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Sci‐Fri AM: Quality, Safety, and Professional Issues 04: Predicting waiting times in Radiation Oncology using machine learning
Author(s) -
Joseph Ackeem,
Herrera David,
Hijal Tarek,
Hendren Laurie,
Leung Alvin,
Wainberg Justin,
Sawaf Marya,
Maxim Gorshkov,
Maglieri Robert,
Keshavarz Mehryar,
Kildea John
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4961840
Subject(s) - medicine , radiation oncology , radiation therapy , worry , random forest , patient safety , health care , artificial intelligence , medical physics , machine learning , computer science , anxiety , surgery , psychiatry , economics , economic growth
We describe a method for predicting waiting times in radiation oncology. Machine learning is a powerful predictive modelling tool that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The patient waiting experience remains one of the most vexing challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick and in pain, to worry about when they will receive the care they need. In radiation oncology, patients typically experience three types of waiting: 1. Waiting at home for their treatment plan to be prepared 2. Waiting in the waiting room for daily radiotherapy 3. Waiting in the waiting room to see a physician in consultation or follow‐upThese waiting periods are difficult for staff to predict and only rough estimates are typically provided, based on personal experience. In the present era of electronic health records, waiting times need not be so uncertain. At our centre, we have incorporated the electronic treatment records of all previously‐treated patients into our machine learning model. We found that the Random Forest Regression model provides the best predictions for daily radiotherapy treatment waiting times (type 2). Using this model, we achieved a median residual (actual minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. The main features that generated the best fit model (from most to least significant) are: Allocated time, median past duration, fraction number and the number of treatment fields.