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

doi:10.1007/978-981-16-5792-4_1

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

PUCE Ibarra

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

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 original	Inglés
Título de la publicación alojada	Communication and Smart Technologies - Proceedings of ICOMTA 2021
Editores	Álvaro Rocha, Daniel Barredo, Paulo Carlos López-López, Iván Puentes-Rivera
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	3-12
Número de páginas	10
ISBN (versión impresa)	9789811657917
DOI	https://doi.org/10.1007/978-981-16-5792-4_1
Estado	Publicada - 2022
Evento	International Conference on Communication and Applied Technologies, ICOMTA 2021 - Bogotá, Colombia Duración: 1 sep. 2021 → 3 sep. 2021

Serie de la publicación

Nombre	Smart Innovation, Systems and Technologies
Volumen	259 SIST
ISSN (versión impresa)	2190-3018
ISSN (versión digital)	2190-3026

Conferencia

Conferencia	International Conference on Communication and Applied Technologies, ICOMTA 2021
País/Territorio	Colombia
Ciudad	Bogotá
Período	1/09/21 → 3/09/21

Nota bibliográfica

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

Acceder al documento

10.1007/978-981-16-5792-4_1

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

Rivero-Albarrán, D. M., Rivas-Echeverría, F. I., Villareal, P. M., & Arciniegas, S. M. (2022). Absences Predictive Model of for the Outpatient Unit in a Public Hospital. En Á. Rocha, D. Barredo, P. C. López-López, & I. Puentes-Rivera (Eds.), Communication and Smart Technologies - Proceedings of ICOMTA 2021 (pp. 3-12). (Smart Innovation, Systems and Technologies; Vol. 259 SIST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-5792-4_1

Rivero-Albarrán, D. M. ; Rivas-Echeverría, F. I. ; Villareal, P. M. et al. / Absences Predictive Model of for the Outpatient Unit in a Public Hospital. Communication and Smart Technologies - Proceedings of ICOMTA 2021. editor / Álvaro Rocha ; Daniel Barredo ; Paulo Carlos López-López ; Iván Puentes-Rivera. Springer Science and Business Media Deutschland GmbH, 2022. pp. 3-12 (Smart Innovation, Systems and Technologies).

@inproceedings{b670a0038e144676b9a0b2fae28d89f5,

title = "Absences Predictive Model of for the Outpatient Unit in a Public Hospital",

abstract = "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.",

keywords = "Data analytics, External consultation services, Time series",

author = "Rivero-Albarr{\'a}n, {D. M.} and Rivas-Echeverr{\'i}a, {F. I.} and Villareal, {P. M.} and Arciniegas, {S. M.}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Communication and Applied Technologies, ICOMTA 2021 ; Conference date: 01-09-2021 Through 03-09-2021",

year = "2022",

doi = "10.1007/978-981-16-5792-4_1",

language = "English",

isbn = "9789811657917",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "3--12",

editor = "{\'A}lvaro Rocha and Daniel Barredo and L{\'o}pez-L{\'o}pez, {Paulo Carlos} and Iv{\'a}n Puentes-Rivera",

booktitle = "Communication and Smart Technologies - Proceedings of ICOMTA 2021",

}

Rivero-Albarrán, DM, Rivas-Echeverría, FI, Villareal, PM & Arciniegas, SM 2022, Absences Predictive Model of for the Outpatient Unit in a Public Hospital. En Á Rocha, D Barredo, PC López-López & I Puentes-Rivera (eds.), Communication and Smart Technologies - Proceedings of ICOMTA 2021. Smart Innovation, Systems and Technologies, vol. 259 SIST, Springer Science and Business Media Deutschland GmbH, pp. 3-12, International Conference on Communication and Applied Technologies, ICOMTA 2021, Bogotá, Colombia, 1/09/21. https://doi.org/10.1007/978-981-16-5792-4_1

Absences Predictive Model of for the Outpatient Unit in a Public Hospital. / Rivero-Albarrán, D. M.; Rivas-Echeverría, F. I.; Villareal, P. M. et al.
Communication and Smart Technologies - Proceedings of ICOMTA 2021. ed. / Álvaro Rocha; Daniel Barredo; Paulo Carlos López-López; Iván Puentes-Rivera. Springer Science and Business Media Deutschland GmbH, 2022. p. 3-12 (Smart Innovation, Systems and Technologies; Vol. 259 SIST).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

TY - GEN

T1 - Absences Predictive Model of for the Outpatient Unit in a Public Hospital

AU - Rivero-Albarrán, D. M.

AU - Rivas-Echeverría, F. I.

AU - Villareal, P. M.

AU - Arciniegas, S. M.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Data analytics

KW - External consultation services

KW - Time series

UR - http://www.scopus.com/inward/record.url?scp=85116894487&partnerID=8YFLogxK

U2 - 10.1007/978-981-16-5792-4_1

DO - 10.1007/978-981-16-5792-4_1

M3 - Conference contribution

AN - SCOPUS:85116894487

SN - 9789811657917

T3 - Smart Innovation, Systems and Technologies

SP - 3

EP - 12

BT - Communication and Smart Technologies - Proceedings of ICOMTA 2021

A2 - Rocha, Álvaro

A2 - Barredo, Daniel

A2 - López-López, Paulo Carlos

A2 - Puentes-Rivera, Iván

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Communication and Applied Technologies, ICOMTA 2021

Y2 - 1 September 2021 through 3 September 2021

ER -

Rivero-Albarrán DM, Rivas-Echeverría FI, Villareal PM, Arciniegas SM. Absences Predictive Model of for the Outpatient Unit in a Public Hospital. En Rocha Á, Barredo D, López-López PC, Puentes-Rivera I, editores, Communication and Smart Technologies - Proceedings of ICOMTA 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 3-12. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-16-5792-4_1

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

Resumen

Serie de la publicación

Conferencia

Nota bibliográfica

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto