Medication Dosage Normalization for Anomalous Behavior Detection
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Solution Overview
Problem
The diversion of controlled substances, particularly prescription pain medications, is difficult to detect due to insufficient custodial oversight during handling processes, making it challenging to identify when and by whom diversion occurs.
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 by normalizing quantities to a common scale like morphine equivalent units (MME).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are applied to detect anomalous behavior in medication transactions, then detection accuracy improves, but false positives and negatives occur due to dosage discrepancies between different medications
Solution Approach 1:
The patent applies parameter changes by converting raw medication dosages into normalized equivalent units (e.g., morphine equivalent units) before analysis. This transformation standardizes different medication types and dosages into a common scale, allowing the machine learning model to accurately compare and detect anomalous behavior without being confounded by dosage discrepancies between different medications.
2Difficulty of detecting and measuring
If clinicians are monitored individually, then specific anomalous behavior can be identified, but context-specific variations in prescribing patterns lead to inaccurate detections
Solution Approach 1:
The patent applies local quality by creating peer communities that group clinicians with similar characteristics, practice settings, and patient populations. Instead of applying a uniform monitoring standard to all clinicians, the system tailors the baseline and comparison group to each clinician's local context, allowing for accurate detection of truly anomalous behavior while accounting for legitimate variations in prescribing patterns.
3Ease of manufacture
If all medication dosages are compared using raw quantities, then data processing is simple, but dosage discrepancies between different medications cause inaccurate comparisons
Solution Approach 1:
The patent transforms the parameter of medication dosage from raw quantities to normalized equivalent units. This parameter change enables accurate comparison across different medication types by converting them to a common scale (e.g., morphine equivalent units), while the automated nature of this transformation maintains computational efficiency and does not significantly complicate the data processing workflow.
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.