Medical Data Modules for Prescription Error Detection
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Solution Overview
Problem
Current Electronic Medical Record (EMR) systems are limited in identifying medically-relevant information such as drug prescription errors, particularly those that involve drug interactions and patient-specific compatibility, leading to potential adverse medication events that may go undetected.
Innovation Solution
A medical data system that represents patient records as multi-element data modules, allowing for comparison with historical data to identify probable drug prescription errors through embedding modules in a vector space and using inference engines for real-time evaluation and alerting physicians of potential errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If commercial computerized physician order entry systems are used to identify dosage errors and incompatible drug interactions, then prescription errors are reduced, but only 53% of fatal medication orders are identified and human errors leading to prescription mix-ups increase
Solution Approach 1:
The patent segments the medical record into multiple data modules, each representing specific aspects of patient information. This modular structure allows the system to process and analyze different types of medical data separately and comprehensively, enabling more thorough detection of medically-relevant information that single-system approaches might miss.
Solution Approach 2:
The patent creates a multi-functional system that performs diverse functions: storing medical records, comparing data modules, identifying prescription errors, detecting drug interactions, and generating alerts. This universal approach addresses multiple limitations of existing systems simultaneously, improving both reliability and information detection capability.
2Productivity
If EMR systems are implemented to lower prescription errors through automated identification, then dosage errors and allergies are detected, but dependence on these systems increases human errors resulting in prescription mix-ups
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously compares patient data modules against stored medical records and provides alerts to physicians. This feedback loop allows for real-time correction of potential errors while maintaining physician oversight, thereby improving productivity without compromising reliability by reducing over-dependence on automated systems.
Solution Approach 2:
The patent introduces an intermediary comparison system that acts as a mediator between the EMR system and the physician. This intermediary layer processes and validates information before presenting it to the physician, reducing the likelihood of prescription mix-ups by providing an additional layer of verification without eliminating physician judgment.
3Measurement precision
If data modules are compared to historical data using vector space embedding and inference engines, then medically-relevant information such as probable drug prescription errors are identified, but system complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing medical record data into standardized data modules before comparison. This preliminary structuring of data simplifies the subsequent comparison process and reduces the computational complexity of the inference engine, while maintaining high measurement precision in error identification.
Solution Approach 2:
The patent transforms medical record data into vector space representations with specific parameters and dimensions. This parameter transformation enables efficient comparison and analysis while managing system complexity through mathematical abstraction, allowing accurate identification of prescription errors without proportionally increasing system complexity.
Data Source
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AI summary
A medical database and system and method using same are provided. The medical database includes a data unit for storing modules representing medical records of subjects. Each module includes a plurality of module elements each representing a medically-relevant parameter of the subject with each element assigned a specific identifier in the module and a numerical value corresponding to the medically-relevant parameter.