The Application of Geographic Information Systems for Neonatal Transport Services with Sub-Districts` Spatial Accessibility Analysis

Many factors may have influence on the process of patient transport, and one of them is a geospatial accessibility. We assume that primary, secondary and tertiary hospitals are main places for providing neonatal care in Southern Alberta and analyze each sub-districts` spatial accessibility to them based on travel time and spatial difference within the city. Objective: To estimate the neonatal transport accessibility among rural hospitals of Southern Alberta (SA) as it related to geographic location, aimed to determine most challenging types of health services at the rural level that could provide a reference for the future teaching and training of local medical personnel. Methods: We assessed spatial distribution relationship between tertiary care center and rural hospitals by using KD2SFCA method, calculated accessibility and spatial variation of medical services with spatial autocorrelation analysis method. We used the Environmental Systems Research Institute’s (ESRI) ArcGIS software to measure the distance along major roads between each rural hospital and tertiary care center and plot selected data on the maps. We used Neonatal Transport charts for information about actual transport duration, TRIPS and SNAP II scores were used to evaluated severity of patient and urgency for each transport. Conclusion: Considering the importance of timely access to the specialized Neonatal Services the travel time instead of the spatial distance from rural hospitals to tertiary center was used in the calculation of spatial accessibility. Transport times were analyzed and their mean value is taken as a reference, to use as thresholds in the KD2FSCA method. Results of the 15 analysis on accessibilities using transport time present similar spatial distribution for rural centers. The quantitative spatial autocorrelation analysis further proves that there are obvious areas with high value clustering and low value clustering. In view of spatial distribution, the medical resources of the SA distributed equally