Use of Data Mining to Predict the Influx of Patients to Primary Healthcare Centres and Construction of an Expert System

In any productive sector, predictive tools are crucial for optimal management and decision-making. In the health sector, it is especially important to have information available in advance, as this not only means optimizing resources, but also improving patient care. This work focuses on the managem...

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Bibliographic Details
Published in:Applied sciences 2022-11, Vol.12 (22), p.11453
Main Authors: Cubillas, Juan J., Ramos, María I., Feito, Francisco R.
Format: Article
Language:English
Subjects:
Algorithms
Applications programs
Chronic obstructive pulmonary disease
Data mining
Decision making
Decision support systems
Demand
expert system
Expert systems
Health care
Health care facilities
Health facilities
Knowledge discovery
Meteorological data
Patient satisfaction
Patients
Primary care
primary health care
resource optimization
Spatial data
Stroke
Variables
Workflow
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Summary:In any productive sector, predictive tools are crucial for optimal management and decision-making. In the health sector, it is especially important to have information available in advance, as this not only means optimizing resources, but also improving patient care. This work focuses on the management of healthcare resources in primary care centres. The main objective of this work is to develop a model capable of predicting the number of patients who will demand health care in a primary care centre on a daily basis. This model is integrated into a decision support system that is accessible and easy to use by the manager through a web application. In this case, data from a primary care centre in the city of Jaén, Spain, were used. The model was estimated using spatial-temporal training data, the daily health demand data in that centre for five years, and a series of meteorological data. Different regression algorithms have been employed. The workflow requires selecting the parameters that influence the health demand prediction and discarding those that distort the model. The main contribution of this research is the daily prediction of the number of patients attending the health centre with absolute errors better than 3%, which is crucial for decision-making on the sizing of health resources in a primary care health centre.
ISSN:2076-3417
2076-3417
DOI:10.3390/app122211453