Assessment on Distributional Fairness of Physical Rehabilitation Resource Allocation: Geographic Accessibility Analysis Integrating Google Rating Mechanism

Abstract

Identifying and treating co-existing diseases are essential in healthcare for the elderly, while physical rehabilitation care teams can provide interdisciplinary geriatric care for the elderly. To evaluate the appropriateness of demand and supply between the population at demand and physical rehabilitation resources, a comparative analysis was carried out in this study. Our study applied seven statistical indices to assess five proposed methods those considered different factors for geographic accessibility analysis. Google ratings were included in the study as a crucial factor of choice probability in the equation for calculating the geographic accessibility scores, because people’s behavioral decisions are increasingly dependent on online rating information. The results showed that methods considering distances, the capacity of hospitals, and Google ratings’ integrally generated scores, are in better accordance with people’s decision-making behavior when they determine which resources of physical rehabilitation to use. It implies that concurrent considerations of non-spatial factors (online ratings and sizes of resource) are important. Our study proposed an integrated assessment method of geographical accessibility scores, which includes the spatial distribution, capacity of resources and online ratings in the mechanism. This research caters to countries that provide citizens with a higher degree of freedom in their medical choices and allows these countries to improve the fairness of resource allocation, raise the geographic accessibilities of physical rehabilitation resources, and promote aging in place.

Integrating Socioeconomic Status and Spatial Factors to Improve the Accessibility of Community Care Resources Using Maximum-Equity Optimization of Supply Capacity Allocation

“…First, this study compensates for the limitations of previous studies, some of which took only spatial factors into consideration [40,47,57,58] or positioned population demand at the geometrically weighted center points of the population [18,47,48]. The limitations of exact locations of the population demand were addressed by using BSA data to solve problems of MAUP.…”

“…Wang [4] used distance weighting and a supply scale to calculate the probability of selection from demand point to each supply point and proposed the three-step floating catchment area (3SFCA) method. In response to the increasing reliance on the evaluation of information from the internet for public decision-making behavior in the digital age, Wu et al [18] proposed the enhanced three-stage floating catchment area (E3SFCA) method, which integrates supply capacity, distance decay, and a Google rating mechanism to calculate the probability of selection.…”

“…This study demonstrated that 600 m is close to the real travel distance of the elderly in Xi'an, China. Wu, Tseng, and Lin [18] applied the three-step floating catchment area method and considered distance, hospital capacity, and Google ratings to produce results that are in better accordance with people's decision-making behavior to assess rehabilitation resource allocation in the community.…”

Priority Criteria for Community-Based Care Resource Allocation for Health Equity: Socioeconomic Status and Demographic Characteristics in the Multicriteria Decision-Making Method

“…For example, the number of community care stations is much lower than that of villages in 2021 [ 8 ], which affects older adults’ access to resources. Therefore, the researchers [ 9 , 10 , 11 , 12 , 13 , 14 ] suggested that a fair allocation of resources through resource optimization, which considers spatial and nonspatial factors, would help reduce inequities in community health.…”

GIS-Based Medical Resource Evaluation Method

“…In the past, the evaluation methods of medical resource allocation were mostly based on traditional databases to establish information systems and quantitatively evaluate medical resource allocation capabilities by using indicators such as the balance of medical resource supply and demand, the professional skills of medical sta , and medical technicians with a population of 1,000 [14][15][16]. However, the traditional database lacks spatial expression ability, which limits its ability to provide decision-makers with information on the spatial distribution of medical resources and decision support [17][18][19][20].…”