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Related Concept Videos

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic illness...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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 relationship...

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Related Experiment Videos

Beyond the Model: Practical Insights from Monitoring Predictive Models across Diverse Clinical Workflows.

John J Hanna1,2,3,4, Christopher R Dennis1, Andrew O Johnson1,5

  • 1ECU Health, Information Services, North Carolina, United States, Greenville.

Applied Clinical Informatics
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

A modular monitoring system was implemented to oversee artificial intelligence (AI) clinical predictive models in an academic health system. This approach enabled sustainable oversight and user-centric performance evaluation.

Related Experiment Videos

Area of Science:

  • Healthcare technology
  • Artificial intelligence in medicine
  • Clinical informatics

Background:

  • Academic health systems increasingly use artificial intelligence (AI) for clinical decision support.
  • Electronic health record (EHR) vendor-provided predictive models require robust lifecycle oversight.
  • Sustainable monitoring is crucial for responsible AI implementation in healthcare.

Purpose of the Study:

  • To describe lessons learned from implementing a modular monitoring system for EHR vendor-provided clinical predictive models.
  • To support the artificial intelligence (AI) lifecycle within an integrated academic health system.
  • To enable sustainable oversight of deployed AI models.

Main Methods:

  • Developed a modular monitoring system with daily automated data pipelines.
  • Utilized an interactive application to bridge technical and operational gaps for stakeholders.
  • Incorporated automated and assisted performance measures, flag rates, and problem-specific metrics.

Main Results:

  • Effective oversight required clear technical and operational ownership and integration into AI governance.
  • Monitoring encompassed alert burden, workflow alignment, and problem-level signals beyond performance drift.
  • Modularity allowed adaptation to evolving models and workflows; silent alert simulations had limitations in predicting real-world performance.

Conclusions:

  • A modular, governance-aligned system facilitated sustained oversight of vendor-provided clinical predictive models.
  • Monitoring shifted towards user-centric and operationally meaningful metrics.
  • Integrated monitoring infrastructure is essential for responsible AI governance in clinical settings.