Patterns, regional differentiation and future forecasting of medical resource allocation in Liaoning Province, China

This study aimed to characterize patterns of medical resource allocation, regional differentiation, and short-term future trends across prefecture-level cities in Liaoning Province, China, and to explore strategies for promoting balanced regional healthcare development. Temporal changes were assessed using annual growth rates and the Estimated Annual Percentage Change (EAPC). K-means clustering was used as an exploratory classification method to group cities according to standardized time-series-derived features, including long-term trend, temporal variability, and recent change. Global Moran’s I and Local Indicators of Spatial Association (LISA) were used to examine spatial dependence and local clustering. ARIMA models were used to generate short-term forecasts for 2023–2027. Medical resources in Liaoning Province increased significantly during 2005–2022, with substantial improvements in physicians, nurses, and hospital beds. However, resource expansion showed clear regional heterogeneity, with higher absolute levels concentrated in core cities such as Shenyang and Dalian. Spatial analysis revealed significant clustering characteristics, indicating persistent differences among regions. Forecasting results suggested continued growth in medical resources, but uneven development trajectories across cities remained. Although medical resource allocation has expanded substantially, persistent regional disparities and uneven spatial distribution patterns remain. Rather than indicating measured changes in scalar inequality, these results capture heterogeneous developmental trajectories among cities. Future policies should adopt differentiated allocation strategies and strengthen regional coordination.