Abstract: Apparatuses, systems, and methods are provided for reducing return of prescriptions to stock. The apparatuses, systems and methods provide for one or more processors to: (1) store pharmacy prescription information in a standardized pharmacy format; (2) receive updated information in a non-standardized electronic prescription format for a prescription of a patient; (3) receive patient health record data; (4) generate prescription return to stock prediction data that indicates a probability the prescription of the patient would be returned to stock based upon the electronic prescription data, the patient health record data, and a predictive model; (5) convert the electronic prescription data and the prescription return to stock prediction data to pharmacy prescription information; and (6) generate a pharmacy prescription in a standardized format based on the updated pharmacy prescription information.

Abstract: Example methods, apparatus, and articles of manufacture to estimate waiting times of prescriptions are disclosed herein. An example computer-implemented method, executed by a processor, to estimate a waiting time of a prescription for a medication includes training a machine learning model using, for each of a plurality of previously filled prescriptions, a set of characteristics of the previously filled prescription, and a fill time for the previously filled prescription, receiving a prescription for a medication for a patient, receiving a request for an estimated waiting time for filling the prescription medication for the patient, identifying a set of characteristics of the prescription medication for the patient, applying the set of characteristics of the prescription medication to the machine learning model to determine the estimated waiting time for filling the prescription medication for the patient, and providing an indication of the estimated waiting time for display on a client device.

Abstract: Example methods, apparatus, and articles of manufacture to estimate costs of prescriptions are disclosed herein. An example computer-implemented method, executed by a processor, to estimate a cost of a prescription for a medication includes receiving, with a processor, a prescription for a medication for a patient, receiving, with the processor from an entity, a request for an estimated cost for the prescription, processing, with a machine learning model, the prescription to determine the estimated cost of the medication for the patient, and providing, with the processor, the estimated cost to the entity.

Abstract: Apparatuses, systems and methods are provided to reduce return of prescriptions to stock. The apparatuses, systems, and methods may reduce return of prescriptions to stock based on a predictive model. The apparatuses, systems, and methods may reduce return of prescriptions to stock based on a response of a patient to a notification that a prescription has been received for the patient.

Abstract: Example systems, methods and apparatus to process medical prescription order rejections are disclosed. An example computer-implemented method, executed by one or more processors, to process a medical prescription order rejection, the method comprising submitting, using one or more processors, a first order for a medical prescription to a third-party entity for payment, when the first order is rejected by the third-party entity, processing, with a machine learning model, the first order to form a second order for the medical prescription, submitting, using one or more processors, the second order for the medical prescription to a third-party entity for payment, and when the second order is approved by the third-party entity, notifying, using one or more processors, a prescription fulfillment entity that the medical prescription is available to be fulfilled.