Systems and methods for scheduling delivery of healthcare services

A method, comprising

• generating a cost matrix, wherein each row of the cost matrix comprises a clinician, wherein each column of the cost matrix comprises a patient visit, wherein each patient visit comprises a patient, a patient visit type, a patient location, and a productivity point value corresponding to an amount of work required of a clinician to complete the patient visit, and a scheduling window, wherein the scheduling window comprises days in a scheduling horizon when a patient is available to receive the corresponding patient visit, wherein the scheduling horizon comprises a plurality of future days subject to scheduling, wherein each entry in the cost matrix represents a clinician and patient visit combination, wherein each entry in the cost matrix comprises a marginal cost value of assigning a clinician to a patient visit, wherein the rows of the cost matrix represent all patient events subject to scheduling in the scheduling horizon;
• optimizing a first proposed schedule of all patient visits across days in the scheduling horizon subject to a first set of constraints, wherein each day corresponds to a daily productivity point capacity, wherein the daily productivity point capacity for a day comprises a total amount of work deliverable by at least one clinician on that day, wherein a daily productivity ratio comprises a sum of productivity point values scheduled on a day divided by a corresponding daily productivity capacity, wherein the optimizing the schedule comprises selecting a first optimized schedule of all patient visits that minimizes the largest daily productivity ratio across the scheduling horizon;
• generating daily visit clusters for each day of the first optimized schedule, wherein the generating a daily visit cluster for a day comprises identifying for each individual patient visit scheduled on that day all combinations of patient visits also scheduled on that day sharing a common visit type with the individual patient visit, at a maximum distance value from the individual patient visit, and not greater than a size value;
• adding each visit cluster of the generated daily visit clusters to a master set of visit clusters if the master set does not already include the cluster, wherein the master cluster comprises a single value cluster corresponding to each patient visit subject to scheduling on the scheduling horizon;
• computing the cost of assigning each cluster of the master set of clusters to a clinician by summing the marginal cost value of assigning each patient visit of the cluster to the clinician using the marginal cost matrix;
• generating an optimized assignment by assigning each clinician to one or more visit clusters of the master set of visit clusters according to an assignment that minimizes the total cost of clinician to visit cluster assignments according to a second set of constraints.