SYSTEM AND METHOD FOR DISPLAYING DYNAMIC PHARMACY INFORMATION ON A GRAPHICAL USER INTERFACE

1 . A computer system for selecting a pharmacy, the computer system comprising one or more processors configured to:

using a machine learning algorithm and an initial training dataset, build a pharmacy selection model of an individual, wherein the initial data training dataset comprises data regarding: (i) which pharmacy or pharmacies the individual has previously used; (ii) travel times to the previously used pharmacies; (iii) wait times at the previously used pharmacies; (iv) prices of medications the individual has purchased at the previously used pharmacies; (v) whether another product or class of products was available at the previously used pharmacies; and/or (vi) whether a locker was available at the previously used pharmacies, the machine learning algorithm trained by a majority vote technique;

receive: (i) an electronic indication of a medication for the individual, or (ii) a location of the individual; and

determine one or more pharmacies to be presented to the individual, the one or more pharmacies determined based on: (i) the pharmacy selection model of the individual, and (ii) the electronic indication of the medication or the location of the individual.

2 . The computer system of claim 1 , wherein the one or more processors are further configured to:

using the machine learning algorithm, continuously update the pharmacy selection model of the individual based on subsequent pharmacy use by the individual.

3 . The computer system of claim 1 , wherein the one or more processors are configured to display, on a display, a map showing pharmacies of the determined plurality of pharmacies with:

pharmacies with a short fill time displayed as green;

pharmacies with an intermediate fill time displayed as yellow; and

pharmacies with a long fill time displayed as red.

4 . The computer system of claim 1 , wherein the one or more processors are further configured to:

display the determined plurality of pharmacies as a list in an order according to: (i) a prescription fill time, and (ii) a travel time from the location of the individual.

5 . The computer system of claim 1 , wherein the initial data training dataset comprises the data regarding (iii) wait times at the previously used pharmacies or (vi) whether the locker was available at the previously used pharmacies.

6 . A computer system for selecting a pharmacy, the computer system comprising one or more processors configured to:

receive, from an individual, an indication of a medication;

determine a location of the individual;

use a machine learning algorithm to create a pharmacy selection model corresponding to the individual, the machine learning algorithm trained by a majority vote technique; and

use the pharmacy selection model to determine the first factor and the second factor;

identify a plurality of pharmacies based on a first factor; and

select a preferred pharmacy from the plurality of pharmacies based on a second factor.

7 . The computer system of claim 6 , wherein the one or more processors are further configured to determine the first and second factors from a plurality of factors including:

whether the pharmacy has a medication in stock;

wait time at the pharmacy;

geographic distance to the individual;

travel time from the location of the individual;

urgency of filling a prescription;

price of a prescription;

whether another product or class of products available at the pharmacy; and

whether a locker is available at the pharmacy.

8 . The computer system of claim 6 , wherein the first factor is geographic distance from the location of the individual.

9 . The computer system of claim 6 , wherein the second factor is a travel time including road traffic.

10 . The computer system of claim 6 , wherein the second factor is a price of the indicated medication based on an insurance carrier of the individual.

11 . The computer system of claim 6 , wherein:

the second factor is an urgency of filling a prescription; and

the one or more processors are further configured to receive an input from the individual of an indication of the urgency as a time period.

12 . The computer system of claim 6 , wherein the second factor is whether groceries are available at the pharmacy.

13 . The computer system of claim 6 , wherein:

the preferred pharmacy is a first preferred pharmacy; and

the one or more processors are further configured to:

select a second preferred pharmacy from the plurality of pharmacies based on the second factor; and

display the first and second preferred pharmacies to allow the individual to select between the first and second preferred pharmacies.

14 . The computer system of claim 6 , wherein the one or more processors are further configured to:

assign scores to each pharmacy of the plurality of pharmacies;

display the plurality of pharmacies on a map; and

color code each displayed pharmacy according to the assigned scores.

15 . The computer system of claim 6 , wherein the one or more processors are further configured to:

assign scores to each pharmacy of the plurality of pharmacies; and

display the plurality of pharmacies as a list in an order according to the assigned scores.

16 . The computer system of claim 6 , wherein the one or more processors are further configured to:

send a prescription corresponding to the indicated medication to the preferred pharmacy;

receive a locker assignment for storage of medication of the prescription; and

send the locker assignment to the individual.

17 . A computer system for selecting a pharmacy, the computer system comprising one or more processors configured to:

use a machine learning algorithm to create a pharmacy selection model corresponding to an individual, the machine learning algorithm trained by a majority vote technique;

receive an indication of a medication;

determine a location of the individual;

identify a plurality of pharmacies;

determine: (i) a travel time from the location of the individual to each pharmacy of the plurality of pharmacies, or (ii) for each pharmacy of the plurality of pharmacies, a prescription fill time; and

select a preferred pharmacy from the plurality of pharmacies based on: (i) the pharmacy selection model corresponding to the individual, and (ii) the determined travel time or the determined prescription fill times.

18 . The computer system of claim 18 , wherein the one or more processors are further configured to:

receive, from the individual, an indication of importance between travel time and prescription fill time; and

select the preferred pharmacy further based on the indication of importance.

19 . The computer system of claim 18 , wherein the determination of prescription fill time for each pharmacy of the plurality of pharmacies are based on inventory data of each pharmacy of the plurality of pharmacies.

20 . The computer system of claim 18 , wherein the one or more processors are further configured to send, to the preferred pharmacy, a prescription corresponding to the indication of the medication.