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    Systems and methods of automatically correcting errors associated with patient insurance profiles
    1.
    发明授权
    Systems and methods of automatically correcting errors associated with patient insurance profiles 有权

    公开(公告)号：US12131387B1

    公开(公告)日：2024-10-29

    申请号：US16717290

    申请日：2019-12-17

    申请人： WALGREEN CO.

    发明人： Nicholas J. Leners ,  Edward J. Bratton

    IPC分类号： G16H10/60 ,  G06F16/23 ,  G06F40/40 ,  G06Q40/08

    CPC分类号： G06Q40/08 ,  G06F16/2379 ,  G06F40/40 ,  G16H10/60

    摘要： Systems and methods for automatically updating patient insurance information in response to a rejection by an insurance provider are provided. An indication that a prescription submitted to an insurance provider associated with a patient for payment has been rejected by the insurance provider may be received. The indication may include a rejection message associated with the rejection of the prescription. Based on the rejection message, the systems and methods may determine whether the rejection is related to incorrect insurance information. A likelihood that the prescription will be paid for when the insurance information is corrected may be calculated. Based on the calculated likelihood, the systems and methods may determine whether to initiate a patient insurance lookup system search. Corrected insurance information associated with the patient may be obtained using the patient insurance lookup system, and the prescription may be re-submitted for payment using the corrected insurance information for the patient.

    Model-based patient adherence classification and intervention
    2.
    发明授权
    Model-based patient adherence classification and intervention 有权

    公开(公告)号：US12124973B1

    公开(公告)日：2024-10-22

    申请号：US16583827

    申请日：2019-09-26

    申请人： WALGREEN CO.

    发明人： Youbei Lou ,  Erik Groves ,  Evie Makris ,  Alexandra Broadus

    IPC分类号： G06N20/00 ,  G06N5/046 ,  G16H10/60 ,  G16H20/10 ,  G16H50/30 ,  H04L67/10

    CPC分类号： G06N5/046 ,  G06N20/00 ,  G16H10/60 ,  G16H20/10 ,  G16H50/30 ,  H04L67/10

    摘要： Systems and methods for using predictive modeling to improve patient adherence to prescription medication regimens are provided. Training data may be generated using historical prescription adherence data associated with a patient population. A prescription adherence machine learning model may be trained using the training data, and the trained model may be applied to current prescription adherence data associated with a patient to predict a likelihood that a proportion of days that the patient will be covered by a prescribed medication will be below a threshold value over a calendar year. A patient risk score may be generated for the patient based at least in part on the predicted likelihood that the proportion of days covered by the prescribed medication will be below the threshold value. Based on the patient's patient risk score, the patient may be automatically contacted for intervention.

    Machine learning system for personally optimized offer decay curves
    3.
    发明授权
    Machine learning system for personally optimized offer decay curves 有权

    公开(公告)号：US12079833B1

    公开(公告)日：2024-09-03

    申请号：US18128637

    申请日：2023-03-30

    申请人： WALGREEN CO.

    发明人： Gunjan Dhanesh Bhow ,  Ajumobi O. Udechukwu ,  Ryan M. Snedden ,  Brian C. Tyrrell

    IPC分类号： G06Q30/02 ,  G06N5/04 ,  G06N20/00 ,  G06Q30/0207 ,  G06Q30/0235

    CPC分类号： G06Q30/0224 ,  G06N5/04 ,  G06N20/00 ,  G06Q30/0235

    摘要： An offer decay generation model determines, for a particular customer, a personalized optimal offer decay curve of an incentive corresponding to a product provided by an enterprise, where the offer decay curve defines a set of decreasing incentive values and respective time intervals during which each incentive value is valid. The offer decay generation model is trained on historical data indicative of customers, customer interactions, offered incentives, resulting outcomes of the incentives, and time intervals elapsing between incentives and resulting outcomes. As such, the optimized offer decay curve is structured to maximize a probability that the particular customer is motivated to accept the incentive offer, purchase a product, and/or further interact with the enterprise during the lifetime of the offer decay curve. The offer decay curve may unique to the individual customer, and may be further customized based on other parameters such as location, time/day/date, inventories, etc.

    Automated agent messaging system
    4.
    发明授权
    Automated agent messaging system 有权

    公开(公告)号：US12047334B1

    公开(公告)日：2024-07-23

    申请号：US17958252

    申请日：2022-09-30

    申请人： WALGREEN CO.

    发明人： Oliver Derza

    IPC分类号： H04L51/02 ,  G06F16/33 ,  G06N20/00 ,  G06Q10/107 ,  G06Q30/016

    CPC分类号： H04L51/02 ,  G06F16/3344 ,  G06N20/00 ,  G06Q10/107 ,  G06Q30/016

    摘要： Methods and systems disclosed herein can assess electronic messages exchanged between messaging applications, by receiving a first version of an electronic message generated by a messaging application for review by an automated agent messaging application prior to delivery of the electronic message to an electronic device operated by a customer, the electronic message having textual content intended for the customer from an agent associated with the enterprise; automatically generating, by the automated agent messaging application, a second version of the electronic message using one or more machine learning-based models, wherein the second version of the electronic message is an adaptation of the textual content of the first version of the electronic message while maintaining an intent of the first version of the electronic message; and transmitting, to a computing device via a network, the second version of the electronic message.

    Automated SIG code translation using machine learning
    8.
    发明授权
    Automated SIG code translation using machine learning 有权

    公开(公告)号：US11914971B1

    公开(公告)日：2024-02-27

    申请号：US17974053

    申请日：2022-10-26

    申请人： WALGREEN CO.

    发明人： Oliver Derza

    IPC分类号： G10L21/00 ,  G06F9/54 ,  G06F40/295 ,  G06F40/58 ,  G06N5/04 ,  G06N20/00

    CPC分类号： G06F40/58 ,  G06F9/54 ,  G06F40/295 ,  G06N5/04 ,  G06N20/00

    摘要： A pharmacy management system includes a processor and a memory storing instructions that, when executed by processor, cause the pharmacy management system to train a machine learning model; receive a sig code utterance; analyze the sig code utterance using the trained machine learning model; and generate an output. A computer-implemented method includes training a machine learning model; receiving a sig code utterance; analyzing the sig code utterance using the trained machine learning model; and generating an output. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to train a machine learning model; receive a sig code utterance; analyze the sig code utterance using the trained machine learning model; and generate an output.

    Methods and system to estimate retail prescription waiting time
    10.
    发明授权
    Methods and system to estimate retail prescription waiting time 有权

    公开(公告)号：US11900228B1

    公开(公告)日：2024-02-13

    申请号：US18113433

    申请日：2023-02-23

    申请人： WALGREEN CO.

    发明人： Adam Robert Snopek ,  Lawrence Salud ,  Nicholas Phillip Baldwin

    IPC分类号： G06Q10/087 ,  G06N20/00 ,  G16H20/10 ,  G06Q10/04

    CPC分类号： G06N20/00 ,  G06Q10/04 ,  G06Q10/087 ,  G16H20/10

    摘要： 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.