Impact of Delayed Time to Advanced Imaging on Missed Appointments Across Different Demographic and Socioeconomic Factors

Daye, Dania; Carrodeguas, Emmanuel; Glover, McKinley; Guerrier, Claude E; Harvey, H. Benjamin; Flores, Efrén J.

doi:10.1016/j.jacr.2018.01.023

Cited by 32 publications

(46 citation statements)

References 28 publications

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“…Secondly, in G&D (between 0 and 13 years) and YAP (between 14 and 44 years), older patients are more likely to miss their appointments. This result is consistent with previously reported findings in primary care and paediatrics settings[20,73,76]. Finally, age seems to have less impact among SP patients.…”

supporting

confidence: 93%

“…On the one hand, this result is highly context-dependent. Whereas some studies have reached the same conclusion [71,72], others have reported that males have lower no-show rates [19,73,74], or concluded that gender does not have impact in no-show probabilities [75,76]. On the other hand, in developing countries it has been argued that, among socio-economically disadvantaged females, high no-show rates might be related to a lack of support from social networks and their responsibilities as caregivers [77][78][79].…”

Section: Lasso Regression Model: Variables Affecting No-show Probabilmentioning

confidence: 99%

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Improving healthcare access management by predicting patient no-show behaviour

Barrera

Brailsford

Bravo

et al. 2020

Decision Support Systems

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Low attendance levels in medical appointments have been associated with poor health outcomes and efficiency problems for service providers. To address this problem, healthcare managers could aim at improving attendance levels or minimizing the operational impact of no-shows by adapting resource allocation policies. However, given the uncertainty of patient behaviour, generating relevant information regarding no-show probabilities could support the decision-making process for both approaches. In this context many researchers have used multiple regression models to identify patient and appointment characteristics than can be used as good predictors for no-show probabilities. This work develops a Decision Support System (DSS) to support the implementation of strategies to encourage attendance, for a preventive care program targeted at underserved communities in Bogotá, Colombia. Our contribution to literature is threefold. Firstly, we assess the effectiveness of different machine learning approaches to improve the accuracy of regression models. In particular, Random Forest and Neural Networks are used to model the problem accounting for non-linearity and variable interactions. Secondly, we propose a novel use of Layer-wise Relevance Propagation in order to improve the explainability of neural network predictions and obtain insights from the modelling step. Thirdly, we identify variables explaining no-show probabilities in a developing context and study its policy implications and potential for improving healthcare access. In addition to quantifying relationships reported in previous studies, we find that income and neighbourhood crime statistics affect no-show probabilities. Our results will support patient prioritization in a pilot behavioural intervention and will inform appointment planning decisions.

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supporting

confidence: 93%

Section: Lasso Regression Model: Variables Affecting No-show Probabilmentioning

confidence: 99%

Improving healthcare access management by predicting patient no-show behaviour

Barrera

Brailsford

Bravo

et al. 2020

Decision Support Systems

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“…This work contributes to the existing literature on no-show modeling in Radiology [3,9,15,21,22,24,[30][31][32][33] in several ways. First, by corroborating ndings on no-show predictors in previous works and compiling such results (see Additional le 4: Table S3).…”

Section: Discussionmentioning

confidence: 93%

“…No-show predictors appearing as signi cant in this study were also observed in previous works. Other studies also identi ed non-white patients as more likely to not show up [3,9]. In the penalized logistic regression model, the predictor with largest regression coe cient (and therefore largest contribution to no-show) was the month of June.…”

Section: Discussionmentioning

confidence: 99%

“…There is consensus in the literature regarding the non-random nature of no-shows, and research has been published proposing statistical analyses of factors that may impact no-show rates [6,7]. Previous studies have shown that factors associated with no-show include patients' sociodemographic conditions such as age [4]; gender [7,8]; distance between home and healthcare unit [9,10]; race [11]; marital status [12,13]; level of schooling [14]; appointment characteristics -time [2], day of the week [9], and month [15]; elapsed time between appointment request and actual consultation [2]; and patient's clinical diagnosis [16].…”

Section: Introductionmentioning

confidence: 99%

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Modeling the No-Show of Patients to Exam Appointments of Computed Tomography

Silva

Fogliatto

Garcia

et al. 2020

Preprint

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Background: No-shows of patients have negative impacts on healthcare systems, such as resources’ underutilization, efficiency loss, and cost increase. Predicting no-show is key to develop strategies that counteract its effects. In this paper, we propose a model to predict the no-show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital.Methods: We carried out a retrospective study on 8,382 appointments made to computed tomography (CT) exams between January and December 2017. Penalized logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients’ no-show. The predictive capabilities of the models were evaluated analyzing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC).Results: The no-show rate in computerized tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalized logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analyzed appearing as significant. One of the variables included in the model (number of exams scheduled in previous year) had not been previously reported in the related literature.Conclusions: Our findings may be used to guide the development of strategies to reduce the no-show of patients to exam appointments.

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Modelling the no‐show of patients to exam appointments of computed tomography

Silva

Fogliatto

Garcia

et al. 2022

Health Planning & Management

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Background Patients' no‐shows negatively impact healthcare systems, leading to resources' underutilisation, efficiency loss, and cost increase. Predicting no‐shows is key to developing strategies that counteract their effects. In this paper, we propose a model to predict the no‐show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital. Methods We carried out a retrospective study on 8382 appointments to computed tomography (CT) exams between January and December 2017. Penalised logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients' no‐shows. The predictive capabilities of the models were evaluated by analysing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). Results The no‐show rate in computerised tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalised logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analysed appearing as significant. One of the variables included in the model (number of exams scheduled in the previous year) had not been previously reported in the related literature. Conclusions Our findings may be used to guide the development of strategies to reduce the no‐show of patients to exam appointments.

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Impact of Delayed Time to Advanced Imaging on Missed Appointments Across Different Demographic and Socioeconomic Factors

Cited by 32 publications

References 28 publications

Improving healthcare access management by predicting patient no-show behaviour

Improving healthcare access management by predicting patient no-show behaviour

Modeling the No-Show of Patients to Exam Appointments of Computed Tomography

Modelling the no‐show of patients to exam appointments of computed tomography

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