If a key goal of a clinical study is to predict a drug candidate’s real-world effectiveness, then patient adherence to the regimen at the center of the study is crucial. If patients don’t take a drug as directed, as frequently as required, or they stop taking it altogether, the data won’t measure up.
To learn more about how trial teams can increase adherence, Outsourcing-Pharma checked in with physician Yossi Bahagon, co-founder of Sweetch, a company that uses artificial intelligence, applied behavioral science, and other tools to help increase adherence.
OSP: How does the gap between drugs’ performance in RCTs (randomized controlled trials) and in real-life use hurt pharmaceutical companies’ work?
YB: In general, RCTs are considered the best methodology to explore or examine the efficacy and safety of a medication. That being said, they are bound by various limitations.
First, RCTs must be conducted on a clearly defined patient population within the constraints of a very specific investigational setup. Trials must be performed with patient groups that, for example, fall into a specific age range, share a specific disease profile, or do not have common co-morbidities. Patients in these trials must adhere to a very specific follow-up regimen and are often supported by clinical trial coordinators.
RCTs are designed to measure a very specific outcome, and so they are, by design, very tightly controlled, and therefore an approximation of what one would expect in the real world. By no means are they a true measure of real-world dynamics.
Despite these limitations, RCTs are still the gold standard of pharmaceutical clinical trials today and are generally perceived as the best proxy for real life. But the gap between RCT results and real-world clinical use has been revealed time and time again. The efficacy results we observe in RCTs do not necessarily reflect reality because the trials are so hyper-controlled.
Real-world clinical practice has many more parameters involved that impact the effectiveness of a drug; parameters not captured in RCTs. As a result, it is possible that a certain medication will turn out to be highly effective in a specific patient sub-population, and less effective in others. These realizations are discovered only once the drug is used widely in clinical practice on a broad spectrum of patients – much broader than the carefully selected patients recruited for RCTs.
OSP: Your company uses AI and behavioral science to enable precision engagement. Can you share some examples of data sets that have been enlarged or corroborated thanks to this technology?
YB: Taking a look at research data, clinical trial data, or even real-world clinical data today, we see a variety of available parameters such as health records, and biological and biometric data. These data sets include information about a patient’s diseases, co-morbidities, medications, genomics, blood glucose, and blood pressure levels, amongst other measures.
However, the patient’s behavior and habits, their goals and aspirations, their struggles and triumphs, are completely ignored in all known data sets, despite the well-known fact that healthy vs. non-healthy behaviors directly impact clinical outcomes. For example, how much a patient sleeps or exercises impacts their heart rate and blood glucose levels.
Thanks to our unique and innovative technology, Sweetch can incorporate these behavioral measures, and enrich clinical data sets by pairing the measured physiological data with behavioral patterns. For example, if we look at two patients prescribed the same medication, we might see that one patient benefits significantly from therapy, while a second patient does not. A pharma company may have no obvious explanation for this phenomenon, but Sweetch could attribute certain behavioral patterns to seeing a better therapeutic effect.
Often, medicine does not consider lifestyle behaviors and how they affect the bottom-line clinical outcome. This is where Sweetch’s technology comes into play.
Capturing this new data set of patient behavior enables us to bring into the equation a set of metrics that to date have been completely disregarded, yet are highly significant. These behavioral data measures can differentiate between parameters that have not historically been clinically captured. Having this rich new data could influence how patients are treated, and even how RCTs are analyzed.
Another area in which this technology can significantly influence RCTs is with its precision engagement capabilities. By combining AI and behavioral science, we can increase the likelihood of a patient adhering to their prescribed treatment regimen – taking the medications analyzed in the clinical trials or adhering to a specific research protocol. One of the biggest challenges with real-world application of pharmaceuticals is patient adherence – behavioral data impacts the health effectiveness and cost-effectiveness of RCTs themselves.
OSP: Given your experience enabling more accurate real-world data sets, do you have any measurable insight into their added benefit for pharmaceutical companies?
YB: Our primary focus at Sweetch is seeing the user as a person, not just with their patient needs. Patients are human beings with lives, families, careers, and needs that go beyond disease management. We need to shift our view from patient or “medication-taker” to that of a holistic consumer – a partner whom we need to support in improving their quality of life and health management.
Enabling more accurate real-world data sets will help pharmaceutical companies bring more value to their patients by finally being able to see them from a deeper perspective. Pharma understands they must provide added value to the patient, and this comes in the form of attempting to forge personalized and proactive relationships with the people consuming their medications.
Sweetch’s technology helps pharma companies transition from being perceived as just selling medication to genuinely caring about their patients. This, in turn, strengthens their customer relationships and builds brand loyalty, while also bringing great value directly to patients.
On a more financial level, the combination of AI and EI in precision engagement solutions provides added value to pharma through four specific channels:
- The first is reduced customer acquisition costs through the unique offering of a more holistic intervention and the differentiated positioning it creates.
- Second, it increases retention rates. People adhere to their medication prescriptions for longer as a result of precision engagement.
- Third, because adherence is increased, patients take their medications more regularly. The more frequently a patient takes their medication, the greater the effect of that medication. Precision engagement solutions can monitor the impact of increased adherence.
- Finally, these platforms provide anonymized data to pharmaceutical and medical device companies that offer significant insights into the behavioral patterns of the populations they serve.
OSP: Do you have anything to add?
YB: Society has shifted in a significant way, in that patients now require personalized, proactive, and holistic treatment modalities. We must work together to provide patients with more user-centric healthcare solutions to truly see them in their entirety and optimize treatment modalities to fit each individual. Considering patient behavioral patterns is just the first step in enhancing treatments and their efficacy.