Clinical trial sites hold a great deal of responsibility for the
success of any given trial. Keeping track of patient compliance is a big
part of that responsibility and one that keeps a lot of site leads up
at night. Having visibility into patient behaviors, particularly in
between clinic visits, is challenging. Yet it’s critical that sites stay
on top of their care plan performance in order to ensure new
medications are tested safely and accurately. Following is what we have
observed through leveraging digital tools that will help sites optimize
data quality while improving continuity of care for patients between
their visits.
The Power of Patient Selection
While conventional wisdom may be that people are unpredictable, we’ve
actually found that the opposite is true. Any given individual’s future
behavior is likely to be consistent with their past behavior, and a
recent study1 concluded that future patient adherence is
highly predictable. We at AiCure, along with our partners, have seen a
great deal of success through implementing placebo lead-in periods
before patients are randomized. By establishing baselines of behavior
during these lead-in periods, we can predict which patients will perform
the best in terms of care plan compliance and providing the kind of
data necessary to detect a signal. Additionally, deploying computer
vision tools such as AiCure’s digital biomarkers during screening,
present the opportunities to better understand facial or voice cues that
may aid in identifying optimal patients prior to randomization.
Training Beyond Dosing Instructions
Despite all the promises digital tools hold with enhancing data quality and odds of overall trial success – none of this is feasible without strong patient onboarding by trials sites. In addition to explaining the basic elements of trial participation such as dosing instructions and how to fill out diaries, it is vital for sites to educate patients about the goals of the research. Sites should talk to patients about the impact of skipping a few doses or filling out diaries at the last minute (and likely forgetting important details when doing so) not only on their own treatment but the ability to better understand the efficacy of the investigational product for future patient use. Non-adherence negatively impacts researchers’ understanding of how the drug works, adds cost and time to trials and can cause trials to fail. Patients who know how their non-compliance affects the potential patient population that would benefit from a new therapy are much more likely to stay on task. We know that an ideal clinical trial patient is one who is motivated. But that motivation needs to surpass the desire only to get healthier themselves. If a patient understands how their behaviors during a trial affect the research and therefore impact the potential of millions of other people who may benefit from the drug or therapy, they are much more likely to dedicate themselves to the care plan.
Technology that Empowers Patients and Informs Clinicians
After patients are trained and return to their homes and daily lives,
technology should be employed to keep that strong communication between
patients and sites. Clinic visits are time-consuming and don’t always
provide clear visibility into what’s happening with the patient when
they aren’t in the clinic. Because of this, clinicians at sites are
forced to treat all patients similarly. Super reliable patients then can
become irritated by what they feel is too much time spent in the
clinic, whereas struggling patients may not get enough time and
attention with clinicians. By utilizing technology like AiCure Patient
Connect, clinicians receive a constant stream of data in real-time,
giving them visibility into the behaviors of each patient. In terms of
clinical visits, this helps clinicians optimize and individualize
patient face time. It also provides near immediate insight into
adherence issues, helping the site team to intervene as necessary while
enabling earlier identification of potential adverse effects.
Clinicians at trial sites have a lot of balls in the air. Through
smart patient candidate screening, adequate education and the use of
simple, easy-to-use patient engagement technologies, sites can decrease
the burden of adherence monitoring and improve the quality of patient
data. For more information on how AiCure can configure a solution to
increase site productivity and reduce patient burden, watch this webinar.
1V. Koesmahargyo, et al. Accuracy of machine learning-based prediction of medication adherence in clinical research
