ArticleThe journal of nursing research : JNR2019
Using a Text Mining Approach to Explore the Recording Quality of a Nursing Record System.
Article in The journal of nursing research : JNR, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Topic Modeling of Nursing Documentation in Hemodialysis Units: A Mixed-Methods Study of Nursing Surveillance Activities.Journal of nursing management · 2026Observational
- Electronic Nursing Records: Importance for Nursing and Benefits of Implementation in Health Information Systems-A Scoping Review.Nursing reports (Pavia, Italy) · 2024Article
- A text mining and network analysis of topics and trends in major nursing research journals.Nursing open · 2024Article
- Data Quality in Health Research: Integrative Literature Review.Journal of medical Internet research · 2023Review
- Large-scale identification of aortic stenosis and its severity using natural language processing on electronic health records.Cardiovascular digital health journal · 2021Article
- Leveraging healthcare professionals' insights to enhance data quality in medical big data platforms: A qualitative study.Digital healthArticle
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Authors and funding
6 authors.
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Abstract
backgroundMost nursing records in Taiwan have been computerized, resulting in a large amount of unstructured text data. The quality of these records has rarely been discussed. PURPOSE: This study used a text mining method to analyze the quality of a nursing record system to establish an auditing model and associated tools for nursing records, with the ultimate objective of improving the quality of electronic nursing records.
methodsThis study utilized a retrospective method to collect the electronic nursing records of 6,277 patients who had been discharged from the internal medicine departments of a medical center in northern Taiwan from January to June 2014. SAS Enterprise Guide Version 6.1 and SAS Text Miner Version 13.2 software were used to perform text mining. Nursing experts were invited to examine the electronic nursing records. The text mining results were compared against a benchmark that was developed by the experts, and the efficiency of SAS Text Miner was examined using the criteria of specificity, sensitivity, and accuracy.
resultsIn this study, 27,356 nurse-formulated events were used in the analysis. The results of the nurse-formulated events showed an 8.08% similar error with system-formulated events, 29.72% were identified as necessary and appropriate names, 17.53% were retained, 10.15% involved error event names, and 34.52% were not classified. In this study, the sensitivity of SAS text mining in the training (testing) data set was 96% (95%), and the specificity and accuracy were both 99% (99%).
conclusionsThe results of this study show that text mining is an effective approach to auditing the quality of electronic nursing records. SAS Text Miner software was shown to identify inappropriate nursing record content quickly and efficiently. Furthermore, the results of this study may be included in in-service education teaching materials to promote the writing of better nursing records to improve the quality of electronic nursing records.
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