Risk Data Management for Prescription Drug Abuse Events
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Solution Overview
Problem
Existing software-based systems inadequately address adverse events such as abuse and diversion associated with the distribution of prescription drugs that have abuse liability potential, including theft, black market sales, and addiction.
Innovation Solution
A system and method for managing and analyzing adverse events by receiving and logging data on abuse and diversion, assigning scores based on event characteristics, and deploying field researchers to investigate and collect further data, with the goal of generating reports and establishing targeted intervention plans to mitigate future occurrences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing software-based systems are used to store and analyze adverse event data, then general adverse event data can be stored, but abuse and diversion events associated with prescription drugs having abuse liability potential cannot be adequately addressed
Solution Approach 1:
The system segments adverse event data management into distinct categories: general adverse events and abuse/diversion events. By creating separate processing streams and criteria for abuse-related events, the system can specifically address abuse liability potential while maintaining general adverse event capabilities. This segmentation allows targeted analysis and intervention strategies for abuse events.
Solution Approach 2:
The system introduces an intermediary scoring mechanism that evaluates received data against predetermined criteria to determine if abuse events are occurring. This scoring system acts as a mediator between raw data intake and field researcher deployment, enabling the system to identify and prioritize abuse-related events that require specialized attention.
2Measurement precision
If all received adverse event data are investigated by field researchers, then comprehensive data collection is achieved, but resource efficiency decreases due to unnecessary investigations
Solution Approach 1:
The system implements a feedback loop where received data is scored against predetermined criteria, and only events meeting threshold scores trigger field researcher deployment. This feedback mechanism ensures that investigations are launched only when abuse events are likely occurring, improving both identification accuracy and resource efficiency by avoiding unnecessary field investigations.
Solution Approach 2:
The system changes the parameter of event evaluation from binary (investigate/not investigate) to a continuous scoring scale. By assigning scores based on multiple criteria and comparing against thresholds, the system can precisely identify events requiring investigation while filtering out routine cases, thereby optimizing field researcher productivity.
3Productivity
If predetermined criteria are used to filter received data, then data processing efficiency is improved, but false positives may occur reducing measurement accuracy
Solution Approach 1:
The system applies partial filtering by using predetermined criteria to identify high-probability abuse events that definitely require investigation, while allowing lower-scoring events to be reviewed or handled differently. This partial action approach maintains processing efficiency for clear cases while preserving accuracy by not overly filtering potential events.
Data Source
AI summary
Managing and analyzing occurrences of certain types of adverse events associated with a distribution of a prescription drug into the commercial marketplace, such as a drug having an associated abuse liability potential. Data concerning an occurrence of an adverse event associated with such distribution is received and selectively logged as an event if the data satisfies one or more predetermined criteria which can be used to filter the received data. If the received data are logged as an event, a score is computed and assigned to the event. If the score meets or exceeds a pre-established threshold, a field researcher is assigned to investigate the logged event. Further data from the field researcher are stored in association with the logged event. Automated review of further data from field researchers can be performed to update the status of a logged event to closed.