Machine Learning Prescription Wait Time Estimation
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Solution Overview
Problem
Current methods fail to accurately predict and reduce patient waiting times for retail prescriptions due to variability in factors like insurance delays, stock availability, and staff numbers, leading to inconsistent patient satisfaction.
Innovation Solution
A machine learning-based prescription waiting time estimation system that analyzes characteristics of previous prescriptions, including medication, insurance, prescriber, patient, and pharmacy information, to generate accurate waiting time estimates and recommend alternative pharmacies for faster filling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If multiple channels of ordering prescriptions are offered, then perceived prescription fill time is reduced, but delays from prior authorization and prescriber contact remain
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical prescription data, insurance information, prescriber response patterns, and pharmacy workload metrics before a patient arrives. This advance preparation enables accurate waiting time predictions to be generated in real-time when prescriptions are submitted, allowing patients to make informed decisions about waiting or alternative locations.
Solution Approach 2:
The system dynamically adjusts waiting time predictions based on real-time changes in pharmacy workload, insurance authorization status, prescriber availability, and medication stock levels. This dynamic adaptation allows the prediction model to remain accurate despite varying conditions across different times of day and different pharmacies.
2Reliability
If accurate waiting time prediction is implemented, then patient satisfaction increases, but system complexity increases
Solution Approach 1:
The system uses a unified machine learning prediction model that handles multiple types of prescriptions, insurance providers, prescribers, and pharmacy locations through a single platform. This universal approach consolidates what could be many separate prediction systems into one multi-functional solution, managing complexity while maintaining accuracy across diverse scenarios.
Solution Approach 2:
The patent introduces an intermediary prediction system that acts as a mediator between the complex backend processes (insurance authorizations, prescriber communications, pharmacy operations) and the patient. This intermediary translates complex operational variables into simple, actionable waiting time estimates, reducing the perceived complexity for end users while incorporating sophisticated analysis.
3Ease of operation
If real-time waiting time estimates are provided, then patient choice and satisfaction improve, but data processing requirements increase
Solution Approach 1:
The system performs preliminary data processing by pre-collecting and organizing historical prescription data, insurance provider information, prescriber response patterns, and pharmacy workload metrics before real-time prediction is needed. This advance data preparation reduces the computational burden during real-time operations, enabling quick prediction generation without excessive data processing requirements.
Solution Approach 2:
The system optimizes data processing by transforming raw operational data into standardized parameters and features that are more efficient for machine learning models to process. This parameter transformation reduces computational complexity and data processing requirements while maintaining the accuracy needed for real-time waiting time estimates.
Data Source
AI summary
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.