Unlocking the vast potential within unstructured clinical text is a primary goal for modern healthcare analytics. Because clinical data is messy, anyone who wants to unlock its value must start by cleaning & standardizing the data first and then mapping clinical terms to clean ontologies like SNOMED CT. While AI has made significant strides, the unique complexity of real-world clinical data remains a challenge, requiring new sophisticated approaches.
Benchmarks look great until they meet real charts. Academic datasets are pristine; production data isn’t. Real-world clinical charts are filled with abbreviations, typos, and contextual noise.
When that messy, real-world data hits general-purpose AI, the quality of output falls far short of what health plans require for operations. In our internal tests, a typical BERT-encoder pipeline—considered state-of-the-art based on benchmarks using academic datasets—managed about 60% accuracy on a real-world dataset. The gap isn’t cosmetic; it’s the difference between a system health plans can trust and one they quietly ignore.
Through our research, we developed and validated a new hybrid pipeline designed to handle the complexity of real-world clinical data. It mimics how a healthcare expert thinks, pairing deep semantic understanding with cross-method agreement.
By combining these two signals, the system makes highly nuanced decisions. It weighs both the deep contextual relevance from the AI and the objective consensus from the initial search, resolving ambiguity with incredible precision.
While theory is important, performance on real-world clinical text is the true test. The examples below, drawn from our benchmark dataset, demonstrate how the hybrid AI pipeline handles diverse and challenging inputs —ranging from typos to shorthand to long-form natural language. For comparison, we also include outputs from a well-prompted, general-purpose large language model (LLM), such as Gemini 2.5 Pro. Although such models can approximate medical reasoning when prompted effectively, they lack domain-specific training and ontology awareness, often resulting in inconsistent or clinically imprecise mappings.
|
Input Mention |
Generic LLM Prediction |
HiLabs Model Prediction |
Why the Two-Brain Approach Performs Better |
|
hypordo ischemic encephalopathy |
Neonatal asphyxial encephalopathy (27747900) |
Ischemic encephalopathy (389100007) |
Better handling of complex spelling errors; avoids context hallucination (e.g., neonatal). |
|
Gdm |
Gestational diabetes (disorder) (11687002) |
Gestational diabetes mellitus (11687002) |
Ensures precise mapping to preferred clinical terms for consistency in analytics. |
|
Closed displaced spiral fracture of shaft of right tibia |
Fracture of tibia (disorder) (31978002) |
Closed fracture of shaft of right tibia (10925321000119100) |
Retains clinically relevant detail (e.g., location and type of fracture) without overgeneralizing. |
|
Persistent pain on the upper right portion of your abdomen |
Pain radiating to middle abdomen (427668002) |
Right sided abdominal pain (285388000) |
More accurate parsing of anatomical location; avoids incorrect inferences like "radiating." |
|
Elevated total protein |
Increased serum protein level / Hyperproteinemia (81711008) |
Serum total protein above reference level (1172922003) |
Selects the most precise SNOMED concept, minimizing ambiguity in downstream use. |
|
Post tussive emesis |
Post-tussive vomiting (disorder) (424580008) |
Post-tussive vomiting (424580008) |
Maintains high reliability across less common but clinically relevant terminology. |
|
Xr ankle left |
X-ray of ankle (procedure) 363680008 |
Plain x-ray of left ankle (426420006) |
Uses domain-specific knowledge to accurately handle abbreviations and link procedural terms to SNOMED codes. |
To validate this hybrid approach, we ran a series of rigorous experiments on a ground-truth dataset of over 1,800 real-world medical terms from clinical charts. The results were transformative. By implementing this two-brain hybrid model, we were able to lift the baseline accuracy from the 60% achieved by standard AI models to 85% on the initial pass.
Further analysis of the remaining errors revealed a clear path forward. By implementing targeted, low-effort solutions for known challenges like clinical abbreviations and data indexing, the model was able to achieve a repeatable accuracy of ~89%. This represents a nearly 30-point improvement over other state-of-the-art solutions on the same noisy, real-world data.
In healthcare, performance on messy, real-world text is the real test—not on academic datasets that only exist in in researcher’s dreams. The next wave of healthcare data systems will be intelligent and multifaceted, built for this reality from day one. Our hybrid approach—combining deep semantic understanding with data-driven consensus—offers a robust, reliable, production-ready path to automated term mapping.
At HiLabs, we are dedicated to solving the most complex challenges in healthcare data. To learn more about our research and our approach to building next-generation AI solutions, visit our website.