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Prediction of the increase in health services demand based on the analysis of reasons of calls received by a customer relationship management
Author(s) -
Ramos Mª. Isabel,
Cubillas Juan José,
Jurado Juan Manuel,
Lopez Wilfredo,
Feito Francisco R.,
Quero Manuel,
Gonzalez Jose Maria
Publication year - 2019
Publication title -
the international journal of health planning and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 41
eISSN - 1099-1751
pISSN - 0749-6753
DOI - 10.1002/hpm.2763
Subject(s) - context (archaeology) , phone , health care , business , health services , service (business) , population , process management , marketing , operations research , computer science , geography , engineering , medicine , environmental health , economics , philosophy , linguistics , archaeology , economic growth
Summary 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.