Takahiro Kubo

Nurses play a crucial role in healthcare, directly influencing the quality of patient care. Facing a global nursing shortage, there is an urgent need for strategies to enhance nursing efficiency and care quality. This foundational study explores an NLP-based approach to determine NANDA nursing diagnoses, leveraging both subjective and objective patient data recorded by nurses. Employing text data similarity analysis and a prototype predictive model, our research aims to refine the nursing assessment process and pave the way for the potential automation of nursing diagnoses. This work highlights the potential of AI to support nursing practices and sets a platform for future research to fully realize AI’s benefits in addressing the challenges posed by the nursing shortage.

Nurses play a crucial role in healthcare, directly influencing patient care quality. With a global nursing shortage, enhancing nursing efficiency and care quality is urgently needed. This foundational study explores the advantages of text and data processing techniques to determine NANDA-I nursing diagnoses using both subjective and objective patient data recorded by nurses. By employing text data similarity analysis and a prototype of the predictive model, our research aims to rene the nursing assessment process and facilitate the automation of nursing diagnoses. This work highlights the accuracy of BERT-based assessment pattern matching to support nursing practices and sets a platform for future research to address the nursing shortage effectively.