Prescription Pattern Monitoring With Digital Twins for Misuse Detection
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Solution Overview
Problem
There is a need for improved methods and systems to detect and address improper prescription of medication, improper utilization of prescribed medications, and diversion of prescribed medications to unprescribed uses, which can lead to adverse health effects and increased treatment costs.
Innovation Solution
A pharmacological tracking platform that utilizes machine learning modules to analyze patient data, including electronic medical records, pharmacy databases, and lifestyle information, to simulate patient health states, predict future wellness, and identify potential issues such as adverse reactions, misuse, and diversion, using digital twins to model patient and population health attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional prescription monitoring methods are used, then system complexity is low, but the ability to detect improper prescription patterns and medication misuse is insufficient
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the patient that replicates their health attributes, medication history, and physiological parameters. This digital replica allows for simulation and analysis of prescription patterns without directly interfering with the actual patient, enabling sophisticated monitoring while maintaining system modularity and manageable complexity.
Solution Approach 2:
The system performs preliminary simulations on the digital twin before making actual clinical decisions. By pre-testing prescription scenarios, predicting outcomes, and identifying potential adverse reactions in the virtual model, the system can detect improper patterns early without requiring complex real-time intervention mechanisms.
2Reliability
If comprehensive patient data is collected and analyzed, then the ability to identify adverse reactions and improper usage increases, but data processing time and computational resources increase
Solution Approach 1:
By working with a digital twin rather than directly processing all actual patient data in real-time, the system can perform comprehensive analyses on replicated data structures. This copying approach allows parallel processing and pre-computation of various scenarios, significantly reducing the time required to analyze comprehensive patient information when actual clinical decisions need to be made.
3Measurement precision
If digital twins and machine learning models are used to simulate patient health states, then prediction accuracy improves, but computational complexity and resource requirements increase
Solution Approach 1:
The digital twin serves as a computational copy that encapsulates complex patient-specific parameters and relationships. Once created, this replicated model can be used repeatedly for different simulation scenarios without requiring re-computation of underlying patient data, reducing overall computational complexity while maintaining high prediction accuracy across multiple analyses.
Data Source
AI summary
Systems and methods are provided for characterizing the activities of one or more patients in a health care system using an interception module for retrieving prescription drug data relating to the one or more patients, a correlation module that ensures that the prescription drug data is associated with the correct records of the one or more patients, and an analytics module that determines whether prescription ordering patterns for the one or more patients and indicates whether a subset of the ordering patterns is anomalous as compared with a stored ordering criterion.