Modeling the critical care pathway for cardiothoracic surgery

Bahou, Nicolas; Fenwick, Claire; Anderson, Gillian; Meer, R.B. Van Der; Vassalos, Tony

doi:10.1007/s10729-017-9401-y

Cited by 13 publications

(8 citation statements)

References 20 publications

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“…There were many different ways of obtaining data described, including historic [143], billing [85], messages [107] and Electronic Medical Records [88]. Similarly, collaboration took on a number of different forms including, consulting with experts [17], staff [10], patients [136] and through observations [101]. Figure 4 explores how the information on the pathway was obtained.…”

Section: Obtaining the Pathwaymentioning

confidence: 99%

“…Obtained Data Driven [5, 7-9, 11, 13, 32, 33, 37, 47-49, 54, 58, 62, 66, 70-72, 74-76, 80, 81, 83, 85-99, 104, 107, 108, 112, 120, 121, 124, 126, 127, 131, 133, 134, 138-140, 143, 144, 147-150, 153, 157, 160, 162, 167, 170, 172, 177-179, 183-185, 191, 192, 195, 197-206, 209-211] Collaboration [3,10,14,17,27,42,51,73,100,114,142,152,163] Both [12,20,35,40,56,101,103,129,136,146,154,166,171,186] Other [19,21,22,24,41,60,79,82,116,128,169] Table A6. Care level considered.…”

Section: Fundingmentioning

confidence: 99%

“…Investigating Type Mapping (Ma) [1, 4, 5, 7, 22, 29, 33, 37, 42, 47, 48, 54, 56, 57, 70, 71, 81, 83, 87, 88, 90, 91, 94-99, 104, 112, 121, 126, 127, 131, 136, 138, 139, 144, 149, 154, 157, 161, 162, 164, 170, 172, 177-179, 183-185, 192, 195, 197-203, 206, 209-211] Modelling (Mo) [15, 19-21, 23, 24, 30, 31, 36, 39, 43, 50, 55, 58, 64, 67, 76, 79, 82, 86, 89, 102, 103, 115, 117-119, 122, 129, 145-147, 150, 151, 160, 169, 171, 187, 188, 190, 191, 196, 212] Improving (I) [13,46] Ma & Mo [3, 9, 11, 17, 18, 40, 60, 62, 66, 72-74, 80, 85, 90, 93, 101, 107, 108, 114, 120, 124, 125, 128, 133, 134, 140, 143, 158, 205] Mo & I [8,16,25,32,34,41,45,52,53,68,75,110,111,113,116,141,148,165,182] All Types [10,12,14,27,35,49,51,100,142,152,153,163,166,167,186,204] Table A12. Outcome focus of the pathway.…”

Section: Fundingmentioning

confidence: 99%

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Clinical pathway modelling: a literature review

2019

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Hospital information systems are increasingly used as part of decision support tools for planning at strategic, tactical and operational decision levels. Clinical pathways are an effective and efficient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This literature review proposes a taxonomy of problems related to clinical pathways and explores the intersection between Information Systems (IS), Operational Research (OR) and industrial engineering. A structured search identified 175 papers included in the taxonomy and analysed in this review. The findings suggest that future work should consider industrial engineering integrated with OR techniques, with an aim to improving the handling of multiple scopes within one model, while encouraging interaction between the disjoint care levels and with a more direct focus on patient outcomes. Achieving this would continue to bridge the gap between OR, IS and industrial engineering, for clinical pathways to aid decision support.

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Section: Obtaining the Pathwaymentioning

confidence: 99%

Section: Fundingmentioning

confidence: 99%

Section: Fundingmentioning

confidence: 99%

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Clinical pathway modelling: a literature review

2019

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“…As a result, health centers should optimize their use of the available resources to meet those needs [2]. The planning and programming of surgical interventions has been a diverse field of knowledge analyzed by multiple researches, because surgery rooms are entities that demand complex logistical interaction and, the Operating Rooms (ORs) represent the highest costs account and source of income in most hospitals [2], [6]. Consequently, the design and implementation of better planning and programming systems is an important tool today, not only to reduce costs but also to improve the access to health services [7].…”

Section: Introductionmentioning

confidence: 99%

Operating Room Time Prediction: An Application of Latent Class Analysis and Machine Learning

Gañan-Cardenas

Pemberthy-Ruiz

Rivera-Agudelo

et al. 2021

IyU

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Objective: The objective of this work is to build a prediction model for Operating Room Time (ORT) to be used in an intelligent scheduling system. This prediction is a complex exercise due to its high variability and multiple influential variables. Materials and methods: We assessed a new strategy using Latent Class Analysis (LCA) and clustering methods to identify subgroups of procedures and surgeries that are combined with prediction models to improve ORT estimates. Three tree-based models are assessed, Classification and Regression Trees (CART), Conditional Random Forest (CFOREST) and Gradient Boosting Machine (GBM), under two scenarios: (i) basic dataset of predictors and (ii) complete dataset with binary procedures. To evaluate the model, we use a test dataset and a training dataset to tune parameters. Results and discussion: The best results are obtained with GBM model using the complete dataset and the grouping variables, with an operational accuracy of 57.3% in the test set. Conclusion: The results indicate the GBM model outperforms other models and it improves with the inclusion of the procedures as binary variables and the addition of the grouping variables obtained with LCA and hierarchical clustering that perform the identification of homogeneous groups of procedures and surgeries.

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“…Average length of stay INTRODUÇÃO O aumento da expectativa de vida da população e as inovações tecnológicas têm aumentado os custos na área da saúde no Brasil e no mundo (BAHOU et al, 2017).…”

Section: Alosunclassified

Sistema de suporte à decisão baseado em simulação para evitar situações de tensão em sistemas de emergência hospitalar

Ferreira¹

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RESUMO O alto período de permanência e a superlotação nos departamentos de emergência, são dois problemas recorrentes no setor de saúde, seja no serviço público ou privado. Para diminuir o tempo médio de permanência e combater a superlotação, não há dúvidas quanto a necessidade de dimensionar adequadamente diversos recursos, desde médicos, enfermeiros, recepcionistas, até a disposição física dos ambientes; ao mesmo tempo que devem ser consideradas as restrições orçamentárias e físicas. Para lidar com essas situações, este trabalho adotou uma abordagem multimétodo baseado na teoria de agentes e eventos discretos. A simulação modelou o departamento de emergência no Hospital Infantil Sabará (HIS), localizado na capital do estado de São Paulo, São Paulo. Foram simulados os comportamentos dos pacientes, recepcionistas, enfermeiros de triagem, enfermeiros de emergência e médicos. Para avaliar o desempenho deste sistema, foram testados cenários diferentes, em cada cenário, indicadores-chave de desempenho foram acompanhados. Foi desenvolvido um sistema de suporte à decisão (DSS) baseado em simulação para execução dos cenários elaborados, a fim de melhorar sua gestão pelo sistema hospitalar. As saídas de cada simulação foram a quantidade de pacientes atendidos e o tempo de permanência no Departamento de Emergência (DE). Este estudo também demonstra o impacto potencial devido a um dimensionamento adequado da equipe composta por médicos, enfermeiros e técnicos.

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Modeling the critical care pathway for cardiothoracic surgery

Cited by 13 publications

References 20 publications

Clinical pathway modelling: a literature review

Clinical pathway modelling: a literature review

Operating Room Time Prediction: An Application of Latent Class Analysis and Machine Learning

Sistema de suporte à decisão baseado em simulação para evitar situações de tensão em sistemas de emergência hospitalar

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