Spatial analysis and prediction of the flow of patients to public health centres in a middle-sized Spanish city
AbstractHuman and medical resources in the Spanish primary health care centres are usually planned and managed on the basis of the average number of patients in previous years. However, sudden increases in patient demand leading to delays and slip-ups can occur at any time without warning. This paper describes a predictive model capable of calculating patient demand in advance using geospatial data, whose values depend directly on weather variables and location of the health centre people are assigned to. The results obtained here show that outcomes differ from one centre to another depending on variations in the variables measured. For example, patients aged 25-34 and 55-65 years visited health centres less often than all other groups. It was also observed that the higher the economic level, the fewer visits to health centres. From the temporal point of view, Monday was the day of greatest demand, while Friday the least. On a monthly basis, February had the highest influx of patients. Also, air quality and humidity influenced the number of visits; more visits during poor air quality and high relative humidity. The addition of spatial variables minimised the average error the predictive model from 7.4 to 2.4% and the error without considering spatial variables varied from the high of 11.8% in to the low of 2.5%. The new model reduces the values in the predictive model, which are more homogeneous than previously.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2016 Juan Jose Cubillas, Maria Isabel Ramos, Francisco Ramon Feito, Tomas Ureña
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.