Pharmacy Selection Model Using Machine Learning
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Solution Overview
Problem
Individuals face difficulties in selecting the most suitable pharmacy for filling prescriptions due to lack of information, leading to potential selection of pharmacies that do not meet their needs, and similar challenges exist when choosing locations for purchasing merchandise items that require preparation time.
Innovation Solution
A computer system utilizing machine learning algorithms and training datasets to build personalized pharmacy selection models based on historical data, including pharmacy usage, travel times, wait times, prices, and availability of products, to determine the most suitable pharmacies or merchandise pickup locations, considering factors like travel time, prescription fill time, and product availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If an individual selects a pharmacy without sufficient information, then the selection process is simple and quick, but the pharmacy may not be the best suited to the individual's needs
Solution Approach 1:
The system performs preliminary actions by pre-gathering comprehensive pharmacy information including travel times, wait times, prices, and product availability. A pharmacy selection model is built in advance using machine learning algorithms and training datasets, so that when an individual needs to select a pharmacy, the analysis is already complete and ready for immediate presentation.
Solution Approach 2:
The patent introduces an intermediary system that acts as a mediator between individuals and pharmacies. This system collects and processes information from multiple pharmacies, analyzes individual preferences through a trained model, and presents optimized recommendations, thereby eliminating the need for individuals to manually gather and compare pharmacy information.
2Reliability
If comprehensive pharmacy information is gathered and analyzed, then the pharmacy selection is optimized for individual needs, but the system complexity increases
Solution Approach 1:
The system employs self-service mechanisms where the machine learning model automatically trains itself using historical pharmacy data and individual selection patterns. The pharmacy selection model continuously improves its recommendations by learning from user behavior, travel time data, wait time data, and price information without requiring manual intervention or complex configuration.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting pharmacy recommendations based on multiple variables including travel time, wait time, price, product availability, and individual preferences. The system changes these parameters in real-time to optimize the pharmacy selection for each specific user situation.
3Measurement precision
If multiple pharmacy factors are considered (travel time, wait time, price, product availability), then the selection accuracy improves, but the information processing requirement increases
Solution Approach 1:
The patent segments the complex pharmacy selection process into distinct analytical components: travel time analysis, wait time analysis, price comparison, and product availability checking. Each factor is evaluated separately by the machine learning model and then integrated to produce the final recommendation, making the information processing more manageable and efficient.
Data Source
AI summary
The following relates generally to pharmacy and/or merchandise pickup location selection. In some embodiments, factors are used to determine a pharmacy and/or merchandise pickup location selection for an individual. In this regard, the factors may include: whether the pharmacy and/or merchandise pickup location has a medication in stock; wait time at the pharmacy and/or merchandise pickup location; 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/or merchandise pickup location; and/or whether a locker is available at the pharmacy and/or merchandise pickup location. In some embodiments, Artificial Intelligence (AI) is used to create a model of pharmacy and/or merchandise pickup location selection for the individual.