Abstract: A scheduling system and methods for scheduling patient appointments disclosed here utilize any available input to identify a timestamp and a spatial location related to a patient and/or one or more providers in proximity of one or more appointment locations, to allocate a set of procedure resources to each patient appointment based on the timestamps and the spatial locations, and to communicate any revised timing to the patient, thereby minimizing a total waiting time. The scheduling system allows for scheduling of appointments divided into multiple appointment states, each having a predicted timing. After a completed appointment state, individual uncertainty of the predicted timing is replaced with an actual timing and the predicted timing of the remaining schedule is revised. This scheduling system enables adaptive reallocation of resources while avoiding compounding the waiting times that create a multi-state iterative and time-variant problem.

Abstract: A scheduling system and methods for scheduling patient appointments disclosed here utilize any available input to identify a timestamp and a spatial location related to a patient and/or one or more providers in proximity of one or more appointment locations, to allocate a set of procedure resources to each patient appointment based on the timestamps and the spatial locations, and to communicate any revised timing to the patient, thereby minimizing a total waiting time. The scheduling system allows for scheduling of appointments divided into multiple appointment states, each having a predicted timing. After a completed appointment state, individual uncertainty of the predicted timing is replaced with an actual timing and the predicted timing of the remaining schedule is revised. This scheduling system enables adaptive reallocation of resources while avoiding compounding the waiting times that create a multi-state iterative and time-variant problem.

Abstract: A scheduling system and methods for scheduling patient appointments disclosed here utilize any available input to identify a timestamp and a spatial location related to a patient and/or one or more providers in proximity of one or more appointment locations, to allocate a set of procedure resources to each patient appointment based on the timestamps and the spatial locations, and to communicate any revised timing to the patient, thereby minimizing a total waiting time. The scheduling system allows for scheduling of appointments divided into multiple appointment states, each having a predicted timing. After a completed appointment state, individual uncertainty of the predicted timing is replaced with an actual timing and the predicted timing of the remaining schedule is revised. This scheduling system enables adaptive reallocation of resources while avoiding compounding the waiting times that create a multi-state iterative and time-variant problem.

Abstract: A scheduling system and methods for scheduling patient appointments disclosed here utilize any available input to identify a timestamp and a spatial location related to a patient and/or one or more providers in proximity of one or more appointment locations, to allocate a set of procedure resources to each patient appointment based on the timestamps and the spatial locations, and to communicate any revised timing to the patient, thereby minimizing a total waiting time. The scheduling system allows for scheduling of appointments divided into multiple appointment states, each having a predicted timing. After a completed appointment state, individual uncertainty of the predicted timing is replaced with an actual timing and the predicted timing of the remaining schedule is revised. This scheduling system enables adaptive reallocation of resources while avoiding compounding the waiting times that create a multi-state iterative and time-variant problem.