Salazar, L.H.A.; Parreira, W.D.; Fernandes, A.M.R.; Leithardt, V.R.Q. No-Show in Medical Appointments with Machine Learning Techniques: A Systematic Literature Review. Information2022, 13, 507.
Salazar, L.H.A.; Parreira, W.D.; Fernandes, A.M.R.; Leithardt, V.R.Q. No-Show in Medical Appointments with Machine Learning Techniques: A Systematic Literature Review. Information 2022, 13, 507.
Salazar, L.H.A.; Parreira, W.D.; Fernandes, A.M.R.; Leithardt, V.R.Q. No-Show in Medical Appointments with Machine Learning Techniques: A Systematic Literature Review. Information2022, 13, 507.
Salazar, L.H.A.; Parreira, W.D.; Fernandes, A.M.R.; Leithardt, V.R.Q. No-Show in Medical Appointments with Machine Learning Techniques: A Systematic Literature Review. Information 2022, 13, 507.
Abstract
No-show appointments in healthcare is a problem faced by medical centers around the world, and understand the factors associated with the no-show behavior is essential. In the last decades, artificial intelligence took place in the medical field and machine learning algorithms can work as a 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 a SLR following the Kitchenham methodology, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each studies 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 patients age, whether the patient missed a previous appointment, and the distance between the appointment and the patients scheduling.
Keywords
No-show; Medical Appointments; Healthcare; Artificial Intelligence; Data processing and management
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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