No-Show in Medical Appointments with Machine Learning Techniques: A Systematic Literature Review

Salazar, Luiz Henrique; Parreira, Wemerson Delcio; Fernandes, Anita Maria da Rocha; Leithardt, Valderi Reis Quietinho

doi:10.3390/info13110507

Cited by 2 publications

(5 citation statements)

References 29 publications

(46 reference statements)

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“…Appointment failure (alternatively named as broken, missed appointments or no-show) represents a significant burden to the overall healthcare system. 1,2 Specifically, it negatively impacts all the domains involved: the healthcare organization, by increasing financial costs related to unused staff time, ineffective use of resources and limiting effective clinic capacity 1,2 ; the provider, by decreasing productivity and efficiency 3,4 ; and the patient, by increasing dissatisfaction related to long waiting time and perception of reduced service quality. [5][6][7] In addition, when no-shows occur in a dental school, dental students' clinical experience and education are also adversely influenced.…”

Section: Introductionmentioning

confidence: 99%

“…If there was a system able to specifically identify selected patients at a higher risk of failing to attend an appointment, in a specific environment such as a dental school, novel and targeted scheduling systems could be incorporated to improve organizational performance. 10 Nowadays, machine learning (ML) systems are frequently applied to large databases to formulate accurate predictive models, 1 with encouraging results in decreasing the no-show rate by 3.4%. 22 Yet, there is a limited number of studies that have investigated the use of ML in dental school clinics (DSC) to prevent patient no shows.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, machine learning (ML) systems are frequently applied to large databases to formulate accurate predictive models, 1 with encouraging results in decreasing the no‐show rate by 3.4% 22 . Yet, there is a limited number of studies that have investigated the use of ML in dental school clinics (DSC) to prevent patient no shows 23,24 …”

Section: Introductionmentioning

confidence: 99%

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Leveraging machine learning to create user‐friendly models to mitigate appointment failure at dental school clinics

Cuevas‐Nunez,

Pan,

Sangalli

et al. 2023

Journal of Dental Education

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Purpose/ObjectivesThis study had a twofold outcome. The first aim was to develop an efficient, machine learning (ML) model using data from a dental school clinic (DSC) electronic health record (EHR). This model identified patients with a high likelihood of failing an appointment and provided a user‐friendly system with a rating score that would alert clinicians and administrators of patients at high risk of no‐show appointments. The second aim was to identify key factors with ML modeling that contributed to patient no‐show appointments.MethodsUsing de‐identified data from a DSC EHR, eight ML algorithms were evaluated: simple decision tree, bagging regressor classifier, random forest classifier, gradient boosted regression, AdaBoost regression, XGBoost regression, neural network, and logistic regression classifier. The performance of each model was assessed using a confusion matrix with different threshold level of probability; precision, recall and predicted accuracy on each threshold; receiver‐operating characteristic curve (ROC) and area under curve (AUC); as well as F1 score.ResultsThe ML models agreed on the threshold of probability score at 0.20–0.25 with Bagging classifier as the model that performed best with a F1 score of 0.41 and AUC of 0.76. Results showed a strong correlation between appointment failure and appointment confirmation, patient's age, number of visits before the appointment, total number of prior failed appointments, appointment lead time, as well as the patient's total number of medical alerts.ConclusionsAltogether, the implementation of this user‐friendly ML model can improve DSC workflow, benefiting dental students learning outcomes and optimizing personalized patient care.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging machine learning to create user‐friendly models to mitigate appointment failure at dental school clinics

Cuevas‐Nunez,

Pan,

Sangalli

et al. 2023

Journal of Dental Education

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“…Manogaran et al (2018) state that several algorithms in these areas have been developed to overcome the difficulties presented in recent years. Moreover, according to Jothi et al (2015) and Salazar et al (2022), these are used in the health area, primarily for disease prevention and to help doctors diagnose.…”

Section: Introductionmentioning

confidence: 99%

“…difficulties presented in recent years. Moreover, according to Jothi et al (2015) and Salazar et al (2022), these are used in the health area, primarily for disease prevention and to help doctors diagnose.…”

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confidence: 99%

Automated and intelligent system for World Health Organization data forecasting

Soares,

de Freitas

2023

Expert Systems

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Technological advances and social transformations have enabled the circulation of a large amount of data in the health area. Analyzing this data becomes more critical and more challenging as the volume of data increases. An alternative to performing this analysis is to use data analysis techniques to process input data sets and build consistent databases for input to machine learning algorithms. Thus, it can forecast future scenarios and collaborate with knowledge discovery. In this context, this work aims to develop a parameterizable system with automated decisions capable of collecting and analyzing many indicators provided by the World Health Organization (WHO). After these analysis steps, the system applies machine learning algorithms for predictions of different indicators to process information automatically, finding different knowledge discovery scenarios. Thus, the contribution of this article is an automated and intelligent system for WHO data forecasting. The efficiency of the system's choices and forecasts was proven with experiments in five different areas of health, obtaining assertiveness by up to 99.92%, root‐mean‐square error (RMSE) by up to 0.0286, and Kling‐Gupta Efficiency (KGE) by up to 0.9988, hitting even the most complex cases, as shown in the confusion matrices. Finally, three case studies were carried out to expand the studies and present the potential of the system in different contexts: anemia in children, age‐standardized suicide rates in men, and number of road traffic deaths.

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No-Show in Medical Appointments with Machine Learning Techniques: A Systematic Literature Review

Cited by 2 publications

References 29 publications

Leveraging machine learning to create user‐friendly models to mitigate appointment failure at dental school clinics

Leveraging machine learning to create user‐friendly models to mitigate appointment failure at dental school clinics

Automated and intelligent system for World Health Organization data forecasting

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