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In Annals of Saudi medicine ; h5-index 0.0

BACKGROUND : No-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.

OBJECTIVE : Use artificial intelligence to build a model that predicts no-shows for individual appointments.

DESIGN : Predictive modeling.

SETTING : Major tertiary care center.

PATIENTS AND METHODS : All 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 MEASURES : No show appointments.

SAMPLE SIZE : 1 087 979 outpatient clinic appointments.

RESULTS : The 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.

CONCLUSION : Appointments 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.

LIMITATIONS : Single center. Only one year of data.


AlMuhaideb Sarab, Alswailem Osama, Alsubaie Nayef, Ferwana Ibtihal, Alnajem Afnan