Using Unsupervised Learning Methods to Explore the Trajectory of Long-Term Care Service in Taiwan

Abstract Background Understanding the temporal trends in long-term care (LTC) utilization enables more effective planning by government and service providers. This study employed machine learning methods to explore LTC utilization trajectories among care recipients in a southern county of Taiwan. Methods Administrative data from the government-funded LTC programme was utilized, with a total of 24,614 participants included after excluding those with less than six months of data. Daily service utilization records, mainly focusing on caregiving services, were aggregated, and the number of days each participant used LTC services within every 30-day period post-application was calculated. Utilization rates during 6 and 12 months post-application were separately analyzed. K-means clustering, an unsupervised machine learning method, was employed to select the optimal classification and identify LTC utilization trajectories using 10-fold cross validation. Temporal frequency variations of each caregiving service within each trajectory group were visualized using heatmaps. Results In the analysis of LTC utilization within the first 6 months post-application, trajectories were classified into 7 groups; while within 12 months, trajectories were divided into 4 groups. These trajectories can be categorized into 4 distinct patterns: High-Stable (6 mo: 21.3%; 12 mo: 35.7%), Low-Stable (6 mo: 33.1%; 12 mo: 37.2%), High-Decrease (6 mo: 12.8%; 12 mo: 10.7%), and Low-Increase (6 mo: 32.7%; 12 mo: 16.5%). We further explored differences in socio-demographic characteristics and needs factors among trajectory groups. Heatmaps were used to illustrate temporal variations in the utilization rates of specific service items, which played a significant role in determining the trajectories. Conclusions K-means clustering is useful in identifying distinct patterns among LTC users. Tailored service delivery strategies can be designed to meet the diverse needs of these different user groups. Key messages • The unsupervised machine learning method distinguished various temporal dynamic patterns in LTC utilization. • Tailored service delivery strategies can be designed to meet the diverse needs of these different user groups.