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Evaluating the Impact of Patient No-Shows on Service Quality

Authors Marbouh D, Khaleel I, Al Shanqiti K, Al Tamimi M, Simsekler MCE, Ellahham S, Alibazoglu D, Alibazoglu H

Received 23 September 2019

Accepted for publication 23 March 2020

Published 4 June 2020 Volume 2020:13 Pages 509—517

DOI https://doi.org/10.2147/RMHP.S232114

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Dr Kent Rondeau


Dounia Marbouh,1 Iman Khaleel,1 Khawla Al Shanqiti,1 Maryam Al Tamimi,1 Mecit Can Emre Simsekler,1,2 Samer Ellahham,3 Deniz Alibazoglu,3 Haluk Alibazoglu4

1Research Center of Digital Supply Chain and Operations, Department of Industrial and Systems Engineering, Khalifa University, Abu Dhabi, United Arab Emirates; 2School of Management, University College London, London, UK; 3Heart and Vascular Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates; 4Imaging Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates

Correspondence: Mecit Can Emre Simsekler
Khalifa University of Science and Technology, Department of Industrial and Systems Engineering, P.O. Box 127788, Abu Dhabi, United Arab Emirates
Tel +9712 501 8410
Fax +971 2 447 2442
Email emre.simsekler@ku.ac.ae

Purpose: Patient no-shows are long-standing issues affecting resource utilization and posing risks to the quality of healthcare services. They also lead to loss of anticipated revenue, particularly in services where resources are expensive and in great demand.
Methods: In order to address common reasons why patients miss appointments, this study reviews the current literature and investigates various tools and methods that have been implemented to mitigate such issues. Further, a case study is conducted to identify the rate of no-shows and underlying causes at a radiology department in one of the leading hospitals in the MENA region.
Results: Our results show that the no-shows are high due to multiple factors, such as patient behavior, patients’ financial situation, environmental factors and scheduling policy.
Conclusion: In conclusion, we generate a list of recommendations that can help in reducing the rate of patient no-shows, such as patient education, application of dynamic scheduling policies and effective appointment reminder systems to patients.

Keywords: quality, no-shows, overbooking, resource utilization, scheduling policy, patient appointment, predictive analytics

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