Absences Predictive Model of for the Outpatient Unit in a Public Hospital

D. M. Rivero-Albarrán, F. I. Rivas-Echeverría, P. M. Villareal, S. M. Arciniegas

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva


The quality must be present external in consultation services, either to satisfy the needs of the users and/or to maximize the use of the resources. For this reason, administrators require the necessary information and knowledge to support them in decision-making processes. This paper presents a time series-based model, to predict absenteeism and attendance at the outpatient service. The CRISP-DM methodology, typical of data analytics, was used to explore the models. It was found that the ARIMA model was the one that obtained the lowest absolute and quadratic error. In addition, based on the time series predictions, it was determined that the number of absences to external medicine consultations is around 10% per day. This information can be used to reduce the attention time lost due to absences.

Idioma originalInglés
Título de la publicación alojadaCommunication and Smart Technologies - Proceedings of ICOMTA 2021
EditoresÁlvaro Rocha, Daniel Barredo, Paulo Carlos López-López, Iván Puentes-Rivera
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas10
ISBN (versión impresa)9789811657917
EstadoPublicada - 2022
EventoInternational Conference on Communication and Applied Technologies, ICOMTA 2021 - Bogotá, Colombia
Duración: 1 sep. 20213 sep. 2021

Serie de la publicación

NombreSmart Innovation, Systems and Technologies
Volumen259 SIST
ISSN (versión impresa)2190-3018
ISSN (versión digital)2190-3026


ConferenciaInternational Conference on Communication and Applied Technologies, ICOMTA 2021

Nota bibliográfica

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.


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