Can we predict a national profile of non-attendance paediatric
urology patients: a multi-institutional electronic health record
study

Bush, Ruth A.; Vemulakonda, Vijaya M.; Corbett, Sean T.; Chiang, George

doi:10.14236/jhi.v21i3.59

Cited by 19 publications

(37 citation statements)

References 16 publications

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“…Maybe this provides insight into what are the prevailing themes that might make up a taxonomy for health informatics. Morrison et al 10 Morrison et al 10 Levy et al 9 Levy et al 9 Pandhi et al 12 Prazeres 13 Prazeres 13 Levy et al 9 Bush et al 11 Morrison et al 10 Morrison et al 10 Pandhi et al 12 Pietrzak et al 8 Bush et al 11 Pandhi et al 12 Pietrzak et al …”

Section: Where In the Taxonomy Does The Editor Place These Papersmentioning

confidence: 99%

In this issue - Making sense of taxonomies in health informatics

Lusignan¹

2014

Journal of Innovation in Health Informatics

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This issue of the journal starts with a leading article about the tangled taxonomies within our discipline. 1 In this issue explores this further, looking at how a clear taxonomy for our discipline might improve our understanding of what is and, perhaps more importantly, is not part of health informatics.Taxonomies, the classification or grouping of things, are well developed for living things. Many of the groupings of plants and animals take a phylogenetic perspective, namely they make the assumption that there was evolution from a common ancestry. Darwin was one of the first known to have sketched out a 'Tree of life' to illustrate this common ancestry of many life forms. 2 We should, as an informatics community, better define the components of what makes up our discipline, as this would help us define and explain what we do. Creating such a taxonomy should not necessarily constrain us. Darwin recognised that some branches of the tree of life might die and fall away:From the first growth of the tree, many a limb and branch has decayed and dropped off; and these fallen branches of various sizes may represent those whole orders, families, and genera, which now have no living representatives, and which are known to us only in a fossil state.

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Section: Where In the Taxonomy Does The Editor Place These Papersmentioning

confidence: 99%

In this issue - Making sense of taxonomies in health informatics

Lusignan¹

2014

Journal of Innovation in Health Informatics

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“…Missed appointments that have not been canceled by the patient are commonly referred to as no-show appointments (Bush et al, 2014;Dantas et al, 2018;Kalb et al, 2012;Miller et al, 2015;Weingarten et al, 1997;Zailinawati et al, 2006). However, researchers often consider late cancellations as missed appointments since such late cancellations do not allow the scheduling system to set an appointment for a new patient (Topuz et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Ahmadi

Garcia-Arce

Masel

et al. 2019

IISE Transactions on Healthcare Systems Engineering

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Patient no-shows and late cancellations for an appointment are common problems in healthcare, which adversely affect the financial performance and quality of service of healthcare organizations. A high rate of patient no-show and late cancellation in a clinic can significantly limit access to healthcare. In general, hospitals create predictive models to assess risk of no-show, and then assign overbooking appointments utilizing those risks. In this paper, by incorporating machine learning and optimization techniques, we proposed a predictive model to assist with the overbooking decision. The model consists of two phases. First, we utilized a metaheuristic optimization technique to explore the best subset of featuresknown as feature selection problemthat can significantly contribute to the prediction outcomes. Second, using the output of the first stage, we proposed a stacking model to improve the prediction performances further. Our extensive computations and comparisons across different classifiers show that formulating the feature selection problem as a multi-objective problem instead of a single-objective problem using random forest classifier yields better results. The proposed model will improve the overbooking at clinics, by increasing the patient access to care. We introduced important new features to the literature that can describe the no-show and late cancellation behavior.

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“…Pacientes sem seguro de saúde (consulta de auto-pagamento) são mais propensos a não comparecerem do que pacientes com seguro saúde Weingarten et al, 1997;Karter et al, 2004;Mugavero et al, 2007 Já em outras pesquisas, possuir ou não um plano de saúde não é significativo Kavanagh et al, 1991;Grunebaum et al, 1996;Hampton-Robb et al, 2003;Cashman et al, 2004;Siminoff et al, 2008;Sherman et al, 2009;Samuels et al, 2015;Miller et al, 2015;Storrs et al, 2016 Margolis et al, 1993;Grunebaum et al, 1996;Livianos-Aldana et al, 1999;Hamilton et al, 2002;Adams et al, 2004;Cohen et al, 2005;Lee et al, 2005;Cohen et al, 2007a;Cohen et al, 2007b;Mugavero et al, 2007;Dreiher et al, 2008b;Siminoff et al, 2008;Sola-Vera et al, 2008;Goldbart et al, 2009;Sherman et al, 2009;Pillai et al, 2012;Cronin et al, 2013;Bush et al, 2014;Guay et al, 2014;Milne et al, 2014;Norris et al, 2014;Machado et al, 2015;Partin et al, 2016).…”

Section: Plano De Saúde/forma De Pagamentounclassified

“…Para Matas et al (1992), pacientes que eram indicados pela emergência foram responsáveis por maior taxa de no-show comparados a indicação de um clínico geral. Ross et al (1995), Grunebaum et al (1996), Weinerman et al (2003) Bush et al, 2014;Weingarten et al, 1997;. (2011)…”

Section: Fonte De Referênciaunclassified

“…As clínicas de fisioterapia sofrem as maiores taxas de não comparecimento, com média de 50,8% de no-show, seguida das clínicas de cardiologia, 30,4%, e neurologia, 29,1%. As taxas médias de no-show para cada especialidade podem ser observadas naFigura 4. et al, 2005;Bush et al, 2014;Miller et al, 2015). Esse cancelamento, segundoWeingarten et al (1997), pode ser feito mediante uma ligação ou quando um lembrete de consulta é recebido pelo paciente, e para, ele deve ser realizado 24 horas antes do horário agendado para não ser considerado um no-show.…”

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Revisão Sistemática Da Literatura Sobre No-Show Em Agendamento De Consultas

DANTAS¹

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A Deus por me permitir chegar até aqui com muita fé e perseverança, por me dar forças e iluminar meus caminhos.Aos meus pais, Francisco e Vilma, agradeço por todo apoio, educação, carinho, amor, torcida e confiança. Sem eles nada disso teria sido possível.Aos meus irmãos, André e Sílvia, pelo companheirismo e apoio em todos os momentos. À Igor, pela ajuda, incentivo e por estar sempre ao meu lado nessa jornada. Obrigada por tudo.Aos orientadores Fernando Cyrino e Silvio Hamacher, por todo suporte, atenção, correções, incentivos e oportunidades durante o desenvolvimento da dissertação. Obrigada por todos os ensinamentos.Aos professores do DEI/PUC-Rio por todo conhecimento compartilhado e pelas oportunidades de aprendizado.Aos meus colegas e amigos da PUC-Rio, obrigada por dividirem as dúvidas, experiências e por tornar toda essa trajetória um pouco mais fácil.À CAPES e à PUC-Rio, pelos auxílios concedidos e pelo excelente ambiente, materiais e professores disponíveis, sem os quais este trabalho não teria sido possível.A todos aqueles que, de alguma forma, contribuíram para este trabalho, meu muito obrigada. PUC-Rio -Certificação Digital Nº 1512274/CA Palavras-chavesNo-shows; agendamentos; sistemas de saúde; análise estatística; revisão sistemática. PUC-Rio -Certificação Digital Nº 1512274/CA Abstract Dantas, Leila Figueiredo; Oliveira, Fernando Luiz Cyrino (Advisor); Hamacher, Sílvio (Co-Advisor). Systematic literature review on no-show in appointment scheduling. Rio de Janeiro, 2016. 89p.The failure to attend to scheduled appointments ("no-show") has a significant impact on healthcare systems. Despite the growing of scientific research on the factors that influence no-show, there is not a current summary of the state of art in this area, and systematic review covering all medical fields. In order to fill this gap, this dissertation has the intention to conduct a systematic literature review with three goals: (i) to summarize the characteristics of the studies according to methodology, country, clinical specialty, dependents variables, and no-show rates;(ii) to gather and integrate the results of the factors that significantly affect the nonattendance rates; (iii) to compare this analysis with previous researches. The literature indicates that the no-show does not happens randomly, and to understand the characteristics related to missed appointments is important to improve attendance, allowing the use of this information in scheduling practices. Using the Scopus database, 724 articles were reviewed and 105 selected for analysis. The average no-show rate was 23%, and it is most common in patients: younger, low socioeconomic level, greater distance from home to the clinic, with public health insurance or no insurance, and attended by less experienced provider. The most significant factors are high lead time and high rates of previously missed appointment.

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Can we predict a national profile of non-attendance paediatric urology patients: a multi-institutional electronic health record study

Abstract: Background Non-attendance at paediatric urology outpatient appointments results in the patient's failure to receive medical care and wastes health care resources.

Cited by 19 publications

References 16 publications

In this issue - Making sense of taxonomies in health informatics

In this issue - Making sense of taxonomies in health informatics

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Revisão Sistemática Da Literatura Sobre No-Show Em Agendamento De Consultas

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