DICOM Structured Reporting

Radiology departments and health systems have started to adopt structured reporting. Structured reporting, sometimes called synoptic reporting, is a method of clinical documentation that captures and displays specific data elements in a specific structured format. In radiology, structured reporting templates provide consistency and clarity, prompt entry of all necessary data elements, and are amenable to scalable data capture, interoperability, and exchange. Each device, such as a CT scanner or MRI machine, produces a set of data in a structured manner, which helps eliminate or reduce the need for the technician to input each field manually or by dictation. These modalities generate outputs that are generally formatted according to DICOM standards. The Dicom Systems Unifier platform offers vendor-neutral DICOM Structured Reporting (DICOM SR) and DICOM Radiotherapy (DICOM RT) integration compatible with any dictation system or EHR/EMR.

DICOM SR and RT Benefits:

  • Capture and insert measurements directly into radiological reports.
  • Ingest DICOM SR, HL7, or FHIR output data to vendor API, HL7, or FHIR.
  • Select from multiple modifiers available when the DICOM SR value is determined.
  • Convert DICOM SR for each protocol.
  • Normalize DICOM SR, XML, JSON, HL7, or FHIR measurement data from different vendor modalities such as CT, DEXA, and Ultrasound.
  • Auto-populate DICOM SR measurement data to perform metric conversions and apply standard formatting for reporting consistency.
  • Customizable DICOM private tags mapping in DICOM SR and FHIR or HL7 formatting.
  • Pre-populate PowerScribe One template fields.
  • Supports any modality, including DICOM SR, FHIR, HL7, or DICOMweb vendors.
  • Map additional clinical and historical data along with DICOM SR data into EHR/EMR and reporting dictation systems.
  • Extract data from DICOM RT objects, integrate RT data, and generate HL7 or FHIR messages.

To learn more about Unifer DICOM SR and RT request a meeting with one of our workflow experts.