Controlled Substance Stakeholder Rating System
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Solution Overview
Problem
Pharmaceutical stakeholders in the distribution chain face challenges in detecting illicit use of controlled substances due to lack of insight into customer purchase volumes and business practices, leading to increased DEA investigations and operational interruptions.
Innovation Solution
A computer-implemented system that rates and ranks stakeholders by aggregating and analyzing retail prescription, encrypted patient, and pharmaceutical purchase data to provide a comprehensive view of potential misuse, helping identify suspicious activity and ensure compliance with DEA regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If wholesalers aggregate and analyze retail prescription data and pharmaceutical purchase data, then detection capability of illicit use is improved, but device complexity increases
Solution Approach 1:
The system segments data collection by stakeholder type (wholesalers, retailers, prescribers) and aggregates data at appropriate levels. Each stakeholder receives customized insights based on their role, allowing complex analysis without overwhelming individual components of the system.
Solution Approach 2:
The patent introduces an intermediary data aggregation and analysis system that sits between raw data sources and end users. This intermediary layer processes, anonymizes, and presents data in actionable formats, reducing the complexity burden on individual stakeholders while maintaining high detection capability.
2Measurement precision
If retailers access comprehensive sales data and prescriber information, then measurement precision of abnormal quantities is improved, but loss of information increases due to data privacy requirements
Solution Approach 1:
The system extracts only the necessary information elements needed for detection and analysis purposes while leaving out personally identifiable information. Anonymous identifiers and aggregated statistics are used instead of raw patient or prescriber data, maintaining precision without compromising privacy.
Solution Approach 2:
The patent creates anonymized copies of sensitive data that retain the statistical and analytical properties needed for detection while removing identifying information. These synthetic datasets enable precise measurement of abnormal quantities without exposing actual patient or prescriber identities.
3Reliability
If the system aggregates data from multiple sources including encrypted patient data, then reliability of illicit use detection is improved, but device complexity increases
Solution Approach 1:
The system dynamically adjusts data aggregation levels and analysis depth based on the specific detection needs and stakeholder roles. Data integration is performed on-demand and adaptively, allowing high reliability detection while managing complexity through flexible, situation-appropriate processing.
Solution Approach 2:
The patent creates a universal data aggregation platform that serves multiple functions: data collection, anonymization, analysis, and customized reporting for different stakeholder types. This multi-functional system improves reliability across all user groups while avoiding the need for separate complex systems for each stakeholder.
4Difficulty of detecting and measuring
If prescribers access patient purchase data and retail outlet information, then detection capability of doctor shopping is improved, but loss of time increases due to data processing requirements
Solution Approach 1:
The system performs preliminary data aggregation, anonymization, and analysis before prescribers need to access information. Detection algorithms pre-process data to identify potential doctor shopping patterns, so prescribers receive ready-analyzed results rather than raw data requiring their time-consuming analysis.
Solution Approach 2:
The patent implements feedback mechanisms where detection results are rapidly communicated back to relevant stakeholders. Anomaly detection triggers automated alerts and preliminary investigations, reducing the time prescribers need to spend on manual detection while maintaining high detection capability through continuous monitoring feedback loops.
Data Source
AI summary
The disclosure generally describes computer-implemented methods, software, and systems for rating and ranking stakeholders in the distribution of controlled substances. One computer-implemented method includes receiving, at a computer system, retail prescription data, encrypted patient data, reference prescriber data, and pharmaceutical purchase data. For a retail outlet included in the received data, the method includes aggregating the received data and determining information about sales of a pharmaceutical product associated with the retail outlet. The method further includes rating the retail outlet based on the aggregated information.