The following is a guest article by Dave Lamar, Chief Growth Officer at MediQuant

In today’s healthcare ecosystem, change moves quickly. Mergers, new EHR implementations, and application modernization initiatives have become the norm. For IT leaders, that means juggling priorities, managing aggressive project timelines, and making decisions under pressure. One area that often feels the squeeze? Data conversion.

When systems change, data has to move. That process—extracting, transforming, and loading data from legacy applications into a new system or archive—can be deceptively complex. It’s tempting to treat it as a tactical task, just another checkbox on the go-live readiness list. But moving data isn’t the same as preserving its meaning, context, and usability. And when organizations prioritize speed above all else, they often end up paying for it later—in rework, delays, and operational disruptions.

The reality is this: not all data conversion approaches are created equal. And in healthcare, where data accuracy directly impacts patient care, compliance, and business continuity, the difference between fast and right couldn’t be more critical.

The Risks of “Lift and Shift”

Some vendors offer what’s known as a “lift-and-shift” approach—moving data from point A to point B without deep validation or schema alignment. On the surface, this can appear efficient and budget-friendly. But in practice, it often leads to incomplete or inaccurate data, broken reporting, and the need to go back and fix errors after the fact.

This approach is especially common in ambulatory settings, where cost sensitivity is high and systems may not be as standardized as those in large hospital networks. But the risks are universal. If the converted data can’t be trusted—or if it doesn’t support the workflows clinicians and staff depend on—it undermines the entire investment in modernization.

Legacy Data Is More Than a File Transfer

One of the most overlooked aspects of data conversion is understanding how legacy systems manage and present data. Older applications often use proprietary data schemas and logic to deliver specific views. Those views are tied to the application itself, not just the raw data. When the application is decommissioned, those capabilities disappear—unless you plan for them.

If your conversion partner doesn’t know how the system used to function, they can’t replicate the necessary views or workflows. You might end up with a pile of data that’s technically intact, but practically useless.

That’s why experience matters. A qualified partner knows what to look for: which system-generated reports need to be preserved, which patient record views must be recreated, and how to translate data manipulation logic into a new environment. They know that legacy systems rarely make it easy—and they come prepared.

Expertise Accelerates Accuracy

Speed and cost are top concerns for every healthcare organization, especially as IT budgets tighten. But the best way to ensure both is through expertise. Rushing into a project without a clear understanding of the data landscape often leads to midstream corrections, extended timelines, and cost overruns.

Experienced teams come equipped with tested scripts, established data maps, and tools that streamline the process. For example, we’ve developed approaches that allow us to extract data directly from legacy system backups using Oracle—bypassing limitations in outdated extraction tools and increasing accuracy. These technical capabilities, combined with operational awareness, help keep projects on track without sacrificing quality.

Converting and Archiving Together

Another opportunity to drive both efficiency and value is by handling data conversion and archiving simultaneously. When done in tandem, these processes can reduce duplicate work, streamline timelines, and ensure consistency across systems.

We’ve seen clients save hundreds of thousands of dollars by approaching their data strategy this way. Instead of managing two separate projects with two sets of vendors and processes, they align efforts from the start—creating a single source of truth and minimizing disruption.

When the archive goes live alongside the new system, organizations are better positioned to decommission legacy applications, reduce risk, and reclaim resources. That’s not just a technical win; it’s a strategic one.

Seeing Data as a Strategic Asset

Ultimately, successful data conversion starts with a mindset. Too often, organizations treat older data as a liability, something to get rid of or minimize. But when normalized and integrated, historical data can become a powerful asset.

Whether for clinical decision-making, population health analysis, or regulatory reporting, older records provide context and continuity. Tools like IMO (Intelligent Medical Objects) can help standardize terminologies across time, making legacy data compatible with newer systems and more valuable in aggregate.

By elevating the role of data in digital transformation, healthcare organizations can shift from a reactive approach to a proactive one—designing conversions that support long-term business and clinical goals, not just short-term technical ones.

Choose the Right Partner, Ask the Right Questions

As you evaluate your next data conversion project, look beyond the timeline. Ask your potential partner:

• How do you validate data accuracy before decommissioning?
• What tools do you use to extract from legacy systems or backups?
• Can you replicate key record views or reports from the original system?
• How do you approach simultaneous archiving and conversion?

These aren’t just technical questions, they’re strategic ones. The answers will determine how much trust your clinicians can place in the data they see, how quickly your teams adapt to the new system, and how much value you can extract from the information you already own.

In a field where every data point matters, precision is everything. The fastest path isn’t always the right one—but the right one is often faster than you think when done with the right expertise.