Medication Dispensing System Diversion Detection
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Solution Overview
Problem
The diversion of high-value and controlled prescription medications, such as opiates and narcotics, occurs due to insufficient custodial oversight during dispensing, administration, and wasting, making it difficult to detect and identify responsible individuals.
Innovation Solution
A medication dispensing system that includes a data processor and memory to capture and analyze data from a dispensing cabinet, using machine learning models to detect diversion by identifying physical and behavioral patterns, and triggering an investigative workflow to isolate and verify suspicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional dispensing cabinets are used without advanced monitoring, then device complexity is low, but diversion detection capability is insufficient
Solution Approach 1:
The system performs preliminary actions by capturing data (videos, images, audio, biometrics) before diversion can occur, and by pre-training machine learning models on diversion patterns. This allows the system to proactively detect and prevent diversion rather than reacting after it happens, thereby improving reliability without proportionally increasing complexity.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw dispensing cabinet data and diversion detection. This intermediary layer processes and analyzes data patterns, enabling sophisticated diversion detection while abstracting the complexity away from the core dispensing cabinet operations.
2Measurement precision
If machine learning models are applied to detect diversion, then diversion detection accuracy improves, but data processing time increases
Solution Approach 1:
The machine learning models are trained in advance on extensive datasets containing diversion patterns and normal behaviors. This preliminary training allows the models to quickly process and analyze real-time dispensing cabinet data without requiring extensive computation during actual monitoring, thereby maintaining high detection accuracy while minimizing processing time delays.
Solution Approach 2:
The patent replaces traditional rule-based or manual diversion detection methods with machine learning-based automated analysis. This substitution enables the system to process complex behavioral patterns and physical traits data much faster than manual review while maintaining or improving detection accuracy through pattern recognition.
3Reliability
If investigative workflow is triggered for all suspicious activities, then diversion identification reliability improves, but operational efficiency decreases
Solution Approach 1:
The system applies partial action by triggering the full investigative workflow only for suspicious activities that meet specific thresholds or patterns identified by the machine learning model. Not all accessed medications require full investigation - the system selectively applies investigative resources to the most suspicious cases, thereby maintaining high reliability for actual diversion detection while preserving operational efficiency by avoiding unnecessary investigations of routine activities.
Data Source
AI summary
A method for detecting diversion may include receiving, from a dispensing cabinet including medication, data associated with a plurality of individuals accessing the dispensing cabinet to retrieve and/or return the medication. Diversion of the medication may be detected by at least applying, to at least a portion of the data received from the dispensing cabinet, a machine learning model trained to detect diversion. An identity of a first individual responsible for the diversion may be determined based on the data received from the dispensing cabinet. In response to the determination of the first individual as being responsible for the diversion, an investigative workflow may be triggered at the dispensing cabinet. Related systems and articles of manufacture, including apparatuses and computer program products, are also disclosed.