Data Technology + RWD positive for rare diseases

In the United States, rare diseases affect less than 200,000 people, or about 30 million. Sadly, three out of 10 children with rare diseases will not survive to see their fifth birthday, but the path to diagnosis and treatment is expensive and uncertain. The uncertainty is intensifying as funding cuts are affecting those who rely on government support their research.

But, there is good news. Technical commitments in the form of technical intelligence platforms and high-fidelity reality data (RWD), designed specifically for healthcare and embedded in the clinical environment, are transforming rare disease discoveries as well as new treatments and therapies.

Accurate through data

Researching rare diseases presents unique challenges. The patient population is small and geographically dispersed, the symptoms are often nonspecific, with little standardization in the way these conditions are encoded or recorded. Many patients wait years for an accurate diagnosis and focus on symptoms rather than illness, leading to misdiagnosis and broken care.

Data intelligence platforms with artificial intelligence and machine learning algorithms can discover patterns in large, complex datasets that are especially important for rare diseases where patients may experience different symptoms and comorbidities. Today’s technology can identify patients, map their disease progression and care journey, and can also identify doctors and other providers who care for rare diseases, as well as possible dangerous diseases.

For example, pattern recognition can identify patients with abnormal diagnosis travel and detect subtle clusters of symptoms, which then shorten the time it takes to find a diagnosis. Ultimately, this increases the number of patients who may be eligible for clinical trials and targeted therapies. As many rare diseases progress silently, AI and ML-driven longitudinal RWD analysis helps to track patient progress based on slight changes in laboratory values, drug transfer or hospitalization patterns, resulting in early and more precise interventions to track patient progress.

To leverage powerful AI and machine learning tools, it is crucial that the data used is both high-quality and interoperable. Healthcare data are very complex, so the quality is often inconsistent and requires substantial investment in data cleaning and preparation. Even quality verification can be inconsistent and inaccurate without correcting missing or incomplete data.

It has long been believed that researchers need more data, but this is not always the case, especially for rare diseases, where accuracy is key. Embedding data from clinically specific or therapeutic settings allows researchers to focus on their issues more accurately. Context-rich data can power integrated control arms or digital twins – a critical tool in rare diseases, as it is difficult to achieve patient numbers and traditional placebo groups.

Decompose data islands

Another important obstacle is the dispersed data. The industry must work hard to break down data silos and combine data sources from different health systems, electronic health records, claims, registries and biobanks. Once the data can be aggregated together, it must be cleaned, standardized, coordinated and mapped to common models such as OMOP to ensure quality and comparability. Compliant and rich data can then be linked to create a unified patient journey and discover hidden meanings in complex data.

True interoperability is especially important in the world of rare diseases. By combining and linking or bridging data, the raw data will become high-value information, allowing researchers to accelerate their clinical trial recruitment activities, discover new findings and improve results.

Connection point

To overcome obstacles within rare disease spaces, the use of data intelligence technology and embedded RWD can provide more insights while accelerating timelines and maintaining tighter control over costs. This is especially important in an era of limited time and capital. Provide tools that comply with, identify, link, and aggregate data science tools such as AI, machine learning, and advanced analytics that can help people who study rare diseases overcome the obstacles they face in discovery and development.

By leveraging RWD, biotechnology and life science companies can overcome the traditional challenges of patient identification, clinical trial recruitment and regulatory approval. Integrating AI, machine learning and standardized data frameworks enable life science companies to bridge existing gaps, ensuring more patients are diagnosed promptly and access to life-changing therapies.

Photo: Ipuba, Getty Images

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