The Value of Patient Engagement: How Sites can Reduce the Burden of Non-Adherence
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