Predicting and understanding no-show behaviour in specialist outpatient clinics

With the number of ageing citizens increasing to 900,000 by the year 2030, there will also be an increase in the demands for adequate healthcare services in Singapore. As such, it is imperative for the country to work towards achieving an efficient healthcare system that will provide quality medical...

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Main Author: Cheng, Jacintha Kei Kee
Other Authors: Chen Songlin
Format: Final Year Project
Language:English
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/10356/64914
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-649142023-03-04T19:15:28Z Predicting and understanding no-show behaviour in specialist outpatient clinics Cheng, Jacintha Kei Kee Chen Songlin School of Mechanical and Aerospace Engineering Tan Tock Seng Hospital DRNTU::Engineering::Mechanical engineering With the number of ageing citizens increasing to 900,000 by the year 2030, there will also be an increase in the demands for adequate healthcare services in Singapore. As such, it is imperative for the country to work towards achieving an efficient healthcare system that will provide quality medical services for everyone. In order to meet the growing demands and needs of Singapore’s ageing population while dealing with capacity constraints, it is of paramount importance to reduce the inefficiencies of the healthcare system. One such inefficiency is the no-show behaviour exhibited by patients of outpatient clinics in the hospitals. Research have demonstrated that missed appointments lead to a waste of clinical resources and a reduction of appointment slots available to other patients. Predicting appointment outcomes and the likelihood of no-show behaviour can help mitigate the negative effects brought about by no-show behaviour among patients. Data mining techniques were used to develop a model for the prediction of appointment outcomes and its probabilities using Microsoft Excel’s Visual Basic Application. The model was then tested with data retrieved from one of the outpatient clinics in Tan Tock Seng Hospital, and the trends and rules were discovered and produced for analysis. The accuracy of the model was ascertained by conducting further analysis. A Microsoft Excel spreadsheet was then used to develop a prediction table using the results acquired for the target user. The study concluded with the limitations highlighted and the suggestions made for potential future extensions of the project. Bachelor of Engineering (Mechanical Engineering) 2015-06-09T05:46:45Z 2015-06-09T05:46:45Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/64914 en Nanyang Technological University 85 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Mechanical engineering
spellingShingle DRNTU::Engineering::Mechanical engineering
Cheng, Jacintha Kei Kee
Predicting and understanding no-show behaviour in specialist outpatient clinics
description With the number of ageing citizens increasing to 900,000 by the year 2030, there will also be an increase in the demands for adequate healthcare services in Singapore. As such, it is imperative for the country to work towards achieving an efficient healthcare system that will provide quality medical services for everyone. In order to meet the growing demands and needs of Singapore’s ageing population while dealing with capacity constraints, it is of paramount importance to reduce the inefficiencies of the healthcare system. One such inefficiency is the no-show behaviour exhibited by patients of outpatient clinics in the hospitals. Research have demonstrated that missed appointments lead to a waste of clinical resources and a reduction of appointment slots available to other patients. Predicting appointment outcomes and the likelihood of no-show behaviour can help mitigate the negative effects brought about by no-show behaviour among patients. Data mining techniques were used to develop a model for the prediction of appointment outcomes and its probabilities using Microsoft Excel’s Visual Basic Application. The model was then tested with data retrieved from one of the outpatient clinics in Tan Tock Seng Hospital, and the trends and rules were discovered and produced for analysis. The accuracy of the model was ascertained by conducting further analysis. A Microsoft Excel spreadsheet was then used to develop a prediction table using the results acquired for the target user. The study concluded with the limitations highlighted and the suggestions made for potential future extensions of the project.
author2 Chen Songlin
author_facet Chen Songlin
Cheng, Jacintha Kei Kee
format Final Year Project
author Cheng, Jacintha Kei Kee
author_sort Cheng, Jacintha Kei Kee
title Predicting and understanding no-show behaviour in specialist outpatient clinics
title_short Predicting and understanding no-show behaviour in specialist outpatient clinics
title_full Predicting and understanding no-show behaviour in specialist outpatient clinics
title_fullStr Predicting and understanding no-show behaviour in specialist outpatient clinics
title_full_unstemmed Predicting and understanding no-show behaviour in specialist outpatient clinics
title_sort predicting and understanding no-show behaviour in specialist outpatient clinics
publishDate 2015
url http://hdl.handle.net/10356/64914
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