Modeling nursing care tasks in simulated emergency scenarios: insights for clinical training and practice.

Rapid nurse decision-making is needed to detect patient deterioration and prevent mortality. Current approaches to support nurses' decisions involve diagnostic data processing and providing a decision with little explanation. Our team aimed to demonstrate the utility of attention architecture to model sequential nurse-patient care actions. Experienced nurses and students completed patient care simulations. Nurse actions were systematically coded and analyzed using our model, consisting of an attention encoder to sequentially process and predict nurse behavior. Performance of our model was compared to recurrent neural networks and long-short term memory models based on accuracy, precision, recall, and F1 score. Behavioral data from 24 nurses (11 experienced nurses and 13 nursing students) were collected during patient care simulations. Nineteen unique types of actions were distilled down to 8 common actions. There were 33 episodes captured (i.e., 33 unique sequences of patient care actions), including a total of 1024 actions (i.e., an average of 31 ± 11 actions). Results showed that the attention model outperformed the other models on all metrics except for precision. Our team demonstrated that machine learning can model sequential nurse actions. These results could be leveraged to provide real-time guidance to support novice nurses' decision-making in the simulated environment.