Prescription Image Processing for Medication Adherence
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Solution Overview
Problem
Medication adherence is a significant issue, with approximately 50% of patients not taking their medications as prescribed, leading to substantial burdens for patients, pharmacies, and the pharmaceutical industry, with costs estimated at $637 billion globally due to non-adherence.
Innovation Solution
A computer-implemented method and system that processes prescription documents by extracting pharmacy and medical product identifiers from image data, allowing for automated determination of medical products dispensed, prescription instructions, and patient notifications, thereby facilitating medication adherence through image-based processing and personalized communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry is used for prescription processing, then patient privacy can be protected, but medication adherence deteriorates due to lack of automated reminders and tracking
Solution Approach 1:
The system enables self-service by allowing patients to scan their own prescription documents using mobile devices, automatically extract information, and receive personalized reminders without manual pharmacy intervention. This automated self-processing improves adherence while maintaining privacy through secure local handling of prescription data.
Solution Approach 2:
The patent replaces manual mechanical data entry processes with optical scanning and automated image processing systems. Mobile devices capture prescription images, which are then automatically processed using pattern recognition and data extraction algorithms, eliminating the need for manual typing and reducing errors while improving adherence tracking.
2Productivity
If automated image processing is implemented, then productivity improves through faster processing, but device complexity increases due to multiple parsing methods and image analysis algorithms
Solution Approach 1:
The system segments the prescription processing task into distinct modules: image capture, pharmacy identifier extraction, parsing method selection, medical product identifier extraction, and reminder generation. Each module handles a specific function, improving processing efficiency while managing complexity through modular design that allows independent optimization of each component.
Solution Approach 2:
The system implements universal parsing methods that can handle multiple pharmacy document formats and identifier types (NDC, DIN, NPN) through a single integrated platform. The image processing system is designed to be format-agnostic, automatically detecting and adapting to different prescription layouts, which improves productivity without proportionally increasing complexity.
3Measurement precision
If pharmacy-specific parsing methods are used, then measurement precision improves for extracting medical product identifiers, but adaptability decreases when encountering unfamiliar pharmacy formats
Solution Approach 1:
The system employs dynamic parsing method selection that adapts to the specific pharmacy identifier detected in each prescription image. When a pharmacy identifier is recognized, the system automatically selects the corresponding optimized parsing method for that pharmacy, ensuring high precision for known formats while maintaining the ability to handle new formats through the general extraction framework.
Solution Approach 2:
The patent introduces an intermediary layer that acts as a bridge between diverse pharmacy formats and the extraction system. This intermediary includes a pharmacy identifier database and pattern recognition module that translates various pharmacy-specific formats into a standardized intermediate representation, allowing precise extraction for known pharmacies while maintaining adaptability to unfamiliar formats through the translation layer.
Data Source
AI summary
Various embodiments are described herein for a system and method for determining a medical product dispensed by a pharmacy. The method involves operating a processor to: receive, from a computing device, image data depicting at least a portion of a prescription document issued by the pharmacy; extract, from the image data, a pharmacy identifier for identifying the pharmacy associated with issuing the prescription document; select, based on the pharmacy identifier, at least one parsing method for parsing prescription documents issued by the pharmacy identified by the pharmacy identifier; and apply the selected parsing method to the image data to determine a medical product identifier for identifying the medical product dispensed by the pharmacy.