How Health Systems Can Extract Full Value from Their Healthcare Data Without Taking on Additional Work

The Untapped Value of Clinical Data

Health systems and laboratories hold some of the most valuable data in healthcare. Diagnostic records, molecular testing results, pathology findings, and treatment data are important to understanding disease, advancing research, and improving how targeted therapies are delivered. While the potential of this data is widely recognized, turning that potential into something actionable has proven harder than expected, especially for institutions that lack time, resources, or infrastructure to support large-scale data initiatives.

For many health systems, making the data usable, let alone shareable or monetizable, can feel out of reach. Preparing datasets for research or external collaboration often requires significant internal lift: integrating disparate sources, harmonizing formats, de-identifying sensitive fields, managing compliance, and navigating governance approvals. Even for organizations with strong research programs, this can become a resource-intensive process that competes with operational priorities.

As a result, many organizations have remained on the sidelines, not because they aren’t willing to share data, but because they lack the time, infrastructure, or internal support to prepare that data for external use. And for those that have made the effort, traditional models often fall short. They offer limited visibility into how data is used, unclear benefits, and ask institutions to give up more control than they’re comfortable with.

A Shift Toward Lower-Lift, Higher-Control Participation

This dynamic is beginning to change. New approaches to data infrastructure, particularly around federated technologies, are opening new opportunities for health systems to extract value from their data without taking on the full burden.

One of the biggest shifts is the automation of data readiness. Historically, converting raw data into research-ready or analytics-ready formats required manual effort and deep institutional knowledge. Data from lab information systems (LIS), pathology reporting tools, or imaging platforms was often stored in disparate formats, making integration a slow and error-prone process. Today, more frameworks support automated mapping, indexing, and standardization of structured data, using pre-defined research or regulatory models, reducing the manual effort typically required.

Preserving Governance While Enabling Collaboration

Equally important is the preservation of governance. One of the biggest concerns health systems face when exploring data-sharing is the potential loss of control. Traditional models that centralize data with a third party often come with uncertainty: who’s accessing the data, how it is being used, and whether appropriate compliance safeguards are in place. These concerns are especially important when the data in question includes sensitive patient information.

Modern frameworks, such as federated models, take a different approach. In some federated models, health systems retain full oversight of how their data is accessed and by whom. Depending on the model, participation may allow institutions to determine which data fields are accessible, what types of queries are permitted, and whether results are returned in aggregate or de-identified form. When properly designed, these systems can offer auditable, governed access, preserving institutional control without compromising patient privacy or compliance.

Lowering the Barrier to Entry

Perhaps the most important shift is that the effort required to participate in these models is significantly lower than it historically has been. Rather than building new infrastructure or assigning dedicated internal teams, health systems can work with partners who manage the technical lift, handling tasks like data mapping, harmonization, privacy controls, and query execution. This allows institutions to contribute to external collaborations or pharmaceutical research needs without pulling focus or staff away from other priorities.

This low-lift model addresses one of the most common barriers to data collaboration: the resource burden required to prepare, manage, and share data securely. It enables health systems to participate in opportunities they may have previously declined, not because they weren’t interested, but because they didn’t have the resources to proceed. Whether the goal is supporting evidence generation, enabling innovation, or participating in incentivized research collaboration, health systems now have a way to engage without taking on the full burden.

Enabling a More Inclusive, Sustainable Solution

It also expands who can participate. Mid-sized institutions, regional labs, and non-academic medical centers, groups that have traditionally been underrepresented in these collaborations, are no longer excluded by these barriers. They can bring their unique datasets into broader research without sacrificing control or security.

As healthcare continues to evolve toward data-driven collaboration, the question for many health systems is now whether they can activate their valuable data without overextending their internal teams. Newer frameworks that lower the technical burden while preserving governance make that possible.

For health systems looking to contribute to research, support evidence generation, or simply extract more value from the data they already have, the tools are finally catching up with the opportunities.