Real-world data gathered directly from EHRs and other data sources, paired with advances in machine learning, will be crucial for architecting the next generation of successful clinical trials. As the volume, velocity, and variety of real-world data reaching the agency increases.
- We have an opportunity to use new software-based machine learning algorithms – like natural language processing or deep learning – to help develop regulatory science tools like surrogate endpoints or digital biomarkers that can be used to guide more efficient development programs.
- Data-driven protocols and strategies powered by advanced AI algorithms processing data automatically collected from mobile sensors and apps, electronic medical and administrative records, and other sources have the potential to significantly cut trial costs.
- As a result, fewer patients are needed to generate statistically significant study data, and fewer patients drop out. Adopting these novel innovations does present challenges, with developing analytics that generate actionable clinical insights from big data high among them. Nonetheless, there exists significant potential for transforming trials
AI and big data help gather and monitor data more accurately as well. In the past, researchers relied heavily on verbal or written evidence from patients at clinical visits and direct clinic observations to assess patient progress.
- This subjective evidence can be unreliable and not provide enough information for analytics and decision making. Moreover, frequent clinic visits can add to patient burden, causing dropouts.
- Gathering real-time, real-world patient data with wearable devices, on the other hand, can help produce consistent, objective evidence of actual disease states and impacts of treatment symptoms.
- This data includes heart rate, blood pressure and movement collected 24/7. It is much richer and more detailed than data collected in the clinic, making it much more reliable.
- AI analysis of live remote data also can detect when patients may not be compliant, allowing clinical personnel to intervene before a patient’s data must be excluded.
- AI-enabled trial management systems can help keep patients engaged. Technologies such as telehealth and digital reporting apps as well as wearables, allow for real-time engagement and communication, and support patient-centric trials.
Patients can send feedback on treatment symptoms and manage medication intake, and can share information with researchers, reducing or eliminating the need for patients to travel to sites, which increases patient adherence and compliance. Moreover, reducing the frequency of clinical visits can lower site costs and improve the quality of patient experience, for example by reducing the number and length of clinic visits.
How it Works?
AI analysis of RWD-generated by mHealth and wearables not only allows for the monitoring of objective high-quality data in real-time as patients live their lives, but also helps find relationships among masses of data not possible using human interpretive skills alone. With advanced analytics, researchers can gain deeper insights into how a treatment affects symptom progression or quality of life. Moreover, expertise in machine learning can help to develop novel endpoints.
- Intelligent & Personalized Patient Retention and Engagement for Clinical Trials and Beyond the Pill
- Adaptive Personalized Patient Engagement We Know Each Patient
- Patient App & Site Dashboard – patient-first mobile platform and intelligent site dashboard together support each patient’s complex journey through the clinical trial and her care journey post-market. The patient application notifies patients about their tasks and site visits at the right moment and informs them every step of the way through personalized conversations and curated content. The site dashboard allows site staff to monitor patient adherence and engagement in real-time, and intervene in a timely manner to prevent dropouts.
- Personalization & Prediction Engine – patient app and site dashboard are powered by its proprietary data-driven AI engine to improve engagement and retention.
The AI engine continuously learns each patient’s unique behavioural and lifestyle profile, and adapts engagement and adherence strategies to seamlessly integrate with the patient’s daily life. It additionally predicts and alerts site staff in real-time about patients at risk for dropout.