Methodology for Real-Time Evaluation of Geographic Health Care Resource Allocation: Iwate Prefectural Hospitals

Abstract

The effective utilization and assessment of medical resources have become a common concern for scholars in various countries due to the impact of the COVID-19 pandemic. This study deals with a method to monitor medical resource allocation in real-time and verify the effectiveness of the proposed method with actual data. In this work, we selected Iwate Prefecture in northeastern Japan based on the geographical characteristics and social environment. By collecting data from the Japanese Ministry of Land, Infrastructure, Transport, and Tourism and Welfare (MLIT), we clustered population centers in Iwate Prefecture, and found the clustering centers in densely populated areas from the k-means algorithm. Subsequently, to compare the distribution of county-level medical resources across different secondary care areas, we selected the indicators of Iwate Prefectural Hospitals from the Hospital Intelligence Agency. We classified 19 prefectural hospitals in Iwate Prefecture into four different clusters using the spectral clustering algorithm. The clustering results revealed that all hospitals close to the " clustering centers in densely populated areas" were Iwate prefectural disaster stronghold hospitals. Moreover, we found that these hospitals performed well in operational indicators. Only the Ninohe prefectural hospital in the Ninohe medical area was found not located in a population center. However, it still performs well in terms of business indicators since the Ninohe medical area has a high proportion of public hospitals and the Ninohe prefectural hospital plays an important role. Hence, the government should fully consider geographical characteristics when considering hospital restructuring. We used a real data set to demonstrate the validity of the proposed technique, providing a theoretical basis for the government's healthcare policy.

Equilibrium of Tiered Healthcare Resources during the COVID-19 Pandemic in China: A Case Study of Taiyuan, Shanxi Province

“…Among them, Interviewees 8 and 10, who were aged and needed long-term medication, said that access to medical and health services was inconvenient, and there was an urgent need for home doctor services or additional health centers. This was similar to the findings of Zhifei Ma and Xuchuan Sun [ 11 , 32 ] who showed that medical and health resources were clustered in large cities and central areas. Therefore, for the implementation of basic public health services, it is necessary to strengthen management and redeployment, optimize the spatial distribution of medical and health resources, increase resource supply in surrounding counties, such as Yangqu, Loufan, Gujiao, and Qingxu, and improve the construction of basic medical services in a 15 min radius.…”

“…Healthcare is one of the basic tasks of modern countries. Scholars in Japan, Poland, Austria, and Brazil have investigated and analyzed the allocation of healthcare resources in their countries, and it is proposed that the quantity of healthcare resources is balanced and that healthcare resource utilization is optimized [ 11 , 12 , 13 , 14 ].…”

Research on the Equity and Influencing Factors of Medical and Health Resources Allocation in the Context of COVID-19: A Case of Taiyuan, China

“…The findings show that the inequitable allocation of medical and health resources exists at all scales studied, with geographic allocation being less equitable and differences in medical and health resources allocation being caused between regions and within regional differences [ 39 , 40 , 41 , 42 , 43 , 44 ]. Affected by the economy, population, society and policy, the allocation of medical and health resources varies greatly among regions [ 45 ]. The influence of various factors on medical and health resources allocation among different regions, global and local factors should be judged reasonably and resources allocation strategies should be tailored to local conditions [ 46 ].…”

Analysis of spatial pattern and influencing factors of private clinics in the main urban area of Guiyang in China from 2021 to 2022 based on multi-source data

“…Most of the existing studies on the influencing factors of medical resource allocation use qualitative descriptions [ 30 , 31 ] or investigate the drivers of medical resource distribution through the weighted regression models and superposition analysis [ 32 , 33 ]. However, there are few quantitative studies for each driver, which fail to reveal the spatial structural characteristics of medical resources and the impact of their influencing factors.…”