The Development of the “Monitoring Application for Inappropriate Expressions in Nursing Records”for PsyNACS©

Lengthening periods of hospitalization, increasing numbers of patients with age-related complications, and a shortage of nursing staff have been of great concern in medical psychiatry in Japan. Under these circumstances, countries such as Japan that face a super-aging society and a decline in the working-age population, have been recommended for use of advanced information and communications technology (ICT) to improve the efficiency of medical treatment and care. This study aims to develop the “Monitoring Application for Inappropriate Expressions in Nursing Records” for using PsyNACS©, which will enable psychiatric nursing plans to harness the advantages of ICT. The functions considered necessary are as follows: 1) identification of users who enter information;2) a necessary database for lists of inappropriate expressions;3) development of a matching function to recommend proper writing input and a warning function;and 4) a management function for an inappropriate expression list database. A demonstration experiment for developing the application in this study was conducted at a specialized psychiatric hospital. To introduce them to the application, nurses and nurse managers were informed about the system developed in this study, and a survey regarding their opinions on the functions of the application was conducted with ten nurse managers. The results were evaluated in terms of usefulness for nursing care, documentation, and the education of nurses from an ethical perspective. This study suggests that matching their input with an inappropriate expression database will allow nurses to record more appropriate expressions. From an ethical perspective, the ability to use appropriate expressions makes records more likely to withstand disclosure requests from patients and their families.

Development of Self-Rated Nursing Record Frequency for Delirium Care of Nurses in Acute Care Hospitals (NRDC-Acute)

Background: Nursing records play an important role in multidisciplinary collaborations in delirium care. This study aims to develop a self-rated nursing record frequency scale for delirium care among nurses in acute care hospitals (NRDC-Acute). Methods: A draft of the scale was developed after a literature review and meeting with researchers with experience in delirium care, and a master’s or doctoral degree in nursing. We identified 25 items on a 5-point Likert scale. Subsequently, an anonymous self-administered questionnaire survey was administered to 520 nurses from 41 acute care hospitals in Japan, and the reliability and validity of the scale were examined. Results: There were 232 (44.6%) respondents and 218 (41.9%) valid responses. The mean duration of clinical experience was 15.2 years (SD = 8.8). Exploratory factor analysis extracted 4 factors and 13 items for this scale. The model fit indices were GFI = 0.991, AGFI = 0.986, and SRMR = 0.046. The Cronbach’s alpha coefficient for the entire scale was .888. The four factors were named “Record of Pharmacological Delirium Care on Pro Re Nata (PRN)”, “Record of Non-Pharmacological Delirium Care”, “Record of Pharmacological Delirium Care on Regular Medication”, and “Record of Collaboration for Delirium Care”. Conclusion: The scale was relatively reliable and valid. Nurses in acute care hospitals can use this scale to identify and address issues related to the documentation of nursing records for delirium care.

A Multi-event Extraction Model for Nursing Records

Nursing records contain information on patients’treatment processes,which reflect the changes in patients’conditions and have legal effects.However,some of the written records of intensive care unit(ICU)nurses are incomplete according to our observations.This paper proposes an approach extracting structured nursing events from unstructured nursing records for detecting the missing items automatically.According to the PIO(problem,intervention,outcome)principle in the field of medical care,we propose event schemas for nursing records and annotate a Chinese nursing event extraction dataset(CNEED)on ICU nursing records.We find that several events may occur in a nursing record.Therefore,we present a multi-event extraction model for the nursing records.The experimental results demonstrate that our model achieves good results on CNEED and outperforms competitive methods on the multi-event argument attribution problem.By observing the results of automatic event extraction by our model,we detect missing items in the existing nursing records.This proves that our model can be used to help nurses check and improve the method of recording nursing processes.