Rare Instance Analytics for Clinician Diversion Detection
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Solution Overview
Problem
Conventional statistical analysis techniques, such as machine learning models, disregard infrequent activity patterns that occur at a below threshold frequency, missing potential indicators of diversion in controlled and high-value substance handling, particularly in medical settings.
Innovation Solution
An analytics engine analyzes infrequent activity patterns using data models specific to anomalous behavior, identifying rare data signals indicative of diversion by applying machine learning models trained to detect deviations from normative clinician activity patterns, and triggers investigative workflows when anomalous behavior is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional statistical analysis techniques are used to analyze clinician activity patterns, then the system can identify common patterns, but infrequent activity patterns that occur below threshold frequency are disregarded as statistically insignificant, causing potential diversion indicators to be missed
Solution Approach 1:
The system segments the analysis into two distinct pathways: one for frequent patterns (using conventional statistical analysis) and one for infrequent patterns (using anomaly detection models). This segmentation allows each pathway to handle its specific type of data appropriately, preventing loss of infrequent activity pattern information while maintaining efficient processing of common patterns.
Solution Approach 2:
Instead of filtering out infrequent patterns as conventional methods do, the system inverts the approach by specifically targeting and analyzing these rare patterns through anomaly detection models. This inversion reveals that infrequent activity patterns, often dismissed as noise, actually contain valuable diversion indicators that would otherwise be lost.
2Reliability
If machine learning models are trained to detect deviations from normative activity patterns, then the system can identify anomalous behavior, but infrequent patterns below threshold frequency are still discarded as statistically insignificant
Solution Approach 1:
The system divides the activity pattern processing into segmented pathways based on frequency thresholds. Frequent patterns go through conventional analysis while infrequent patterns are routed to anomaly detection models. This segmentation maintains reliability by ensuring infrequent patterns receive appropriate analysis, while productivity is maintained through efficient processing of common patterns.
Solution Approach 2:
The system changes the analysis parameter from frequency-based filtering to frequency-based routing. Instead of discarding patterns below a frequency threshold, the system uses this parameter to direct different types of analysis, thereby maintaining both reliability in detecting rare diversion indicators and productivity in processing high-volume data.
3Measurement precision
If the system analyzes all activity patterns including infrequent ones, then potential diversion indicators can be identified, but the system complexity increases due to the need for multiple analysis approaches
Solution Approach 1:
The analytics engine is segmented into distinct functional modules: conventional statistical analysis for frequent patterns and anomaly detection models for infrequent patterns. This segmentation achieves high precision in identifying diversion indicators while managing complexity through modular architecture, where each module handles specific types of patterns with dedicated algorithms.
Solution Approach 2:
The system applies partial action by analyzing only the portion of activity patterns that require specialized attention (infrequent patterns) using advanced anomaly detection, while using simpler conventional methods for the majority of frequent patterns. This partial application of complex analysis only where needed maintains precision for rare diversion indicators while limiting overall system complexity.
Data Source
AI summary
A method for detecting diversion may include identifying an activity pattern associated with a clinician as being an infrequent activity pattern that occurs below a threshold frequency. Whether the infrequent activity pattern corresponds to an anomalous behavior may be determined based at least on one or more data models. The infrequent activity pattern may include a series of transaction records, which may be matched to the reference transaction values included in each of the one or more data models. An investigative workflow may be triggered in response to the infrequent activity pattern being determined to correspond to the anomalous behavior. Related methods and articles of manufacture are also disclosed.