Decentralisation and Democracy: A New Deal for Real World Data in Rare Diseases

In my previous blogs, I explored the challenges of generating real-world data (RWD) for rare diseases, focusing on the issues of patient dispersal and the potential of the European Health Data Space (EHDS) to improve data sharing and standardization across Europe. While the EHDS offers a promising step forward in one geography, it’s clear that addressing the complexities of rare disease research requires more than just one solution. In this post, I will explore how novel approaches to collecting RWD can help solve the rare disease data puzzle.

Why Now?

The field of life sciences has traditionally been slow to adopt new methods, often relying on established hierarchies of evidence when assessing the appropriateness of the approach and thus, perhaps unintentionally, creating barriers to innovation. While less traditional approaches to drug development have always been more common in rare diseases, recent years have seen a surge in creativity in generating RWD. The reasons for this are clear: technological advancements, regulatory support, and the unforeseen impact of the COVID-19 pandemic have all played a role.? These developments have improved both the logistical aspects of data collection and expanded the sources of data available, significantly impacting how we generate RWD in rare diseases.? And in turn, two trends are apparent – decentralisation and democratisation of RWD collection.

Let’s Decentralise

Traditional data collection methods in clinical trials and observational studies rely on finding enough healthcare sites to recruit sufficient patients, which can be challenging for rare diseases due to the dispersed and small patient populations. Maintaining participation over time is also difficult, particularly in observational studies where patient attrition is a common issue.

Decentralised approaches have emerged as a solution, increasing accessibility and inclusivity for geographically dispersed patients by minimizing the need for travel. By leveraging digital tools to collect data remotely, decentralized trials reduce the physical, emotional, and financial burdens on participants, which is especially important for those with rare diseases. This approach not only enhances data collection by capturing real-time information in naturalistic settings but also helps retain patients, ultimately providing richer datasets that better reflect patients' everyday experiences and outcomes.? In other words, the infrastructure for data collection is growing exponentially, which enables the subsequent decentralization.

Let’s not overlook another important aspect: decentralised approaches rely heavily on trust in individuals to adhere to research protocols. This makes RWD generation particularly well-suited for decentralisation, as it inherently focuses on observation rather than experimentation. Wearable devices and other digital tools continuously collect data without requiring active intervention from participants, making them ideal for capturing real-world insights.

What are the examples? The Apple Heart Study, conducted by Stanford University in collaboration with Apple, leveraged the Apple Watch to identify irregular heart rhythms, including atrial fibrillation, in a fully decentralised format. This study demonstrated the feasibility of large-scale health monitoring using wearable technology. Similarly, the NIH’s All of Us Research Program is a decentralised observational study that aims to collect health data from over a million participants to better understand how lifestyle, environment, and biology affect health outcomes, thereby creating a diverse dataset for personalized medicine research. In the UK, GRAIL ’s NHS-Galleri trial represents another decentralised effort, evaluating the effectiveness of the Galleri blood test in detecting 50+ early-stage cancers. By utilising mobile clinics to reach participants, this study seeks to determine whether this innovative genomic technology can detect cancer earlier, potentially improving treatment success rates.

Let’s Democratise

Patient-generated health data (PGHD) refers to health-related data created, recorded, or gathered by patients or their caregivers. In rare diseases, PGHD is particularly valuable as it facilitates continuous, longitudinal monitoring of disease progression and treatment effects, which is crucial for understanding conditions that evolve over time. Importantly, many endpoints critical for measuring outcomes in rare diseases are subjective, such as pain, fatigue, or quality of life, and require direct assessment from the patient.

By allowing patients to contribute their own data, this approach empowers them to actively participate in their care and research, leading to increased engagement and more relevant data collection. It captures the individual variability often seen in rare diseases, supporting personalized care and treatment plans tailored to each patient’s unique needs. PGHD offers insights into quality of life, treatment adherence, and day-to-day symptoms, helping researchers understand the real-world impact of the disease and its treatment.        

In rare diseases, where clinical endpoints often involve subjective measures and are specific to each condition, PGHD becomes invaluable. For example, in Duchenne muscular dystrophy, PGHD can track the progression of muscle weakness through patient-reported outcomes on physical activities, fatigue levels, and respiratory function, all of which provide crucial data on disease progression and treatment efficacy. In rare metabolic disorders like Gaucher disease, PGHD might capture daily pain levels, gastrointestinal symptoms, and the ability to perform everyday tasks, offering insights that go beyond traditional biomarkers to reflect the patient’s overall well-being. In fibrodysplasia ossificans progressiva, a disorder where soft tissue progressively turns into bone, patient-reported flare-up episodes, pain severity, and mobility limitations are critical for understanding the effectiveness of therapies and the real-life impact on patients.

Similarly, in rare neurological conditions such as Rett syndrome, a neurodevelopmental disorder, PGHD can be used to document seizure frequency, communication abilities, and daily living skills, providing a comprehensive picture of the patient’s functional status and response to treatment. In hereditary angioedema, a condition characterized by unpredictable swelling attacks, PGHD helps record attack frequency, duration, and severity, which are vital for tailoring prophylactic treatments and managing acute episodes. For patients with Ehlers-Danlos syndrome, a connective tissue disorder, tracking joint pain, dislocations, and skin elasticity through PGHD enables more personalized management strategies and better understanding of the disorder’s impact on daily life.

I hope that these examples illustrate my main point: by integrating these diverse clinical endpoints through PGHD, researchers and clinicians can collect more nuanced RWD on rare diseases, ensuring that new treatments are not only effective but also aligned with the patients’ real-world experiences and needs.

Is It One or the Other? It’s Both!

Translating findings from clinical trials to real-world settings is challenging because trials often use measures and endpoints not typically employed in routine care. Research protocols are designed to control and standardise every aspect of the trial environment, focusing on specific biomarkers and assessments that may not be regularly monitored in everyday practice. This gap makes it difficult to apply trial findings to general patient populations.

However, digital technologies enable the capture of various endpoints more easily and naturally, bridging the gap between clinical trials and real-world practice. By combining decentralized approaches with PGHD, we can generate more relevant, real-time information directly from patients' daily lives. This integration represents a significant step forward in producing robust RWD that better reflects the true effectiveness and impact of treatments in diverse, real-world patient populations.

The path forward for rare disease RWD is clear: we must embrace innovation and focus on being inclusive in sourcing RWD to solve the rare disease data puzzle. In the next blog, I will explore the role of patient societies and advocacy groups in driving research and supporting the rare disease community, highlighting the power of patient engagement and forums in advancing the way we capture information. Stay tuned!