Health systems are under pressure to improve margins without increasing clinical workload. Few assets offer that potential, bu medical data is one of them.
When shared responsibly, data can drive breakthroughs in AI to improve patient care and can create new, sustainable revenue streams for health systems.
With responsible data sharing, and meticulous compliance protocols, health systems can transform their currently underused datasets into valuable assets. Hospitals acting now are leading the way. Making data sharing operationally feasible is the challenge for most healthcare systems, ensuring strong governance from the outset.
Why medical data is an underused asset for healthcare systems
Healthcare systems create a wealth of data with every patient visit. Once each patient has been diagnosed, treated or entered into their care pathway, their data is often stored away and not used again. However, whilst it is not adding new clinical value, it is still adding cost to the system.
Why AI developers need large, diverse medical datasets
AI systems require vast amounts of diverse, high-quality, real world data to be trained and validated on, but accessing this data is time-consuming and difficult for AI developers.
Historically, the development of medical AI has been hindered by the lack of well-annotated, large-scale, and diverse datasets. Algorithms trained on narrow datasets often fail in real-world settings and exacerbate disparities rather than eliminating them. Without a wide range of representative data from different geographies, demographics, and health systems, accuracy and applicability are narrowed, reducing real-world impact.
For AI to be developed effectively, the operational burden of data sharing needs to be reduced.
How to share healthcare data safely and compliantly
The hesitation around data sharing is understandable. Healthcare data is sensitive, and providers are rightfully cautious.
In practice, the barrier isn’t willingness, it’s execution. Health systems face multi-stakeholder approval processes, complex data extraction from clinical systems, and the challenge of turning raw data into usable, compliant datasets.
Once healthcare systems decide to share their data, the narrative turns to how to make this possible. And the answer must achieve it under defined governance models that prioritise safety, sustainability, and responsibility.
It is crucial that health systems are supported by trustworthy partners that enable them to share their data in a way that is compliant, de-identified, and privacy-preserving. Transparency and control are paramount.
In addition, data sharing doesn’t have to be a one-time transaction. Sustainable data partnerships can operate with models that deliver long-term value. That means generating recurring revenue while also ensuring the data serves a greater good. Long-term agreements give healthcare systems predictable revenue, and ensure alignment with research and AI development partners providing more value to them over time.
How data partnerships generate recurring revenue
With the advent of AI, healthcare systems can either shape the future, or react to the market shifts. By directly contributing to the data needed to train medical AI, healthcare systems can be part of the force shaping how healthcare will look in the coming decades, rather than simply reacting.
We see the challenges in building medical AI today, and our mission is to improve data access. We believe data sharing can be done safely, sustainably, and responsibly and with value for the hospitals who provide the data and the communities they serve.
Learn more about how data partnering actually works in practice: Data Partners