A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital

A significant problem in outpatient perinatology departments is the long waiting time for pregnant women to receive an appointment. In this respect, appointment delays are related to patient dissatisfaction, no shows and sudden infant death syndrome. This paper aims to model and evaluate improvement...

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Autores:
Ortiz Barrios, M.
Jiménez Delgado, G.
De Ávila Villalobos, J.
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/3283
Acceso en línea:
https://hdl.handle.net/11323/3283
https://repositorio.cuc.edu.co/
Palabra clave:
Discrete-event simulation (DES)
Healthcare
Outpatient care appointment lead-time
Perinatology
Simulación de eventos discretos (DES).
Atención médica
Citas para atención ambulatoria
Tiempo de espera
Perinatología
Rights
openAccess
License
Attribution-NonCommercial-ShareAlike 4.0 International
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/3283
network_acronym_str RCUC2
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repository_id_str
dc.title.spa.fl_str_mv A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital
dc.title.translated.spa.fl_str_mv Un enfoque de simulación por computadora para reducir el tiempo de cita en los departamentos de perinatología ambulatoria: un estudio de caso en un hospital materno-infantil
title A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital
spellingShingle A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital
Discrete-event simulation (DES)
Healthcare
Outpatient care appointment lead-time
Perinatology
Simulación de eventos discretos (DES).
Atención médica
Citas para atención ambulatoria
Tiempo de espera
Perinatología
title_short A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital
title_full A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital
title_fullStr A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital
title_full_unstemmed A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital
title_sort A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital
dc.creator.fl_str_mv Ortiz Barrios, M.
Jiménez Delgado, G.
De Ávila Villalobos, J.
dc.contributor.author.spa.fl_str_mv Ortiz Barrios, M.
Jiménez Delgado, G.
De Ávila Villalobos, J.
dc.subject.spa.fl_str_mv Discrete-event simulation (DES)
Healthcare
Outpatient care appointment lead-time
Perinatology
Simulación de eventos discretos (DES).
Atención médica
Citas para atención ambulatoria
Tiempo de espera
Perinatología
topic Discrete-event simulation (DES)
Healthcare
Outpatient care appointment lead-time
Perinatology
Simulación de eventos discretos (DES).
Atención médica
Citas para atención ambulatoria
Tiempo de espera
Perinatología
description A significant problem in outpatient perinatology departments is the long waiting time for pregnant women to receive an appointment. In this respect, appointment delays are related to patient dissatisfaction, no shows and sudden infant death syndrome. This paper aims to model and evaluate improvement proposals to outpatient care delivery by applying computer simulation approaches. First, suitable data is collected and analyzed. Then, a discrete-event simulation (DES) model is created and validated to determine whether it is statistically equivalent to the current system. Afterward, the average appointment lead-time is calculated and studied. Finally, improvement proposals are designed and pretested by simulation modelling and statistical comparison tests. A case study of an outpatient perinatology department from a maternal-child is shown to validate the effectiveness of DES to fully understand and manage healthcare systems. The results evidenced that changes to care delivery can be effectively assessed and appointment lead-times may be significantly reduced based on the proposed framework within this paper.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017-06-25
dc.date.accessioned.none.fl_str_mv 2019-05-10T15:19:13Z
dc.date.available.none.fl_str_mv 2019-05-10T15:19:13Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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dc.language.iso.none.fl_str_mv eng
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dc.relation.references.spa.fl_str_mv Jameson, J.L., Longo, D.L.: Precision medicine-personalized, problematic, and promising. Obstetr. Gynecol. Surv. 70(10), 612–614 (2015) Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 3 (2014) IBM-The FOUR V’s of Big Data. http://www-01.ibm.com/software/data/bigdata/. Accessed 2017 Sagiroglu, S., Sinanc, D.: Big data: a review. In: Proceedings of International Conference on Collaboration Technologies and Systems, pp. 42–47 (2013) Belle, A., Thiagarajan, R., Soroushmehr, S., et al.: Big Data Analytics in Healthcare. BioMed Research International (2015) Alickovic, E., Subasi, A.: Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier. J. Med. Syst. 40(4), 1–12 (2016) Constantinou, A.C., Fenton, N., Marsh, W., et al.: From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artif. Intell. Med. 67, 75–93 (2016) Woosley, R., Whyte, J., Mohamadi, A., et al.: Medical decision support systems and therapeutics: the role of autopilots. Clin. Pharmacol. Ther. 99(2), 161–164 (2016) Cambria, E., Olsher, D., Rajagopal, D.: SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of Twenty-Eighth AAAI Conference on Artificial Intelligence (2014) Mirzaa, G.M., Millen, K.J., Barkovich, A.J., et al.: The developmental brain disorders database (DBDB): a curated neurogenetics knowledge base with clinical and research applications. Am. J. Med. Genet. Part A 164(6), 1503–1511 (2014) Taglang, G.D., Jackson, B.: Use of “big data” in drug discovery and clinical trials. Gynecol.Oncol. 141(1), 17–23 (2016) Vicini, P., Fields, O., Lai, E., et al.: Precision medicine in the age of big data: the present and future role of large-scale unbiased sequencing in drug discovery and development. Clin. Pharmacol. Ther. 99(2), 198–207 (2016) Holzinger, A.: Trends in interactive knowledge discovery for personalized medicine: cognitive science meets machine learning. IEEE Intell. Inform. Bull. 15(1), 6–14 (2014) Kim, D., Joung, J.G., Sohn, K.A., et al.: Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction. J. Am. Med. Inform. Assoc. 22(1), 109–120 (2015) Kamsu-Foguem, B., Tchuenté-Foguem, G., Foguem, C.: Using conceptual graphs for clinical guidelines representation and knowledge visualization. Inf. Syst. Front. 16(4), 571–589(2014) Zhang, D., Xie, Y., Li, M., et al.: Construction of knowledge graph of traditional Chinese medicine based on the ontology. Technol. Intell. Eng. 3(1), 8 (2017) Yu, T., Li, J., Yu, Q., et al.: Knowledge graph for TCM health preservation: design, construction, and applications. Artif. Intell. Med. 77, 48–52 (2017) Shi, L., Li, S., Yang, X., et al.: Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services. BioMed Research International (2017) Mikolov, T., Kombrink, S., Deoras, A., et al.: RNNLM-recurrent neural network language modeling toolkit. In: Proceedings of the 2011 ASRU Workshop, pp. 196–201 (2011) Ou, A., Lin, X., Li, G., et al.: LEVIS: a hypertension dataset in traditional Chinese medicine. In: Proceedings of Bioinformatics and Biomedicine (BIBM), pp. 192–197 (2013) State Administration of Traditional Chinese Medicine of People’s Republic of China: Clinic terminology of traditional Chinese medical diagnosis and treatment–Syndromes. Standards Press of China, Beijing, GB/T 16751.2–1997 (1997) Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007) Sorower, M.S.: A Literature Survey on Algorithms for Multi-label Learning. Oregon State University, Corvallis (2010)
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spelling Ortiz Barrios, M.Jiménez Delgado, G.De Ávila Villalobos, J.2019-05-10T15:19:13Z2019-05-10T15:19:13Z2017-06-25978-331969181-703029743https://hdl.handle.net/11323/3283Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/A significant problem in outpatient perinatology departments is the long waiting time for pregnant women to receive an appointment. In this respect, appointment delays are related to patient dissatisfaction, no shows and sudden infant death syndrome. This paper aims to model and evaluate improvement proposals to outpatient care delivery by applying computer simulation approaches. First, suitable data is collected and analyzed. Then, a discrete-event simulation (DES) model is created and validated to determine whether it is statistically equivalent to the current system. Afterward, the average appointment lead-time is calculated and studied. Finally, improvement proposals are designed and pretested by simulation modelling and statistical comparison tests. A case study of an outpatient perinatology department from a maternal-child is shown to validate the effectiveness of DES to fully understand and manage healthcare systems. The results evidenced that changes to care delivery can be effectively assessed and appointment lead-times may be significantly reduced based on the proposed framework within this paper.Un problema importante en los departamentos de perinatología para pacientes ambulatorios es el largo tiempo de espera para que las mujeres embarazadas reciban una cita. En este sentido, los retrasos en las citas están relacionados con la insatisfacción del paciente, la ausencia de asistencia y el síndrome de muerte súbita del lactante. Este documento tiene como objetivo modelar y evaluar propuestas de mejora para la prestación de atención ambulatoria mediante la aplicación de enfoques de simulación por computadora. En primer lugar, se recogen y analizan los datos adecuados. Luego, se crea un modelo de simulación de eventos discretos (DES) y se valida para determinar si es estadísticamente equivalente al sistema actual. Posteriormente, se calcula y estudia el tiempo promedio de entrega de la cita. Finalmente, las propuestas de mejora se diseñan y prueban mediante modelos de simulación y pruebas de comparación estadística. Se muestra un estudio de caso de un departamento de perinatología ambulatoria de una madre-hijo para validar la efectividad del DES para comprender y administrar completamente los sistemas de salud. Los resultados evidenciaron que los cambios en la prestación de la atención pueden evaluarse eficazmente y los plazos de entrega de las citas pueden reducirse significativamente en función del marco propuesto en este documento.Ortiz Barrios, M.-086d30c0-dad9-49bf-bd17-4e368a624cfb-600Jiménez Delgado, G.-78f5ef78-4f12-48c3-830e-e4e1f037b070-600De Ávila Villalobos, J.-e55bcb42-c0d8-4ff1-a622-59050c4b946a-600engUniversidad De La Costa10.1007/978-3-319-69182-4_4Jameson, J.L., Longo, D.L.: Precision medicine-personalized, problematic, and promising. Obstetr. Gynecol. Surv. 70(10), 612–614 (2015) Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 3 (2014) IBM-The FOUR V’s of Big Data. http://www-01.ibm.com/software/data/bigdata/. Accessed 2017 Sagiroglu, S., Sinanc, D.: Big data: a review. In: Proceedings of International Conference on Collaboration Technologies and Systems, pp. 42–47 (2013) Belle, A., Thiagarajan, R., Soroushmehr, S., et al.: Big Data Analytics in Healthcare. BioMed Research International (2015) Alickovic, E., Subasi, A.: Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier. J. Med. Syst. 40(4), 1–12 (2016) Constantinou, A.C., Fenton, N., Marsh, W., et al.: From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artif. Intell. Med. 67, 75–93 (2016) Woosley, R., Whyte, J., Mohamadi, A., et al.: Medical decision support systems and therapeutics: the role of autopilots. Clin. Pharmacol. Ther. 99(2), 161–164 (2016) Cambria, E., Olsher, D., Rajagopal, D.: SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of Twenty-Eighth AAAI Conference on Artificial Intelligence (2014) Mirzaa, G.M., Millen, K.J., Barkovich, A.J., et al.: The developmental brain disorders database (DBDB): a curated neurogenetics knowledge base with clinical and research applications. Am. J. Med. Genet. Part A 164(6), 1503–1511 (2014) Taglang, G.D., Jackson, B.: Use of “big data” in drug discovery and clinical trials. Gynecol.Oncol. 141(1), 17–23 (2016) Vicini, P., Fields, O., Lai, E., et al.: Precision medicine in the age of big data: the present and future role of large-scale unbiased sequencing in drug discovery and development. Clin. Pharmacol. Ther. 99(2), 198–207 (2016) Holzinger, A.: Trends in interactive knowledge discovery for personalized medicine: cognitive science meets machine learning. IEEE Intell. Inform. Bull. 15(1), 6–14 (2014) Kim, D., Joung, J.G., Sohn, K.A., et al.: Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction. J. Am. Med. Inform. Assoc. 22(1), 109–120 (2015) Kamsu-Foguem, B., Tchuenté-Foguem, G., Foguem, C.: Using conceptual graphs for clinical guidelines representation and knowledge visualization. Inf. Syst. Front. 16(4), 571–589(2014) Zhang, D., Xie, Y., Li, M., et al.: Construction of knowledge graph of traditional Chinese medicine based on the ontology. Technol. Intell. Eng. 3(1), 8 (2017) Yu, T., Li, J., Yu, Q., et al.: Knowledge graph for TCM health preservation: design, construction, and applications. Artif. Intell. Med. 77, 48–52 (2017) Shi, L., Li, S., Yang, X., et al.: Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services. BioMed Research International (2017) Mikolov, T., Kombrink, S., Deoras, A., et al.: RNNLM-recurrent neural network language modeling toolkit. In: Proceedings of the 2011 ASRU Workshop, pp. 196–201 (2011) Ou, A., Lin, X., Li, G., et al.: LEVIS: a hypertension dataset in traditional Chinese medicine. In: Proceedings of Bioinformatics and Biomedicine (BIBM), pp. 192–197 (2013) State Administration of Traditional Chinese Medicine of People’s Republic of China: Clinic terminology of traditional Chinese medical diagnosis and treatment–Syndromes. Standards Press of China, Beijing, GB/T 16751.2–1997 (1997) Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007) Sorower, M.S.: A Literature Survey on Algorithms for Multi-label Learning. 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