Medication Dispensing Equipment With User-Behavior Diversion Scoring
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Solution Overview
Problem
Medical facilities face challenges in identifying and preventing the diversion of medications and medical supplies due to lack of supervision and difficulty in tracking user behavior.
Innovation Solution
A medication dispensing system that analyzes user interactions to identify statistically anomalous behavior, generates diversion scores, and flags potential diverters, preventing them from accessing the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If medication dispensing systems are made accessible to medical personnel for use, then the ease of operation is improved, but the risk of diversion by unauthorized individuals increases
Solution Approach 1:
The system performs preliminary actions by analyzing user behavior patterns and generating diversion scores before actual diversion can occur. The processor continuously monitors usage data and identifies anomalous behavior patterns, flagging potential diverters in advance to prevent further diversion activities.
Solution Approach 2:
The system implements feedback mechanisms by generating diversion scores based on analyzed usage data and providing this information back to facility personnel. The system continuously monitors user interactions with the medication dispensing system and provides real-time or periodic feedback about potential diversion risks, enabling corrective actions.
2Reliability
If supervision and tracking of user behavior are increased to prevent diversion, then the reliability is improved, but the device complexity increases
Solution Approach 1:
The system performs self-service by automatically analyzing usage data and generating diversion scores without requiring external intervention. The processor autonomously processes usage data, identifies patterns, and generates diversion scores, reducing the need for manual supervision while maintaining high reliability in diversion detection.
Solution Approach 2:
The system replaces complex mechanical supervision mechanisms with electronic data processing and analytical algorithms. Instead of requiring physical monitoring or manual tracking of user behavior, the system uses electronic usage data collection and automated pattern recognition to achieve reliable diversion detection with simpler operational complexity.
3Measurement precision
If all users are monitored equally for diversion behavior, then the measurement precision is improved, but the loss of time in analyzing user data increases
Solution Approach 1:
The system applies local quality by focusing analytical resources on users exhibiting anomalous behavior patterns rather than treating all users uniformly. The processor identifies and flags users with diversion scores above certain thresholds, concentrating investigative attention on high-risk individuals while reducing analysis overhead for low-risk users.
Solution Approach 2:
The system uses parameter changes by adjusting the stringency of monitoring based on user risk profiles. The diversion score threshold and analysis frequency can be modified based on user behavior patterns, allowing the system to maintain high detection precision for high-risk users while reducing time investment for low-risk users.
Data Source
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AI summary
A method for identifying medical diverters includes identifying users having similar job functions. Use data indicative of user access to a medication dispensing system is retrieved and is analyzed to identify periods of use of the system for users. Boundaries of work shifts are determined and users are organized into work shifts based on periods of use. A comparison period is determined. Diversion data indicative of behavior associated with diversion for each user is identified. A diversion score indicative of a likelihood that a user is diverting medication is generated by averaging the data by shifts worked for each user and statistically comparing the averaged data. Diversion scores are combined for a medication type to generate a group score. A consistency factor is determined and an overall score is generated. A determination whether any overall scores exceed an overall threshold is made. Users whose score exceeds the threshold are flagged.