Veuillez utiliser cette adresse pour citer ce document : https://hdl.handle.net/10953/1769
Titre: Prediction of the increase in health services demand based on the analysis of reasons of calls received by a customer relationship management
Autre(s) titre(s): -
Auteur(s): Ramos, Mª Isabel
Cubillas, Juan José
Jurado, Juan Manuel
Lopez, Wilfredo
Feito, Francisco R.
Quero, Manuel
Gonzalez, José María
Résumé: Currently, customer relationship management (CRM) tools are very important in our society because they provide a comunication channel to the healthcare system for patients. Salud Responde is a CRM that provides many health services for the entire population of Andalusia, in southern Spain. The number and frequenzy of phone calls received change along the year. They depend on many factors, such as weekdays, seasons, vaccination campaigns, environmental factors, pandemic periods, etc. All these are the main reasons number of health calls changes along the year. This variability makes that the current management of resources for offering emergency services based on historical data is inefficient. The factors, which influence the phone calls along the year, are different from one period to another. Therefore, it is clear to demand an improved in the current management system. In this context, the main goal for this research is to develop an expert system able to identify and analyze, using different data mining algorithms, the most relevant factors to predict the variability of health service demand. Thus, here, it is proposed a methodology in which using reasons calls received in the CRM as input data, it is possible to predict in advance the healthcare resources demand.
Mots-clés: prediction
health
service demand
Date de publication: 15-mar-2019
metadata.dc.description.sponsorship: -
Editeur: Wiley
Référence bibliographique: Ramos MI, Cubillas JJ, Jurado JM, et al. Prediction of the increase in health services demand based on the analysis of reasons of calls received by a customer relationship management. Int J Health Plann Mgmt. 2019; 34: e1215–e1222. https://doi.org/10.1002/hpm.2763
Collection(s) :DICGF-Artículos



Ce document est protégé par copyright


Ce document est autorisé sous une licence de type Licence Creative Commons
Creative Commons