FHIR Medical Measurement Converter-FHIR Data Conversion Tool

Example

Convert unstructured imaging reports into structured data.

Scenario

A hospital's IT department uses the tool to standardize historical radiology reports, converting text into structured fields like 'Examination Type,' 'Modality,' and 'Findings,' which can then be mapped to appropriate LOINC codes.

Example

Identify relevant clinical details, such as modality type and anatomical location.

Scenario

A medical coder uses the tool to automatically extract modality details from a set of DICOM images and identifies the appropriate LOINC code for each modality, simplifying coding for billing purposes.

Example

Generate machine-learning-ready features based on extracted clinical information.

Scenario

A data scientist uses the tool to convert a series of ophthalmology examination reports into structured FHIR resources, generating features like 'Severity of Findings,' 'Visual Acuity,' and 'Lens Condition,' to build predictive models.

Example

Map clinical data to suitable LOINC codes based on DICOM tags.

Scenario

A clinical researcher standardizes data from different imaging studies by using the converter to match DICOM tags to LOINC codes, enabling consistent comparison across studies.

Example

Generate detailed FHIR Observation resources with narrative summaries.

Scenario

An imaging center adopts the converter to transform CT scan results into FHIR Observation resources, complete with narrative summaries, to ensure the data is human-readable and complies with FHIR standards.