Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Therapeutic Drug Monitoring: Drug Analysis Methods01:26

Therapeutic Drug Monitoring: Drug Analysis Methods

6
Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
6
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

419
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
419
Therapeutic Drug Monitoring: Overview and Classification01:16

Therapeutic Drug Monitoring: Overview and Classification

14
Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
14
Pharmacovigilance01:19

Pharmacovigilance

1.0K
Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
1.0K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

245
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
245
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

142
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
142

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Improved outcomes with early immunosuppression in patients with immune-checkpoint inhibitor induced myasthenia gravis, myocarditis and myositis: a case series.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2023
Same author

Safety by design: Biosafety and biosecurity in the age of synthetic genomics.

iScience·2023
Same author

Immune checkpoint inhibitor-mediated hypophysitis: no place like home.

Clinical medicine (London, England)·2023
Same author

Predictors of time to return to play and re-injury following hamstring injury with and without intramuscular tendon involvement in adult professional footballers: A retrospective cohort study.

Journal of science and medicine in sport·2021
Same author

Emergency Presentations of Immune Checkpoint Inhibitor-Related Endocrinopathies.

The Journal of emergency medicine·2021
Same author

Writing's on the wall: improving the WHO Surgical Safety Checklist.

BMJ open quality·2021

Related Experiment Video

Updated: Oct 4, 2025

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
10:17

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry

Published on: April 23, 2019

9.8K

Detecting drug diversion in health-system data using machine learning and advanced analytics.

Tom Knight1, Bernie May1, Don Tyson2

  • 1Invistics Corporation, Peachtree Corners, GA, USA.

American Journal of Health-System Pharmacy : AJHP : Official Journal of the American Society of Health-System Pharmacists
|February 9, 2022
PubMed
Summary

Novel machine learning methods significantly improve drug diversion detection in hospitals. This technology identifies diversion cases faster than traditional methods, enhancing patient safety and reducing risks for healthcare organizations.

Keywords:
controlled substance compliancediversiondrug diversion detectionhealthcare-acquired infectionsmachine learningpatient safety

More Related Videos

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.8K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K

Related Experiment Videos

Last Updated: Oct 4, 2025

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
10:17

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry

Published on: April 23, 2019

9.8K
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.8K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K

Area of Science:

  • Healthcare Informatics
  • Data Science in Medicine
  • Pharmacovigilance

Background:

  • Drug diversion from healthcare facilities is a frequent, often undetected issue.
  • Effective detection is crucial for patient safety, harm reduction, and mitigating organizational liability.

Purpose of the Study:

  • To develop and test novel methods for detecting drug diversion.
  • To improve the speed and accuracy of identifying medication diversion incidents.

Main Methods:

  • Extracted and consolidated datasets from health information technology systems across multiple hospitals.
  • Utilized supervised machine learning to train algorithms for classifying medication movement transactions by diversion risk.
  • Validated the model on a large historical dataset, comparing detection times against existing methods.

Main Results:

  • The machine learning model achieved high accuracy (96.3%), specificity (95.9%), and sensitivity (96.6%) in initial testing.
  • Detected 22 known drug diversion cases significantly faster than existing methods (160 days faster on average).
  • Users reported improved investigation efficiency with the new technology.

Conclusions:

  • Consolidated datasets and supervised machine learning offer a superior approach to drug diversion detection.
  • This technology can detect diversion cases more rapidly, improving patient safety and operational efficiency.
  • The findings support the implementation of advanced analytics for proactive risk management in healthcare settings.