Assisting nurses in care documentation: from automated sentence classification to coherent document structures with subj
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(2020) 11:10
RESEARCH
Open Access
Assisting nurses in care documentation: from automated sentence classification to coherent document structures with subject headings Hans Moen1*† , Kai Hakala1,2† , Laura-Maria Peltonen3 , Hanna-Maria Matinolli3 , Henry Suhonen3,4 , Kirsi Terho3,4 , Riitta Danielsson-Ojala3,4 , Maija Valta4 , Filip Ginter1 , Tapio Salakoski1 and Sanna Salanterä3,4
Abstract Background: Up to 35% of nurses’ working time is spent on care documentation. We describe the evaluation of a system aimed at assisting nurses in documenting patient care and potentially reducing the documentation workload. Our goal is to enable nurses to write or dictate nursing notes in a narrative manner without having to manually structure their text under subject headings. In the current care classification standard used in the targeted hospital, there are more than 500 subject headings to choose from, making it challenging and time consuming for nurses to use. Methods: The task of the presented system is to automatically group sentences into paragraphs and assign subject headings. For classification the system relies on a neural network-based text classification model. The nursing notes are initially classified on sentence level. Subsequently coherent paragraphs are constructed from related sentences. Results: Based on a manual evaluation conducted by a group of three domain experts, we find that in about 69% of the paragraphs formed by the system the topics of the sentences are coherent and the assigned paragraph headings correctly describe the topics. We also show that the use of a paragraph merging step reduces the number of paragraphs produced by 23% without affecting the performance of the system. Conclusions: The study shows that the presented system produces a coherent and logical structure for freely written nursing narratives and has the potential to reduce the time and effort nurses are currently spending on documenting care in hospitals. Keywords: Patient care documentation, Nursing documentation, Electronic health records, Text classification, Natural language processing, Neural networks, Model interpretation
*Correspondence: [email protected] † Hans Moen and Kai Hakala contributed equally to this work. 1 Department of Future Technologies, University of Turku, Vesilinnantie 5, 20500 Turku, Finland Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is n
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