Data Analytics and Predictive Modeling for Appointments No-show at a Tertiary Care Hospital

Moharram, Amani; Altamimi, Saud; Alshammari, Riyad

doi:10.1109/caida51941.2021.9425258

Cited by 4 publications

(3 citation statements)

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“…Other studies [2][3][4] propose the use of overbooking techniques as a way of mitigating the patients' non-attendance. The optimization of appointment planning through the integration of the predictive model developed in the scheduling system [3,5,16,21], the collection of basic information for new patients through an online form [24], and the remodeling of workflow and scheduling policies [21] are alternatives mentioned in the studies as a way of reducing absenteeism.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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No-show appointments in healthcare is a problem faced by medical centers around the world, and understanding the factors associated with no-show behavior is essential. In recent decades, artificial intelligence has taken place in the medical field and machine learning algorithms can now work as an efficient tool to understand the patients’ behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on an SLR following the PRISMA procedure, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each study were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patient’s age, whether the patient missed a previous appointment, and the distance between the appointment and the patient’s scheduling.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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“…The proposed model achieved results with accuracy up to 96.3%. Also, in 2021 another research was presented to produce a prediction model for healthcare centers based on machine learning algorithms and analysis methods (Moharram, Altamimi and Alshammari, 2021). In this paper, they analyze the input data to reduce the number of training data.…”

Section: Data Analytics and Its Methodsmentioning

confidence: 99%

Data Analytics and Techniques

Abdul-Jabbar

Farhan²

2022

ARO

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Big data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide improvements for many applications. In addition, critical challenges and research issues were provided based on published paper limitations to help researchers distinguish between various analytics techniques to develop highly consistent, logical, and information-rich analyses based on valuable features. Furthermore, the findings of this paper may be used to identify the best methods in each sector used in these publications, assist future researchers in their studies for more systematic and comprehensive analysis and identify areas for developing a unique or hybrid technique for data analysis.

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“…Authors in [36] develop a machine learning model for predicting no-shows in paediatric outpatient clinics. Three machine-learning algorithms (logistic regression, JRip, and Hoeffding tree) were compared and all models have precision and recall of around 90%.…”

Section: Related Workmentioning

confidence: 99%

Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector

et al. 2021

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The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient’s profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient’s no-show and develop a prediction model able to identify whether the patient will attend their scheduled appointment or not. The study was based on data from clinics that serve the Unified Health System (SUS) at the University of Vale do Itajaí in southern Brazil. The model obtained was tested on a real collected dataset with about 5000 samples. The best model result was performed by the Random Forest classifier. It had the best Recall Rate (0.91) and achieved an ROC curve rate of 0.969. This research was approved and authorized by the Ethics Committee of the University of Vale do Itajaí, under opinion 4270,234, contemplating the General Data Protection Law.

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Data Analytics and Predictive Modeling for Appointments No-show at a Tertiary Care Hospital

Cited by 4 publications

References 10 publications

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

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

Data Analytics and Techniques

Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector

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