Machine learning based safety controller

Abstract

A method may include identifying a shift associated with a clinician by applying a machine learning model trained to identify, based on a series of transaction records associated with the clinician, one or more shifts associated with the clinician. The clinician may be identified as likely to engage in a hazardous behavior based at least on the shift associated with the clinician. In response to determining that the clinician as likely to engage in the hazardous behavior, activating a protective workflow. The protective workflow may be configured to prevent the clinician from engaging in the hazardous behavior as well as to collect evidence associated with the hazardous behavior. Related methods and articles of manufacture are also disclosed.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to U.S. Provisional Application No. 62 / 789,337, entitled "MACHINE LEARNING BASED SHIFTTRACKING," and filed January 7, 2019, the disclosure of which is incorporated by reference in its entirety. into this article. technical field [0003] The subject matter described herein relates generally to machine learning, and more specifically, to machine learning-based techniques for detecting and preventing harmful behavior. Background technique [0004] Harmful behaviors that lead to loss of resources and harm to patients as well as clinicians can occur in a variety of healthcare settings. Diversion may be an example of a harmful practice where instead of administering a substance to a patient and / or discarding the substance, the clinician retains the substance for unauthorized personal use and / or personal gain. High value medical supplies and controlled pharmaceuticals (eg, opiates, opioids,...

AI Technical Summary

Problems solved by technology

For example, clinicians may seize, administer and / or discard incorrect types and / or quantities of medications

Images