• Sarab AlMuhaideb, Osama Alswailem, Nayef Alsubaie, Ibtihal Ferwana, and Afnan Alnajem.
• From the Department of Computer Science, Prince Sultan University, Riyadh, Saudi Arabia.
• Ann Saudi Med. 2019 Nov 1; 39 (6): 373-381.

BackgroundNo-shows, a major issue for healthcare centers, can be quite costly and disruptive. Capacity is wasted and expensive resources are underutilized. Numerous studies have shown that reducing uncancelled missed appointments can have a tremendous impact, improving efficiency, reducing costs and improving patient outcomes. Strategies involving machine learning and artificial intelligence could provide a solution.ObjectiveUse artificial intelligence to build a model that predicts no-shows for individual appointments.DesignPredictive modeling.SettingMajor tertiary care center.Patients And MethodsAll historic outpatient clinic scheduling data in the electronic medical record for a one-year period between 01 January 2014 and 31 December 2014 were used to independently build predictive models with JRip and Hoeffding tree algorithms.Main Outcome MeasuresNo show appointments.Sample Size1 087 979 outpatient clinic appointments.ResultsThe no show rate was 11.3% (123 299). The most important information-gain ranking for predicting no-shows in descending order were history of no shows (0.3596), appointment location (0.0323), and specialty (0.025). The following had very low information-gain ranking: age, day of the week, slot description, time of appointment, gender and nationality. Both JRip and Hoeffding algorithms yielded a reasonable degrees of accuracy 76.44% and 77.13%, respectively, with area under the curve indices at acceptable discrimination power for JRip at 0.776 and at 0.861 with excellent discrimination for Hoeffding trees.ConclusionAppointments having high risk of no-shows can be predicted in real-time to set appropriate proactive interventions that reduce the negative impact of no-shows.LimitationsSingle center. Only one year of data.Conflict Of InterestNone.

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