Predicting and understanding no-show behaviour in specialist outpatient clinics
Cheng, Jacintha Kei Kee
Date of Issue2015
School of Mechanical and Aerospace Engineering
Tan Tock Seng Hospital
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.
Final Year Project (FYP)
Nanyang Technological University