We’ve said it before, and we’ll say it again: healthcare data has the power to transform care. It can personalize treatments and speed up diagnoses in ways we’ve only dreamed of.
But here’s the part nobody really likes to talk about: most healthcare data strategies fail before they even get off the ground.
It’s not because the ideas are bad or the people behind them aren’t brilliant. It’s because the data itself is… a hot mess.
80% of health data is unstructured. We’re talking about handwritten notes, PDFs, audio files, scanned images—data that could literally save lives, if only anyone could actually use it.
The problem? Unstructured data is tough to wrangle. And without the right tools, it’s almost impossible to turn it into something usable without violating privacy, losing context, or hitting compliance walls.
Let’s break it down.
Why It’s Such a Struggle
We surveyed 50 healthcare organizations. Here’s what they told us are the top three blockers when it comes to sensitive unstructured data:
- • There’s too much of it. Over 70% of physicians say they’re drowning in data—often without the tools or standards to manage it.
- • It’s not just text. Notes, images, audio—every format you can think of is in play.
- • Manual processes aren’t cutting it. In our survey, nearly 30% are still de-identifying manually, which is not only risky, but time consuming. Another 24% aren’t de-identifying at all. That’s over half relying on resource intensive or risky approaches.
No wonder so much of this data never gets used. It’s either too complicated, too slow, or too risky to touch.
Siloed Systems, Legacy Tech, and the Interoperability Wall
Another big issue? Siloed data stuck in ancient systems.
Post-pandemic, only 30% of healthcare organizations say they’ve had successful digital transformation projects. A lot of the tech still in use predates the iPhone. And these older systems weren’t built for AI, let alone pulling insights from scanned documents or free-text notes.
Add to that the lack of interoperability, and it’s chaos. Patient records are scattered across hospitals, labs, research databases—all using slightly different formats and languages.
One provider told us, “You’d be horrified at how little access the people who matter have to the data that matters.”
The Default? Don’t Use It at All
Instead of risking a privacy issue, a lot of teams just avoid using unstructured data altogether.
“We know there’s good stuff in those notes,” a researcher told us. “Someone took the time to write them. But we can’t safely use them, so we skip them.”
In our survey:
- • 28% said they don’t use unstructured data for decision-making, research, or operations at all.
- • 17% use it in a very limited way.
That’s a huge amount of valuable information—ignored, just in case.
One provider put it bluntly: “We have the tech to do better. We just need to use it.”
The Fix? Purpose-Built Tech for the Mess
This is exactly where Private AI comes in.
We’re built for this mess. For the notes, the PDFs, the audio recordings. For the teams that need to use this data, not just store it. We’re built for critical.
Our linguistics-first technology is designed to understand the messy, nuanced world of healthcare data. It works across formats and languages, and it doesn’t just look for keywords—it understands context.
Here’s what you can do with it:
- Discover where sensitive info is hiding
- De-identify it without losing meaning
- Transform it into usable, AI-ready, research-friendly insights
- All while keeping privacy and security top of mind
It’s not about the volume of data—it’s about a better way to use the data you already have.
Most health data strategies fall apart not because the goal is off, but because the foundation is shaky. Too much noise, too little trust in the data.
Let’s fix that.
With Private AI, your data team can stop putting out fires and start activating your previously underutilized data— and actually moving things forward.
