Convert unstructured medical data for smarter analytics and reporting

Automatically transform unstructured data efficiently and without errors

How it works?

The Plain-Text to FHIR/CCDA Converter solution offers a fast and reliable way to convert unstructured 'text' data into structured objects.

 

The solution processes TXT or HL7 messages with relevant TX/NTE segments to extract useful information (e.g. patient medical history, claims, etc.) and then to encode it based on the range/format of measurements.
 

The extracted data points are provided as CCDA or FHIR, with original text referred to as a source. Non-digitized documents can be processed after scanning and OCR.

  • Data Transformation

    Trained to efficiently identify various types of data in unstructured texts, the model enables and accelerates text to CCDA/FHIR conversion.

  • Conversion Accuracy

    Powered by machine learning, the solution can extract and convert data from unstructured documents with an accuracy of up to 100%.

  • Lower Document Processing Costs

    The combination of high accuracy, automation, and efficiency-driven by machine learning allows you to reduce document processing costs.

  • New Data-Driven Capabilities

    Drive analytics initiatives like readmission rate prediction and patient deterioration rate prediction using PHI and ePHI in structured formats.

  • No code or templates to maintain

  • Automatic text annotation tool

  • Convert and work with TXT files, HL7 TX, or NTE segments

  • Secure, scalable, and customizable

  • Easy integration through RESTful API

Key Features

Key Benefits

How to deploy our software products?

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