US 12,148,524 B1
Apparatuses, systems, and methods for reducing return of prescriptions to stock
Lawrence Salud, Chicago, IL (US); and Zhou Jiang, Princeton, NJ (US)
Assigned to WALGREEN CO., Deerfield, IL (US)
Filed by WALGREEN CO., Deerfield, IL (US)
Filed on Apr. 14, 2023, as Appl. No. 18/135,005.
Application 18/135,005 is a continuation of application No. 16/871,224, filed on May 11, 2020, granted, now 11,664,120.
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 40/20 (2018.01); G06Q 10/087 (2023.01); G06Q 10/10 (2023.01); G16H 10/60 (2018.01); G16H 70/40 (2018.01)
CPC G16H 40/20 (2018.01) [G06Q 10/087 (2013.01); G06Q 10/10 (2013.01); G16H 10/60 (2018.01); G16H 70/40 (2018.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus for reducing return of prescriptions to stock, the apparatus comprising:
one or more processors; and
a non-transitory computer-readable medium having computer-readable instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to:
store pharmacy prescription information in a standardized pharmacy format about a prescription for a patient;
receive, over a network in near real time, updated information from one or more users in a non-standardized electronic prescription format dependent on a hardware and software platform used by the one or more users, wherein an electronic prescription data is representative of the prescription for the patient;
automatically receive patient health record data in response to the one or more processors receiving the electronic prescription data from the one or more users over the network;
generate prescription return to stock prediction data based upon the electronic prescription data, the patient health record data, and a predictive model, wherein a prescription return to stock prediction data is indicative of a probability of whether the prescription for the patient would be returned to stock;
convert, by the one or more processors, the electronic prescription data and the prescription return to stock prediction data to the pharmacy prescription information; and
generate a pharmacy prescription in a standardized format based on an updated pharmacy prescription information.