April 21, 2026
On March 30, 2026, I attended RISE Together: Data Sharing Across the Rare Disease Ecosystem [1] expecting a discussion about the platforms and infrastructure needed to move data more efficiently across systems. Instead, what surfaced over the course of the day was something more fundamental, and more difficult to resolve. The rare disease field is not short on data. It is struggling to use the data it already has.
Approximately 30 million Americans are living with a rare disease and of the more than 7,000 identified conditions, only 4 to 6% have an approved therapy [2]. The default explanation is that science has not yet caught up, that we simply need more research, more innovation, more funding and more time. But the discussion at RISE pointed to a different reality [3]. Across registries, natural history studies, electronic medical records (EMRs), and completed clinical trials, an enormous volume of patient data already exists. It is detailed, longitudinal, and in many cases exactly what would be needed to accelerate development. The problem is that much of it remains confined to the context in which it was generated. There are, of course, examples of where the system works as it should. The approval of omaveloxolone for Friedreich’s ataxia drew in part on a matched natural history cohort, allowing existing data to serve as confirmatory evidence [4]. Similarly, givinostat, the first nonsteroidal therapy approved for Duchenne muscular dystrophy across genetic variants, emerged from years of coordinated data sharing and infrastructure-building within the community. [5] These cases are often presented as success stories-and they are-but they also highlight how unusual this level of data integration still is. The underlying systems are capable of supporting it, they are just not consistently designed to do so.
Part of the challenge is structural. Many of the regulatory frameworks governing human subjects research were designed to protect patients from physical harm and translate less cleanly to a landscape in which health data can be reused, recombined, and increasingly re-identified [6]. Patients themselves are rarely the limiting factor, rare disease communities have shown a consistent willingness to contribute their data in the hope that it will lead to progress. The friction lies in how that participation is operationalized: through consent processes that are often opaque, and systems that are not built with downstream use in mind.
Industry dynamics add another layer. Patient foundations frequently invest in building registries and natural history datasets and make those resources available to support clinical development. Yet when trials are completed, the data generated within them, particularly placebo-arm or longitudinal datasets that could be highly informative, are not routinely returned to the broader ecosystem. This is not a question of bad intent so much as misaligned incentives. Data carries competitive value, and without structures that reward sharing, it tends to remain siloed.
There are examples that suggest a different approach is possible. In one case discussed at the meeting, a company chose to share data from a failed Phase 3 trial in epidermolysis bullosa. That dataset was later used by another sponsor as an external control, contributing to a successful regulatory outcome. It is a simple idea, but it represents a shift in how data is treated, not as a static asset tied to a single program, but as something that can continue to generate value beyond its original purpose. What becomes clear, stepping back from the individual examples, is that the field is not waiting for new tools. Platforms such as the Critical Path Institute’s RDCA-DAP, large-scale datasets assembled by disease consortia, and prospective external control efforts like the CF Foundation’s REACH study all demonstrate that the infrastructure for sharing and reuse is already in place [7].
These barriers are not unique to rare disease. In Canada, parallel challenges play out across disease areas that operate at very different scales but face structurally similar problems of data mobility. In Ontario, POGO (Pediatric Oncology Group of Ontario) has built a deeply curated, longitudinal registry that has informed pediatric cancer surveillance, system planning, and care delivery [8]. At the national level, population health research platforms including the Canadian Longitudinal Study on Aging, which tracks over 50,000 Canadians aged 45 and older across biological, medical, and psychosocial dimensions [9], and CanPath, which captures genomic, behavioural, and environmental data from over 330,000 participants to study cancer and chronic disease risk-are generating rich, multidimensional datasets [10]. Combined with administrative health data platforms such as ICES in Ontario, these resources have enormous potential to inform prevention and care across the lifespan, provided they are consistently structured and made accessible for reuse [11].
The remaining barriers are less technical than structural. They sit at the level of governance, incentives, and, perhaps most persistently, habit.
From a medical writing perspective, this is where the issue becomes unexpectedly familiar. Many of the obstacles discussed at RISE trace back to how information is documented and communicated, echoing themes we explored in our synopsis of the MAX 2026 Medical Affairs Forum: organizations often collect data that is never structured for reuse, write consent forms patients do not understand, and generate placebo-arm trial data that remains inaccessible to future studies. At its core, these are failures of medical communication.
At Craft Science Inc., we work at exactly this intersection: we know the right documentation and communication strategy can unlock data reuse from the very beginning. Rather than treating data strategy as a downstream concern, we help teams build the infrastructure for reuse into their initial documentation: consent forms designed to support future research, trial protocols structured to enable data sharing, and regulatory packages built with secondary use in mind. If your organization is designing trials, navigating data sharing agreements, or developing the documentation framework needed to make your data fit for regulatory use and future research, we would love to connect..
The data exists, the tools exist. The missing piece is the communication strategy that lets that data travel beyond its original study and into future research, and that is where we can help.
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