Peer community based anomalous behavior detection

Abstract

A peer network includeing nodes corresponding to different clinicians. An edge interconnects 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 is applied to identify, in the peer network, one or more peer communities of clinicians. The activity pattern of a clinician is compared to the activity patterns of other clinicians in the same peer community to determine whether that clinician exhibits anomalous behavior. An investigative workflow is triggered when the clinician is determined to exhibit anomalous behavior. The investigative workflow includes generating an alert, activating surveillance devices, and / or isolating medicationaccessed by the clinician.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to U.S. Provisional Application No. 62 / 667,409, filed May 4, 2018, entitled "ADDRESSING ANOMALOUS BEHAVIORAMONG PEER GROUPS OF CLINICIANS," the disclosure of which is incorporated herein by reference in its entirety. technical field [0003] The subject matter described herein relates generally to complex networks, and more specifically to a peer-to-peer community-based technique for detecting anomalous behavior. Background technique [0004] Misappropriation can refer to the transfer of a controlled substance to a third party who is not legally authorized to receive, possess and / or administer the controlled substance. High-value and / or controlled prescription drugs, especially opioids, are especially prone to misappropriation. For example, prescription pain medications may be diverted while being loaded into and / or retrieved from the dispensing cabinet. Some prescription pain medicat...

AI Technical Summary

Problems solved by technology

For example, some pain medications may be withdrawn before being loaded into the pump

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