Predicting non-attendance in hospital outpatient appointments using deep learning approach

Dashtban, M.; Li, Weizi

doi:10.1080/20476965.2021.1924085

Cited by 12 publications

(11 citation statements)

References 70 publications

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“…Naturally, what we are looking for is the set of parameters that make the output value the closest to the expected value [ 16 ]. In principle, any function that can calculate the difference between the actual output value and the expected output value can be used as a loss function [ 17 ]. For multiclassification problems (n training data are divided into m classes), the cross-entropy loss function is generally used:

For recursive problems, a mean square (least squares) loss function is usually used:

…”

Section: Deep Learning Algorithm For the Layout Of Urban Social Facil...mentioning

confidence: 99%

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Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning

Huang

2022

Computational Intelligence and Neuroscience

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The spatial layout and optimization of social facilities for sports are related to many factors such as urban economy, transportation, population, and urban planning. With the rapid development of artificial intelligence today, deep learning came into being. It also provides a lot of methods for our in-depth research on it. To solve the problem of unbalanced layout of urban social facilities for sports and the difficulty in meeting the needs of residents under the current background, this paper adopts the methods of M-P model, loss function, and activation function. Taking Hangzhou, a sports city that is about to host the Asian Games, as an example, it carried out an experiment to optimize the layout of urban public sports services. Through various tests and data, it divides the accessibility level of social facilities for sports in Hangzhou into four levels: high accessibility point (1.0-1.5), high accessibility point (0.5-1.0), the accessibility is average (0.2-0.5), and the accessibility is poor (<0.2). It also concluded that the areas with high accessibility (1.0-1.5) are Shangcheng District and Binjiang District, and the layout of sports facilities is optimal. According to the minimum facility point model, Xiaoshan District and Yuhang District need to add 6 large-scale social facilities for sports. Through this experiment, this paper can also provide a reference for other urban layouts. This has significance for the optimization of sports facilities and the optimization of public facilities.

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For recursive problems, a mean square (least squares) loss function is usually used:

…”

Section: Deep Learning Algorithm For the Layout Of Urban Social Facil...mentioning

confidence: 99%

Section: Loss Functionmentioning

confidence: 99%

Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning

Huang

2022

Computational Intelligence and Neuroscience

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“…The predictive performance of the model was below the attendance rate with an AUC of 0.78 and 0.81 for the primary care and endocrinology clinics, respectively [30]. Another model, which also used deep neural networks, was proposed by Dashtban and Li [24,32]; this model was based on sparse stacked denoising autoencoders (SDAEs). The model was trained using in-hospital data collected from an acute care NHS hospital in the UK.…”

Section: Discussionmentioning

confidence: 99%

An artificial intelligence-based model to reduce the no-show rate in outpatient clinics of an academic hospital

Aij,

Knoester,

Werkhoven

2024

Preprint

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Purpose non-attendance of patients for outpatient appointments, known as "no-shows," poses a persistent challenge for healthcare facilities, with significant repercussions for both patients and healthcare systems. This study aimed to investigate whether targeting high-risk individuals with interventions could effectively reduce the rate of no-shows within reasonable resource allocation. Methods we developed an artificial intelligence (AI) algorithm-based prediction model to estimate the likelihood of an appointment resulting in a no-show. Utilizing retrospective data from 24 outpatient clinics, a machine learning (ML) model was constructed and trained to identify patients at high risk of no-show. Subsequently, over a 6-month period, 35% of the highest-risk patients were randomly assigned to either the intervention group (receiving a reminder phone call three workdays before their appointment) or the control group (no reminder call). Results following the intervention, the intervention group experienced a notable 26.2% reduction in no-shows. This reduction translates to a 14.3% decrease in the overall number of no-shows, demonstrating the efficacy of the reminder service. Moreover, this intervention led to additional benefits, including the ability to schedule new patients on previously avoided no-show slots, enhanced patient experience, reduced staff preparation time for missed appointments, and a decrease in administrative burden associated with rescheduling no-shows. Conclusions Our AI-powered model proved to be an effective tool for identifying high-risk patients prone to missing their outpatient appointments. This allowed for targeted interventions, such as reminder phone calls, to be implemented. The substantial reduction in no-show rates underscores the potential impact of this approach on optimizing healthcare resource allocation and improving patient attendance.

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“…Successfully predicting the risk of a particular patient not attending their appointment will enable targeted strategies to investigate barriers to access and improve attendance and health outcomes for high risk patients. Non‐attendance models can be used to customise clinic scheduling, improve attendance and maximise the use of limited healthcare resources 15,16 …”

Section: Introductionmentioning

confidence: 99%

“…Non-attendance models can be used to customise clinic scheduling, improve attendance and maximise the use of limited healthcare resources. 15,16 The aim of this study was to develop machine learning models to accurately predict non-attendance in all government-funded ophthalmology clinics in New Zealand. This study uses nationwide multicentre data to develop predictive models and evaluate predictive performance for individual regions.…”

Section: Introductionmentioning

confidence: 99%

Predicting ophthalmic clinic non‐attendance using machine learning: Development and validation of models using nationwide data

Breeze,

Hossain,

Mayo

et al. 2023

Clinical Exper Ophthalmology

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BackgroundOphthalmic clinic non‐attendance in New Zealand is associated with poorer health outcomes, marked inequities and costs NZD$30 million per annum. Initiatives to improve attendance typically involve expensive and ineffective brute‐force strategies. The aim was to develop machine learning models to accurately predict ophthalmic clinic non‐attendance.MethodsThis multicentre, retrospective observational study developed and validated predictive models of clinic non‐attendance. Attendance data for 3.1 million appointments from all New Zealand government‐funded ophthalmology clinics from 2009 to 2018 were aggregated for analysis. Repeated ten‐fold cross validation was used to train and optimise XGBoost and logistic regression models on several demographic and clinic‐related variables. Models developed using the entire training set were compared with those restricted to regional subsets of the data.ResultsIn the testing data set from 2019, there were 407 574 appointments (median [range] age, 66 [0–105] years; 210 365 [51.6%] female) with a non‐attendance rate of 5.7% (n = 23 309 missed appointments), XGBoost models trained on each region's data achieved the highest mean AUROC of 0.764 (SD 0.058) and mean AUPRC of 0.157 (SD 0.072). XGBoost performed better than logistic regression (mean AUROC = 0.756, p = 0.002). Training individual XGBoost models for each region led to better performance than training a single model on the complete nationwide dataset (mean AUROC = 0.754, p = 0.04).ConclusionMachine learning algorithms can predict ophthalmic clinic non‐attendance with relatively basic demographic and clinic data. These findings suggest further research examining implementation of such algorithms in scheduling systems or public health interventions may be useful.

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Predicting non-attendance in hospital outpatient appointments using deep learning approach

Cited by 12 publications

References 70 publications

Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning

Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning

An artificial intelligence-based model to reduce the no-show rate in outpatient clinics of an academic hospital

Predicting ophthalmic clinic non‐attendance using machine learning: Development and validation of models using nationwide data

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