Assisting nurses in care documentation: from automated sentence classification to coherent document structures with subject headings

• Published in: Journal of biomedical semantics 2020-09, Vol.11 (1), p.1-10, Article 10
• Main Authors: Moen, Hans, Hakala, Kai, Peltonen, Laura-Maria, Matinolli, Hanna-Maria, Suhonen, Henry, Terho, Kirsi, Danielsson-Ojala, Riitta, Valta, Maija, Ginter, Filip, Salakoski, Tapio, Salantera, Sanna
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Automatic classification; Automation; Classification; Coherence; Documentation; Electronic health records; Medical informatics; Methods; Narratives; Natural language processing; Neural networks; Nurses; Nursing; Nursing care; Nursing documentation; Patient care documentation; Practice; Subject heading schemes; Taxonomy; Technology application; Text classification
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-020-00229-7

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