We discussed how new technology is transforming healthcare: As the volume of electronic data continues to increase, many sectors refer to this phenomenon as a data revolution. And though this revolution promises faster diagnoses and personalized care, it comes with a catch: most of that data is never even used.
Welcome to healthcare’s quiet crisis: the abandonment of unstructured data.
Let’s start with the sheer volume. 70% of physicians say they’re drowning in data, but without the right tools, that data just becomes noise. You might think, “Wouldn’t AI help here?” And you’re right, it could. But to make AI work, you first have to tackle a whole new set of challenges:
- • Same thing, different label: Health data is not standardized. The same concept might be described differently by different regions, hospitals, or even departments within an organization. One hospital says “MI,” another says “myocardial infarction,” and a third just writes “heart attack.” That means that combining data from multiple sources is a nightmare for training AI models.
- • It’s all in the notes: Critical patient information is often buried in unstructured text fields and contains rich clinical insights, but it’s usually inaccessible for traditional systems. Simple pattern matching or database queries often overlook these insights.
- • Context matters: Clinical language is full of nuance. Medical jargon, abbreviations, and “doctor-speak” shorthand are common. Contextual nuances like negations or conditional statements make entity recognition tricky – a patient “denies chest pain” vs. “has chest pain” are opposite meanings, which simple keyword spotting might misinterpret. Traditional rule-based or regex approaches struggle with these nuances, often leading to missed entities or false positives.
- • Data silos everywhere: Health data lives in silos. One patient’s record might span five databases. This lack of consistency makes it nearly impossible to combine datasets, train generalizable models, or create a unified view of the patient. Add in missing metadata, documentation inconsistencies, and red tape, and you’ve got a recipe for fragmentation and frustration.
Now, combine all of that with regulatory and operational complexity: health data is entangled in a maze of red tape, usage restrictions, and process checklists. These constraints don’t just slow things down; they often prevent organizations from engaging with unstructured data at all.
The result is that this data is often left untouched, and the valuable insights within it remain hidden. As one researcher told us, “We know there’s good stuff in those notes. Someone took the time to write them. But we can’t safely use them, so we skip them.” In fact, 97% of healthcare data is discarded due to its complexities. And if that much data is left behind, we end up with a “Swiss cheese” dataset: full of holes, resulting in incomplete or misleading analysis.
So What If We Could Leverage It?
Health data is messy—but that’s exactly why structuring it matters. Here’s what becomes possible when we do:
- • Data Standardization & Structuring: We can align synonyms, capture nuance, and translate messy shorthand into structured, usable data. Imagine unifying scattered clinical notes across systems into one coherent, research-ready dataset. As one researcher put it, “If we can retrieve datasets from disparate organizations, organize them uniformly, we leapfrog into a completely different area of discovery.”
- • Enhanced AI Model Development: Training AI models or conducting advanced research requires large, diverse datasets drawn from real-world clinical documentation. By removing the manual, error-prone burden of data curation, we could accelerate the development and deployment of powerful healthcare AI models—ultimately driving more personalized patient care and innovative clinical solutions.
- • Safe, Context-Aware Data Sharing: Scientific breakthroughs depend on access to high-quality clinical data; however, traditional de-identification tools either remove too much context or miss subtle identifiers, rendering the data unusable or unsafe to share. With advanced, context-aware transformation, it’s possible to accurately detect and manage even nuanced or indirect identifiers, without losing the clinical meaning that drives discovery. That way, health organizations can confidently share rich, usable datasets with research partners, enabling faster insights, stronger collaborations, and a more open path to innovation.
- • Strategic Partnerships & Collaboration: The healthcare big data analytics market was valued at $46.8 billion in 2024 and is projected to reach $123.5 billion by 2033. And yet, most of it still sits unused. With advanced, linguistically aware transformation, it’s possible to accurately protect sensitive details while preserving the context needed for real-world value. This opens the door to compliant, controlled data monetization—whether through licensing, research partnerships, or external collaborations, unlocking new revenue streams while maintaining patient privacy and public confidence.
Bringing Data Out of Hiding.
The good news? We already have the capability to extract and utilize that data, without compromising privacy or losing context. Here’s a step-by-step:
Find It
Use health-specific Named Entity Recognition (NER) to pinpoint relevant information across all formats—text, images, audio—in multiple languages.
Extract It
Take out relevant data securely without stripping it of clinical meaning. (e.g., “Patient Michael Hodgkins” vs. “Patient has Hodgkins.”)
Transform It
Take action on the data you found: De-identify it, substitute it, label it, structure it. Activate the insights within.
This isn’t just about cleaning up messy data. It’s about accelerating research, improving care, and unlocking collaboration that was never possible before.
The value is there. The data is there.
