• Kevin Foley, Medicom Solutions Architect

Clinical imaging environments run on strict standards. Thanks to highly integrated systems, clinical PACS workflows are built for efficiency and patient care. But when you look at the research side of an academic medical center, the picture is very different.

In my recent conversation with Luke Bideaux of Vega Imaging Informatics, we discussed that while clinical workflows are replicable and structured, research workflows are often highly fragmented. Because legacy systems were designed for patient care, they’re awkward to use for research purposes. As a result, research initiatives frequently rely on manual workarounds, homegrown tools, or processes that vary wildly from one lab to the next.

The Consequences of the Research Data Gap

When data lacks standardization, everyone feels the pain. Researchers and healthcare data engineers often find themselves stuck in a bottleneck, and getting files out of clinical systems and into a usable format is a chore.

One major hurdle is de-identification because imaging files are much more complex to de-identify than standard text. Simply scrubbing a DICOM tag doesn't work because it breaks the file structure and ruins the image for downstream viewers. Worse, poor de-identification destroys the longitudinal view of a patient's journey. If you can't track time intervals between a screening, a diagnostic exam, and a biopsy, the information loses much of its value. These complexities lead to manual intervention, slowing down projects and frustrating teams.

How IIPs and PACS Admins Can Bridge the Gap with AI

Who is best equipped to address this disconnect? Imaging Informatics Professionals (IIPs) and PACS administrators. Because they already know the databases inside and out, they’re uniquely positioned to bridge the clinical and research divide.

While IIPs are often stretched thin with daily duties, AI-driven tools offer a practical way forward. Instead of manually pulling and scrubbing files, AI and LLMs can automate the heavy lifting, including aggregating free text from reports and connecting it with corresponding imaging files. They can also apply robust de-identification across large datasets quickly.

By adopting these solutions, IIPs can bring clinical-grade standardization to research workflows. This allows academic medical center leaders to securely share information with private partners, driving innovation without compromising security or data integrity.

Unlocking the Value of Research Assistant

To truly streamline these fragmented workflows, institutions need the right tools. That's where Medicom's Research Assistant comes in. Research Assistant automates the complex process of de-identification at scale and uses advanced techniques to shift dates and replace identifiers in a way that preserves the longitudinal integrity of the data. This means researchers get the high-quality, standardized files they need to track disease progression over time.

By providing a structured, automated pipeline, Medicom helps institutions incorporate research imaging interoperability strategies. To learn more about how Kevin Foley and Luke Bideaux view the future of imaging research, check out their full conversation here. 

Ready to see it in action? Schedule a demo of Research Assistant to explore clinical-grade automation for your team.