Enhancing Maintenance Management Effectiveness of Healthcare Facilities through Natural Language Processing

Existing maintenance management systems of healthcare facilities tend to have vague facility failure classifications and rely on manual request interpretations and task assignments, which are time-consuming and human error-prone. To improve its effectiveness, in this paper, the authors utilize natural language processing (NLP) techniques to automate the interrogation of the textual data of maintenance work orders (MWOs), to reduce manual efforts, and to improve efficiency. Five word-splitters (i.e., FoolNLTK, Jieba, Stanford NLP, SnowNLP, and THULAC) and four machine learning algorithms (i.e., decision tree, random forest, eXtreme Gradient Boosting, and support vector machines) were configured to automate failure identification and staff assignment from MWOs. Experimental results on multiple MWOs datasets showed a 0.80 accuracy for failure identification and a 0.83 accuracy for staff assignment were achieved, indicating a promising multiclass classification performance, which can help transit maintenance staff assignment from a labor-intensive process to a more automated one, improving overall efficiency.