of potential patients to enroll ranked in order of eligibility.
Case studies have shown that using CLiX ENRICH to truly automate the pre-screening stage has yielded 10X the amount of quality, eligible patients in ¼ of the time it takes to do so manually. This marked reduction in time, manual effort, errors and educated guessing can only have a positive impact your clinical trial enterprise.
Study feasibility is a pretty ambiguous term that’s used widely but understood differently from person to person. One way to set benchmarks in determining feasibility is to use data – structured and unstructured – to offer a more precise approach than educated guessing. Enlisting CLiX ENRICH to determine recruitment feasibility is only the start of broader data-driven study feasibility that gives investigators the information they need to best use their judgement, while leaving the information mining to a tried and tested technology.
Sheryl Lowenhar, MBA, RPh, is Vice President of Sales and Marketing for Clinithink