Towards automated classification of intensive care nursing narratives

Background: Nursing narratives are an important part of patient documentation, but the possibilities to utilize them in the direct care process are limited due to the lack of proper tools. One solution to facilitate the utilization of narrative data could be to classify them according to their content. 

Objectives: Our objective is to address two issues related to designing an automated classifier: domain experts' agreement on the content of classes Breathing, Blood Circulation and Pain, as well as the ability of a machine-learning-based classifier to learn the classification patterns of the nurses. 

Methods: The data we used were a set of Finnish intensive care nursing narratives, and we used the regularized least-squares (RLS) algorithm for the automatic classification. The agreement of the nurses was assessed by using Cohen's κ, and the performance of the algorithm was measured using area under ROC curve (AUC). 

Results: On average, the values of κ were around 0.8. The agreement was highest in the class Blood Circulation, and lowest in the class Breathing. The RLS algorithm was able to learn the classification patterns of the three nurses on an acceptable level; the values of AUC were generally around 0.85. 

Conclusions: Our results indicate that the free text in nursing documentation can be automatically classified and this can offer a way to develop electronic patient records.