When it comes to rare and ultra-rare diseases, finding the right data can be one of the biggest hurdles in research and drug development. With thousands of rare conditions affecting only a small number of people, the data needed to understand these diseases is often scattered, inconsistent, or hard to access. That’s why collaboration, smart technology, and a focus on patient trust are so important. Here, we take a closer look at how better data practices, from standardization to secure sharing, can help researchers, clinicians, and patients work together to move precision medicine forward.
Characterizing patient populations
Precision medicine initiatives focusing on rare or ultra rare conditions are faced with the challenge of accessing sufficient data to accurately characterize these small-scale populations. While there are more than 7,000 conditions which are defined as rare diseases, each of these affects approximately 1 in 200,000 people or less. Collecting statistically significant data is therefore an ongoing challenge for tackling rare disease, but is particularly heightened in the context of precision therapy development. Due to the limited amount of data available for an individual rare condition, companies are having to utilize a combination of natural history and claims data when comparing study design and results.
Such challenges further highlight the importance of international collaboration to enable data sharing and build aggregated datasets which better represent rare disease populations and enable accelerated treatment development. Integrating patient centric approaches will also play a vital role in winning the trust of these communities and embedding a culture of data sharing and research participation.
Interoperability and data sharing
While pharma and healthcare organizations are making strides in digitizing, integrating, and improving secure access to medical healthcare records, data linking issues are posing setbacks to doing so effectively. For example, while physicians and researchers are increasingly able to access medical records, integrating all the associated information, such as scans and biomarker data, is still proving a challenge due to information being stored in disconnected repositories or formats.
Ensuring that data is formatted in a way in which it can be coordinated, integrated, and securely shared across systems and between organizations is vital not only to precision medicine research efforts, but to the improvement of frontline patient care and overall health outcomes.
Adopting universal data sharing standards which can be rolled out on an international scale will play a key role in scaling patient engagement strategies, enabling the pooling of fragmented datasets and contributing to improving the overall likelihood of trial success.
As of 2023, the European Union estimated that poor data accessibility, interoperability, and reusability costs member states a total of approximately €10.2bn per year, further highlighting the impact of poor data practices and shedding light on the substantial trickle-down cost savings that stand to benefit both businesses and patients significantly.
FAIR Guiding Principles
The FAIR Guiding Principles for scientific data management and stewardship, published in 2016, provide one such example of universal data sharing standards. These principles aim to improve the Findability, Accessibility, Interoperability, and Reuse of scientific data. The framework provides a useful template for establishing key pillars to which data standardization should adhere in order to foster large-scale collaboration and best support patient benefit.
Agreeing to and following such principles will be vital to unlocking collaborative potential, garnering a better understanding of rare diseases and enabling a personalized approach to healthcare and treatment.
Some key tenets of collaborative data sharing practices include:
Key takeaway
Making the most of data in healthcare isn’t just a technical challenge, it’s about building trust, breaking down silos, and making sure the right people can access the right data at the right time. By embracing universal standards, investing in better tools, and working across borders, we can create a future where even the rarest diseases are better understood and more treatable.
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