Motivated by the use of appointment templates in healthcare schedulingpractice, we study how to offer appointment slots to patients in order tomaximize the utilization of provider time. We develop two models, {\emnon-sequential} scheduling and {\em sequential} scheduling, to capturedifferent types of interactions between patients and the scheduling system. Thescheduler offers either a single set of appointment slots for the arrivingpatient to choose from, or multiple sets in sequence, respectively. This isdone without knowledge of patient preference information. For thenon-sequential scheduling model, we identify certain problem instances wherethe greedy policy (i.e., offering all available slots throughout) issuboptimal, but show through analytical and numerical results that for mostmoderate and large instances greedy performs remarkably well. For thesequential model we derive the optimal offering policy for a large class ofinstances, and develop an effective and simple-to-use heuristic inspired byfluid models. We present a case study based on real patient preference data,and demonstrate a potential improvement of up to 17% in provider capacityutilization by adopting our proposed scheduling policies. This improvement maytranslate into \$45k-\$120k increase in annual revenues for a single primarycare provider.
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