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Research plays a critical role in identifying new treatments, developing innovative care models, and improving existing therapies. It provides the evidence base required for healthcare professionals to make informed decisions and offer the best possible care to patients; provides compelling evidence to health plans, employers, and other payers who are considering a new innovative product; and, when appropriate, assures regulatory bodies that new approaches are safe and effective. Without robust and timely research, the speed of innovation is significantly hampered, and breakthrough innovations often take years to get into the hands of the people who need them.
ieso recently conducted a UK-based study with 300 participants to evaluate a digital program for anxiety, worry, or stress. Unlike Randomized Controlled Trials (RCTs), where participants are randomly assigned to a waitlist, placebo, or sham control group, all study participants were given access to the digital program. To assess the program's effectiveness, outcomes were compared with three other patient groups using existing real-world therapy data:
These control groups were propensity-matched, which means that study volunteers were matched to people in the control group on key factors such as age, anxiety and depression severity, as well as the presence of chronic health conditions. While this approach may not eliminate all sample biases that are controlled for by randomization, it provides reasonable assurances that observed outcomes are not due to sample characteristics or other threats to validity.
In addition to expediting recruiting (the study was completed in less than 12 months) and reducing costs (because less time requires less resources, and fewer participants were needed to adequately power the study), we believe this approach is more ethical. All who want access to the intervention receive it immediately, instead of being assigned to languish on a waitlist or receive a sham intervention while deferring access to something that is likely to help them.
Creative study designs and approaches aren’t the only research method innovations to get novel solutions in the hands of patients faster. Data scientists leveraging technology like machine learning and AI to analyze patterns in aggregated, de-identified data also has the potential to speed innovation. For example, ieso has developed proprietary tools that use machine learning to automatically label every element of therapy we give to each patient (in the UK, ieso is a CBT therapy provider under its Talking Therapies program). This enables the measurement of various aspects of therapy sessions, such as understanding a patient's needs, delivering different therapy protocols, and evaluating progress.
Using these methods, ieso has made significant breakthroughs in understanding how mental health and treatments are understood, for example isolating specific elements of therapy associated with better patient outcomes, developing an algorithm that can identify different types of depression from patients' mental health measures, and building models using health, care, and socio-demographic data to predict patients' pathways to recovery and identify those needing additional support. Most recently, ieso released results showing that an AI-driven digital program delivered results comparable to traditional human-led therapy for generalized anxiety.
In a world where rapid advancements in technology and healthcare have become the norm, the pace of research methods has failed to keep up, causing significant delays in delivering crucial interventions to those who need them the most. Traditional approaches are often too slow to address the immediate needs of the population.
Mental health conditions are on the rise, with over 301 million people living with anxiety disorders globally. The growing demand for mental health support, combined with limited therapist availability, means that many people struggle to access the help they need, when and where they need it. Despite the availability of over 20,000 mental health support apps, these tools are rarely tested for safety, effectiveness, and engagement, leading to low usage and questionable efficacy.
The first wave of digital health interventions had little or low-quality research to support their safety and efficacy. However, times have changed, and there is now an expectation that new technologies be accompanied by evidence demonstrating their safety and effectiveness. No or low-quality evidence or speed should not be a dichotomy: it is imperative that evidence generation is optimized to bring high quality programs to market as quickly as possible.
The urgency of human suffering demands a swift and effective response from the research community. Traditional methods, while foundational, are no longer sufficient to meet the pressing needs of patients. By embracing new technologies, fostering collaboration, and reimagining the research process, we can accelerate the pace of discovery and bring much-needed relief to those who suffer. We’ve made tremendous strides in embracing care innovation, it is now time to embrace innovation in research methods so that we may deliver new products and services we can be sure are safe and effective.
To learn more about ieso, visit www.iesogroup.com. Follow Dr Dietz on LinkedIn.
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