Clustering Nursing Sentences - Comparing Three Sentence Embedding Methods.

In health sciences, high-quality text embeddings may augment qualitative data analysis of large amounts of text by enabling, e.g., searching and clustering of health information. This study aimed to evaluate three different sentence-level embedding methods in clustering sentences in nursing narratives from individual patients' hospital care episodes. Two of these embeddings are generated from language models based on the BERT framework, and the third on the Sent2Vec method. These embedding methods were used to cluster sentences from 20 patient care episodes and the results were manually evaluated. Findings suggest that the best clusters were produced by the embeddings from a BERT model fine-tuned for the proxy task of predicting subject headings for nursing text.

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

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.

Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods.

OBJECTIVE: This study focuses on the task of automatically assigning standardized (topical) subject headings to free-text sentences in clinical nursing notes. The underlying motivation is to support nurses when they document patient care by developing a computer system that can assist in incorporating suitable subject headings that reflect the documented topics. Central in this study is performance evaluation of several text classification methods to assess the feasibility of developing such a system. MATERIALS AND METHODS: Seven text classification methods are evaluated using a corpus of approximately 0.5 million nursing notes (5.5 million sentences) with 676 unique headings extracted from a Finnish university hospital. Several of these methods are based on artificial neural networks. Evaluation is first done in an automatic manner for all methods, then a manual error analysis is done on a sample. RESULTS: We find that a method based on a bidirectional long short-term memory network performs best with an average recall of 0.5435 when allowed to suggest 1 subject heading per sentence and 0.8954 when allowed to suggest 10 subject headings per sentence. However, other methods achieve comparable results. The manual analysis indicates that the predictions are better than what the automatic evaluation suggests. CONCLUSIONS: The results indicate that several of the tested methods perform well in suggesting the most appropriate subject headings on sentence level. Thus, we find it feasible to develop a text classification system that can support the use of standardized terminologies and save nurses time and effort on care documentation.

Unsupervised Phrase-Level Query Rewriting for Assisting Search in Clinical Free Text.

We report on the pilot evaluation of an experimental query-based search functionality that enables phrase-level query rewriting in an unsupervised way. It is intended for supporting search in clinical text. Qualitative evaluation is done by three clinicans using a prototype search tool. They report that they find the tested search functionality to be beneficial for making query-based searching in clinical text more efficient.

The nurse manager's role in perioperative settings: An integrative literature review.

AIM: To describe the nurse manager's role in perioperative settings. BACKGROUND: The nurse manager's role is complex and its content unclear. Research in this area is scarce. We need to better understand what this role is to support the nurse manager's work and decision-making with information systems. EVALUATION: An integrative literature review was conducted in May 2018. Databases CINAHL, Cochrane, PubMed and Web of Science were used together with a manual search. The review followed a framework especially designed for integrative reviews. Quality of the literature was analysed with an assessment tool. Nine studies published between 2001 and 2016 were included in the final review. KEY ISSUE: The findings from the review indicate that the nurse manager's role requires education and experience, and manifests in skills and tasks. A bachelor's degree with perioperative specialisation is the minimum educational requirement for a nurse manager. CONCLUSION: Research lacks a clear description of the nurse manager's role in perioperative settings. However, the role evolves by education. More education provides advanced skills and, thereby, more demanding tasks. Information technology could provide useful support for task management. IMPLICATIONS FOR NURSING MANAGEMENT: These findings can be used to better answer the current and future demands of the nurse manager's work.

Improving Layman Readability of Clinical Narratives with Unsupervised Synonym Replacement.

We report on the development and evaluation of a prototype tool aimed to assist laymen/patients in understanding the content of clinical narratives. The tool relies largely on unsupervised machine learning applied to two large corpora of unlabeled text - a clinical corpus and a general domain corpus. A joint semantic word-space model is created for the purpose of extracting easier to understand alternatives for words considered difficult to understand by laymen. Two domain experts evaluate the tool and inter-rater agreement is calculated. When having the tool suggest ten alternatives to each difficult word, it suggests acceptable lay words for 55.51% of them. This and future manual evaluation will serve to further improve performance, where also supervised machine learning will be used.