Machine-Learning Peer Communities for Clinician Anomaly Detection
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Solution Overview
Problem
The diversion of controlled substances, particularly prescription pain medications, is difficult to detect and attribute due to insufficient custodial oversight during shipping, receiving, stocking, dispensing, administration, and wasting, and existing analytics systems are prone to false positives and negatives when comparing activity patterns of clinicians with varying legitimate causes.
Innovation Solution
A system utilizing a machine-learning model to identify peer communities of clinicians based on shared attributes and activity patterns, comparing activity data within these communities to detect anomalous behavior, and triggering investigative workflows when deviations from expected norms are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing analytics systems compare activity patterns of all clinicians, then they can detect potential anomalies, but they produce high false positives and negatives due to varying legitimate causes
Solution Approach 1:
The patent segments the clinician population into peer communities based on shared attributes (work area, shift, role, etc.). This segmentation allows comparison of activity patterns only within homogeneous groups, eliminating false positives caused by comparing dissimilar clinicians while maintaining detection sensitivity within each community.
Solution Approach 2:
The system applies different comparison criteria and peer groups to different clinicians based on their local characteristics (work area, shift, role). Each clinician is evaluated against peers with similar legitimate causes for activity variations, making the detection locally adaptive and reducing false positives.
2Reliability
If peer community identification is implemented, then false positives are reduced, but system complexity increases due to machine-learning model requirements
Solution Approach 1:
The machine-learning model automatically identifies peer communities and activity patterns without requiring manual configuration or expert intervention. The system self-organizes clinicians into appropriate peer groups based on their attributes and behavior, reducing operational complexity despite the underlying computational complexity.
Solution Approach 2:
The machine-learning model serves multiple functions: it identifies peer communities, determines activity patterns, detects anomalies, and adapts to new clinicians and attributes. This multi-functionality consolidates what would otherwise require separate systems into a single platform.
Data Source
AI summary
A peer network may include nodes corresponding to different clinicians. An edge may interconnect the two nodes based on the corresponding clinicians sharing at least one common attribute such as for example, treating the same patients and/or interacting with the same medical devices. A machine-learning model may be applied to identify, in the peer network, one or more peer communities of clinicians. The activity pattern of a clinician may be compared to the activity patterns of other clinicians in the same peer community to determine whether that clinician exhibits anomalous behavior. An investigative workflow may be triggered when the clinician is determined to exhibit anomalous behavior. The investigative workflow may include generating an alert, activating surveillance devices, and/or isolating medication accessed by the clinician.