Medication Dosage Normalization for Clinician Anomaly Detection
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Solution Overview
Problem
The diversion of controlled substances, particularly prescription pain medications, is difficult to detect due to lack of custodial oversight during handling, making it challenging to identify when diversion occurs and the responsible clinicians.
Innovation Solution
A system utilizing an analytics engine that normalizes transaction records of medication interactions using equivalent units and applies machine learning models to identify peer communities of clinicians, comparing activity patterns to detect anomalous behavior such as diversion, overmedication, or undermedication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transaction records of different medications are compared using raw quantities, then the detection process is simple, but the detection accuracy is poor due to lack of dosage normalization
Solution Approach 1:
The patent transforms the parameter of medication quantity from raw dosage to normalized dosage using morphine equivalent units. This parameter change enables accurate comparison across different medication types by converting all dosages to a common reference scale, thereby improving detection accuracy without requiring complex medication-specific analysis for each record
Solution Approach 2:
The patent introduces morphine equivalent units as an intermediary standard for normalizing medication quantities. This intermediary conversion factor serves as a mediator that translates diverse medication dosages into a unified measurement scale, enabling the machine learning model to accurately compare and analyze transaction records across different medication types
2Reliability
If all clinicians are compared together, then the analysis is straightforward, but false positives increase due to lack of peer community context
Solution Approach 1:
The patent segments the clinician population into distinct peer communities based on shared attributes such as practice settings, patient populations, and medication types. This segmentation allows for contextualized comparison within homogeneous groups, reducing false positives by accounting for legitimate variations in prescribing patterns across different practice environments
Solution Approach 2:
The patent performs preliminary clustering of clinicians into peer communities before conducting anomaly detection. This preliminary action establishes the contextual framework necessary for accurate comparison, ensuring that each clinician is evaluated against appropriate peers rather than the entire population, thereby improving reliability before the main detection process
3Measurement precision
If detailed transaction records are analyzed, then detection precision improves, but processing time increases
Solution Approach 1:
The patent performs preliminary normalization of medication quantities to morphine equivalents and pre-clustering of clinicians into peer communities before applying the machine learning anomaly detection model. These preliminary actions prepare the data in an optimized format that enables the model to process records efficiently while maintaining high detection precision, reducing the computational burden during the actual anomaly detection phase
Data Source
AI summary
A method may include receiving a first transaction record indicating a first interaction with a first raw quantity of a first medication and a second transaction record indicating a second interaction with a second raw quantity of a second medication. The first transaction record and the second transaction record may be normalized by generating, based on an equivalent unit, a first normalized quantity of the first medication and a second normalized quantity of the second medication. A machine learning model may be applied to the normalized first transaction record and second transaction record to detect, based on the first transaction record and the second transaction record, an anomalous behavior. An investigative workflow may be triggered in response to the machine learning model detecting the anomalous behavior. Related systems and articles of manufacture, including computer program products, are also provided.