HL7 Data Normalization for Dictation Systems
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Solution Overview
Problem
Current methods for transferring Health Level-7 (HL7) data from medical modalities to dictation systems are inefficient, leading to transcription errors and time-consuming manual processes, as they require printing and manual dictation of HL7 content, which is not optimally formatted for report generation.
Innovation Solution
A system and method that receives HL7 clinical data from modalities, parses and normalizes it using user-configurable formatting templates, and maps the normalized data to report templates in dictation systems, eliminating the need for manual transcription and improving data transfer efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual printing and dictation of HL7 content is used, then data transfer can be performed, but transcription errors occur and the process is time-consuming
Solution Approach 1:
The system enables self-service by automatically parsing HL7 data from modalities and populating report templates without requiring manual dictation. The formatting templates self-configure data extraction and transformation, eliminating the need for human transcription and thereby eliminating transcription errors while significantly reducing process time
Solution Approach 2:
The patent replaces the mechanical manual process of printing and dictating with an automated computational system. Software-based parsing, formatting, and data population replace the physical actions of printing documents and manually speaking measurements, thereby eliminating transcription errors and reducing time consumption
2Productivity
If HL7 content is printed and dictated manually, then data can be transferred to dictation systems, but the workflow is inefficient and requires human resources
Solution Approach 1:
The system performs preliminary action by pre-configuring formatting templates that define how HL7 data should be parsed and formatted before actual data transfer occurs. These templates are established in advance to match report template structures, enabling automated processing without manual intervention during the actual data transfer workflow
Solution Approach 2:
The patent introduces formatting templates as an intermediary layer between HL7 data sources and report templates. This intermediary automatically transforms raw HL7 content into formatted data suitable for report population, simplifying the workflow by eliminating the need for manual printing and dictation steps
3Measurement precision
If manual dictation of HL7 results is performed, then reports can be generated, but transcription errors occur in measurements
Solution Approach 1:
The formatting templates perform self-service by automatically extracting measurement values from HL7 data and placing them in the correct report template fields. This automated process eliminates human transcription of measurements, ensuring measurement precision while the template-based approach manages processing complexity through predefined rules
4Ease of manufacture
If HL7 data is directly transferred without formatting, then data transfer is simple, but the data is not optimally formatted for report generation
Solution Approach 1:
The system performs preliminary formatting action by pre-configuring templates that specify how HL7 data should be structured for report generation. These templates are established before data transfer, allowing automated formatting that ensures data precision while maintaining transfer simplicity through standardized processes
Solution Approach 2:
The patent uses formatting templates as an intermediary that automatically transforms raw HL7 data into properly formatted report data. This intermediary layer handles the complexity of data formatting precision while keeping the overall transfer process simple through automated template-based transformation
Data Source
AI summary
A method of retrieving data from a Health Level 7 (HL7) clinical data from one or more modalities for transferring to a dictation system is disclosed. The method includes receiving HL7 clinical data having one or more multi-segment fields from a modality that generates the HL7 clinical data; accessing a formatting template for use in normalizing the parsed HL7 clinical data, the formatting template including a retrieval setting that specifies a type of the one or more multi-segment fields from which to retrieve the one or more values; retrieving the values from the fields of the one or more multi-segment fields based on the retrieval setting as specified in the formatting template; normalizing the retrieved values using format settings set in the formatting template; and sending the normalized values to the dictation system for use in generating a report by the dictation system.