Predicting consultation durations in a digital primary care setting
Abstract: The aim of this thesis is to develop a method to predict consultation durations in a digital primary care setting and thereby create a tool for designing a more efficient scheduling system in primary care. The ultimate purpose of the work is to contribute to a reduction in waiting times in primary care. Even though no actual scheduling system was implemented, four machine learning models were implemented and compared to see if any of them had better performance. The input data used in this study was a combination of patient and doctor features. The patient features consisted of information extracted from digital symptom forms filled out by a patient before a video consultation with a doctor. These features were combined with doctor's speed, defined as the doctor's average consultation duration for his/her previous meetings. The output was defined as the length of the video consultation including administrative work made by the doctor before and after the meeting. One of the objectives of this thesis was to investigate whether the relationship between input and output was linear or non-linear. Also the problem was formulated both as a regression and a classification problem. The two problem formulations were compared in terms of achieved accuracy. The models chosen for this study was linear regression, linear discriminant analysis and the multi-layer perceptron implemented for both regression and classification. After performing a statistical t-test and a two-way ANOVA test it was concluded that no significant difference could be detected when comparing the models' performances. However, since linear regression is the least computationally heavy it was suggested for future usage until it is proved that any other model achieves better performance. Limitations such as too few models being tested and flaws in the data set were identified and further research is encouraged. Studies implementing an actual scheduling system using the methodology presented in the thesis is recommended as a topic for future research.
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