Automated Mapping of LEP Nursing Data to Nursing Minimum Data Sets
Renate Ranegger1,2, Werner O Hackl2, Inge Eberl3
1Department of Research and Development, LEP AG, St. Gallen, Switzerland.
Studies in Health Technology and Informatics
|June 24, 2020
Summary
Automating nursing data transformation into Nursing Minimum Data Sets (NMDS) using a novel pipeline significantly improves efficiency. This approach effectively maps nursing interventions, streamlining data ascertainment for better healthcare informatics.
Area of Science:
- Health Informatics
- Nursing Informatics
- Data Management
Background:
- Nursing Minimum Data Sets (NMDS) are crucial for systematically describing nursing care.
- Current manual data ascertainment for NMDS is inefficient and time-consuming.
- A need exists for automated methods to populate NMDS.
Purpose of the Study:
- To evaluate an automated mapping pipeline for transforming nursing data into NMDS.
- To assess the efficiency and effectiveness of automated data mapping for nursing interventions.
Main Methods:
- Developed and implemented an automated data mapping algorithm based on human expert mapping.
- Utilized LEP Nursing 3 data as the source and Austrian (NMDS-AT) and German (G-NMDS) NMDS as target formats.
- Integrated the algorithm into an automatic mapping pipeline.
Main Results:
- The pipeline successfully mapped most LEP nursing interventions to both NMDS-AT and G-NMDS.
- A fully automated mapping process from LEP Nursing 3 to NMDS-AT demonstrated high effectiveness and efficiency.
- The approach is adaptable for mapping diverse nursing classifications.
Conclusions:
- Automated mapping pipelines offer an effective and efficient solution for populating NMDS.
- This technology can significantly reduce manual data ascertainment burdens.
- The developed approach facilitates the transformation of point-of-care nursing data into standardized NMDS.
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