Walgreen Company

Lawrence Salud

Adam Robert Snopek

Nicholas J Leners

Nicholas Phillip Baldwin

Zhou Jiang

Joseph Anthony Kuta

August Karlstedt

Example methods, apparatus, and articles of manufacture to estimate costs of prescriptions are disclosed herein. An example system to estimate a cost of a prescription for a medication includes receiving a prescription for a medication for a patient, receiving a request for an estimated cost for the prescription for the patient from an entity, forming an input vector including prescription information for the received prescription and payor information for the patient, processing the input vector for the received prescription with a trained machine learning model to determine the estimated cost of the medication for the patient, updating the machine learning model based upon computed differences between predicted costs determined by the machine learning model and actual sold costs for the plurality of sold prescriptions, providing the estimated cost to a user device, and deploying the updated machine learning model.

Methods and apparatus to estimate costs of prescriptions

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 may include: submitting, using one or more processors, a first order for a medical prescription to a third-party entity for payment; and when a rejection of the first order is received from the third-party entity: processing, with a trained machine learning model, the first order to form a second order for the medical prescription, wherein the machine learning model is trained by one or more processors based on training data representing a plurality of rejected orders for prescriptions and a plurality of approved orders for prescriptions by updating the machine learning model based on computed differences between (i) orders for medical prescriptions determined by the machine learning model, and (ii) associated approved and rejected orders; and submitting, using one or more processors, the second order for the medical prescription to the third-party entity for payment.

Systems, methods and apparatus to process medical prescription order rejections

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.

Apparatuses, systems, and methods for reducing return of prescriptions to stock

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.

Methods and system to estimate retail prescription waiting time

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.

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.

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.

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Chicago, IL, United States of America

Lawrence Salud