AI-native clinical entity recognition. Transform unstructured text files into structured key-value parameters.
Our healthcare-trained LLMs classify medical terminology and link clinical parameters, preserving relationship contexts.
Parse text arrays to identify clinical tokens (medications, strengths, procedures, diagnostics).
Link doses with medication names and lab values with lab test names using clinical dependency models.
Evaluate extraction certainty at the token level, flagging low-confidence values for manual audit.
Entity: Medication | Confidence: 99%
Name: Acetaminophen | Dose: 325mg | Freq: Q4-6H
Entity: Lab Result | Confidence: 97%
Lab: Serum Sodium | Value: 138 | Unit: mmol/L
Integrate this platform module with standard code requests. Receive clean, validated, and normalized payloads that fit into EHR databases or server hooks.
{
"documentId": "doc_8f17a942b083",
"entities": [
{
"category": "MEDICATION",
"text": "Tylenol 325mg",
"confidence": 0.99,
"attributes": {
"strength": "325mg",
"frequency": "every 4-6 hours as needed"
}
},
{
"category": "LAB_RESULT",
"text": "Sodium: 138",
"confidence": 0.97,
"attributes": {
"value": "138",
"unit": "mmol/L"
}
}
]
}Request a custom data extraction assessment. Our healthcare integration architects will evaluate your email attachments, faxes, scanned files, and EHR configurations.