By Jim Hammer, COO, Harmony Healthcare IT
In nearly every conversation I have with health system leaders about AI, a central theme keeps surfacing: AI is only as good as the data behind it.
Yet for many organizations, legacy data management is still approached primarily as a compliance checkbox rather than a strategic initiative. As AI becomes more embedded in operations, that approach to legacy data management is increasingly insufficient.
Earlier this year, I joined Bill Russell on “This Week Health” to discuss why that’s the case, and what it actually takes to get legacy data working for health systems and their AI initiatives. In this article, I’m focusing on three key takeaways from that discussion, and on the big shifts I’m seeing when it comes to AI.
Takeaway #1: Legacy Data Is Not Automatically “AI-Ready”
For years, the primary drivers for legacy archiving were cost reduction, security risk reduction, and retention requirements. Those drivers still matter, but what has changed, especially over the past year, is that more leaders are asking: How can we ensure our legacy data is positioned to fully support AI initiatives? It’s the right question to be asking, and it’s one that not all legacy data archiving vendors are equipped to answer effectively.
For legacy data to be “AI-ready,” the front-end work associated with data migration and archiving must be approached with many critical requirements in mind. At the core, any source data that migrates to the archive must be transformed accurately, efficiently, and comprehensively while maintaining and improving data integrity. If not, it can become a significant constraint—and a potential risk to compliance and patient care—as your organization implements AI solutions that rely on it.
Takeaway #2: “AI-Ready” Depends on Trusted, Governed Data
Beyond successful data transformation, AI-readiness depends on several foundational capabilities that should be built into your archiving strategy:
- Trusted clinical data. Legacy data should be accurate, complete, and organized in a way that allows AI to distinguish between overlapping information from multiple source systems rather than treating conflicting or redundant records as equally authoritative.
- Privacy-preserving patient linkage. Organizations should be able to de-identify and securely link patient records across systems, enabling AI models to analyze longitudinal patient journeys without exposing protected health information.
- Strong data governance. Archived data should include appropriate governance, lineage, auditability, and access controls so AI applications can be deployed responsibly and in alignment with organizational policies and regulatory requirements.
Takeaway #3: AI Hype Doesn’t Always Translate to Operational Value
Of course, data readiness is only half the equation. How AI is applied within your archiving solution should also be top of mind. Right now, there is a lot of noise in the market related to AI. “AI-enabled” has become a label applied broadly by vendors, even when applications are not well defined and the value is unclear.
Our view is that AI should be applied prescriptively: where it solves a validated problem, fits securely into existing workflows, and delivers measurable outcomes. That’s how organizations gain efficiency and enhance compliance, without introducing unnecessary risk.
For example, our solution enables customers to use AI to identify potential compliance risks within archived records, reducing the need for manual review, and helping HIM teams operate more efficiently. This kind of use case only works because the underlying data is accurate, structured, and governed, and the AI solution is built into an existing user workflow.
Final Thoughts
The organizations that will get the most value from AI over the next several years will not be the ones that simply adopt the most tools. They will be the ones that ensure their data is accessible, governed, and trusted. They will also be the ones that apply AI intentionally.
Keep in mind that being AI-ready isn’t a one-time project; it’s a commitment to ensuring your legacy data is governed and positioned for current and future needs. That requires choosing an archive partner who prioritizes compliance and builds toward long-term data management success.
If your team is evaluating how to make legacy data more usable for AI and operations, we’d welcome the conversation. Contact us today.
