Your healthcare AI processes too much data.
Cuts that by 80%–93%.
Cuts that by 80%–93%.Faster results. Better accuracy. A fraction of the cost.
Cuts that by 80%–93%.A pediatric oncology patient at a major children's hospital. Real record complexity.
| Laboratory Results | 31,659 |
| Documents | 7,049 |
| Treatment Episodes | 3,524 |
| Medications | 2,584 |
| Measurements (Vitals) | 1,863 |
| Encounters | 932 |
| Imaging Studies | 604 |
| Diagnoses / Conditions | 171 |
| Pathology Diagnostics | 160 |
| Tumor Procedures | 53 |
| Distinct Clinicians | 300+ |
| Clinical Specialties | 30+ |
~49,000 structured data points. ~7,000 clinical documents. Millions of tokens. One patient.
Now imagine your AI trying to generate a care plan, a prior authorization letter, or a treatment recommendation from this record. It can't process all of it. And it shouldn't have to. The question is: which data points matter for the task at hand?
A complex patient's record can exceed a million tokens — too large to send, too expensive per call. So most systems retrieve fragments based on keyword similarity. What comes back sounds related but misses critical relationships between labs, medications, and diagnoses.
FHIR resources are verbose, fragmented, and relationship-poor. Models spend their context parsing structure instead of reasoning about the patient. Trends that should be obvious are buried across hundreds of discrete observations.
A bad recommendation could be a model, data, or retrieval problem. Without visibility into exactly which data informed each decision, debugging is guesswork and improvement is slow.
Example: complex chronic disease patient (6+ conditions, extensive clinical history)
Every resource loaded. Redundant references. Unresolved codes. No task-specific filtering.
Clinically relevant data only. Organized for AI reasoning. Every inclusion and exclusion logged.
This patient: 91% token reduction | Range across patient complexity: 40–93%
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