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

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

1.0K
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...
1.0K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

302
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...
302
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

59
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
59
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

68
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...
68
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

331
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
331
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

69
The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A...
69

You might also read

Related Articles

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

Sort by
Same author

Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation.

American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting·2026
Same author

Plagiarism in the Age of Generative Artificial Intelligence: The advent of generative artificial intelligence (GenAI) tools is challenging the scientific community's understanding of the meaning and significance of plagiarism. A new definition of research misconduct is needed that specifically addresses the use of GenAI writing tools.

Nature machine intelligence·2026
Same author

Improving acknowledgments sections to better credit research contributors.

Accountability in research·2026
Same author

Improving radiation shielding and mechanical properties of concrete incorporating barite, magnetite, and serpentine aggregates.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
Same author

The Vicious Spiral of AI Slop: Uncurated machine-generated content threatens research integrity and trust in science, ultimately harming all of us.

American scientist·2026
Same author

A digital archive reveals how a funding agency cooperated with academics to support the nascent field of genomics.

Nature communications·2026

Related Experiment Video

Updated: Mar 15, 2026

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
07:13

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

5.3K

A Pathophysiological Model-Driven Communication for Dynamic Distributed Medical Best Practice Guidance Systems.

Mohammad Hosseini1, Yu Jiang2, Poliang Wu2

  • 1Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA. shossen2@illinois.edu.

Journal of Medical Systems
|September 16, 2016
PubMed
Summary

This study introduces a novel system for real-time synchronization of distributed medical best practice models, improving emergency care coordination between rural and urban hospitals. The solution addresses communication challenges during patient transport, enhancing patient safety and treatment adherence.

Keywords:
Medical best practice guidance systemsMedical modelsModel-drivel communicationStroke

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.8K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

Related Experiment Videos

Last Updated: Mar 15, 2026

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
07:13

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

5.3K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.8K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

Area of Science:

  • Medical Informatics
  • Computer Science
  • Health Systems Engineering

Background:

  • Significant disparities exist in medical emergency care between rural and urban settings.
  • Existing medical best practice guidelines are often too complex for rapid clinical application, especially in rural emergency situations and during patient transport.
  • Current telemedicine literature lacks solutions for the dynamic synchronization of distributed medical models amidst communication uncertainties.

Purpose of the Study:

  • To propose a novel distributed executable medical best practice guidance system for seamless emergency care coordination.
  • To develop a robust communication architecture for real-time synchronization of distributed best practice models in dynamic healthcare environments.
  • To address the challenges of patient diagnosis and transport across geographically distributed healthcare networks.

Main Methods:

  • Codified complex medical knowledge into simplified, distributed executable disease automata.
  • Developed a pathophysiological model-driven message exchange communication architecture for reliable synchronization.
  • Utilized stroke patient transport as a use case, implementing and simulating the communication system with best practice automata.

Main Results:

  • Demonstrated a proof-of-concept for a system capable of real-time, dynamic synchronization of distributed medical best practice models.
  • The proposed architecture effectively manages uncertainties and changes inherent in emergency patient transport.
  • Laboratory simulations validated the system's potential for reliable and safe communication.

Conclusions:

  • The developed system offers a promising solution for bridging the rural-urban divide in emergency medical care.
  • The novel communication architecture addresses critical gaps in telemedicine for dynamic, distributed healthcare scenarios.
  • This approach has broad applicability across various medical domains requiring synchronized best practice adherence.