Practical Applicability of an AI-Based Automated Classification Model in Elderly Care Institutions

This study evaluates the practical applicability of an artificial intelligence (AI)-based automated text classification model in elderly care institutions. While prior research has primarily focused on improving algorithmic performance, limited empirical attention has been given to whether automated outputs demonstrate sufficient alignment with existing manual classification practices in real operational environments. This study reanalyzes a categorized dataset of 1,224 elderly care records to examine predictive agreement from an organizational applicability perspective. A logistic regression classifier with hyperparameter optimization was implemented, and performance was assessed using accuracy, precision, recall, and F1-score metrics. The optimized model achieved an accuracy of 0.790 and a weighted F1-score of 0.780. Category-level analysis indicated stable performance across primary operational classes, although variation in recall was observed in specific categories. The findings suggest that AI-assisted classification may function as a structured support mechanism within institutional documentation workflows. This study contributes empirical evidence regarding feasibility of practical implementation without asserting direct organizational performance transformation.