Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

Current Trends in Nursing II

Current Trends in Nursing II

Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...

Methods of Documentation VI: Case Management Model

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...

Errors occurring during blood pressure monitoring

Errors occurring during blood pressure monitoring

Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...

Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...

Rheumatic Heart Disease IV: Nursing Management

Rheumatic Heart Disease IV: Nursing Management

AssessmentA comprehensive assessment is essential in managing a patient with rheumatic heart disease (RHD). Begin with obtaining a detailed medical history, including recent streptococcal infections, a history of rheumatic fever, or previously diagnosed rheumatic heart disease. Assess the patient for symptoms such as fever, chest pain, widespread joint pain (arthralgia), tachycardia, pericardial friction rub, muffled heart sounds, heart murmurs, peripheral edema, subcutaneous nodules, and...

You might also read

Related Articles

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

Sort by

Same author

Revitalizing Stroke Care: The LEADER Strategy for Sustainable Transformation in Health Care Delivery.

Stroke·2024

Same author

Updating Race-Based Risk Assessment Algorithms in Clinical Practice: Time for a Systems Approach.

The American journal of bioethics : AJOB·2021

Same author

Mobile Health App for Prostate Cancer Patients on Androgen Deprivation Therapy: Qualitative Usability Study.

JMIR mHealth and uHealth·2020

Same author

Access denied: The relationship between patient insurance status and access to high-volume hospitals.

Cancer·2020

Same author

The provision of comfort: addressing barriers to sleep in prostate cancer.

BJU international·2020

Same author

Lower odds of cardiac events for gonadotrophin-releasing hormone antagonists versus agonists.

BJU international·2020

Search research articles

Related Experiment Video

Updated: Jan 10, 2026

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

Understanding the Risks and Benefits of Implementing AI-Enabled Remote Patient Monitoring Systems for Disease

Junaid Nabi^1,2, Richard Staynings^3,4, Javaid Iqbal Sofi⁵

¹RAND School of Public Policy, Santa Monica, CA 90401, USA.

International Journal of Environmental Research and Public Health

|November 27, 2025

Summary

This summary is machine-generated.

Managing risk is key for healthcare innovation with AI and machine learning. Policymakers should use an optimal risk framework to balance safety and benefits of AI-enabled remote patient monitoring.

Keywords:

AI-enabled remote patient monitoring algorithmic accountability cybersecurity machine learning systems patient safety policy analysis regulatory frameworks risk analysis risk mitigation technology adoption

More Related Videos

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

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

Published on: April 12, 2021

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Related Experiment Videos

Last Updated: Jan 10, 2026

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

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

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

Published on: April 12, 2021

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Area of Science:

Healthcare innovation
Artificial intelligence in medicine
Risk management in healthcare

Background:

Advancements in artificial intelligence (AI) and machine learning (ML) are transforming healthcare delivery, aiming to improve accessibility, efficiency, and equity.
AI-enabled remote patient monitoring (RPM) devices offer significant potential for enhanced patient care.
Effective risk management is crucial for the successful adoption of these innovative technologies.

Purpose of the Study:

To assess the practical utility of AI-enabled remote patient monitoring (RPM) devices.
To identify and evaluate the risks associated with AI-enabled RPM.
To propose a framework for policymakers to ensure patient safety while maximizing the benefits of these technologies.

Main Methods:

Literature review on AI/ML in healthcare and risk management frameworks.
Analysis of risk assessment methodologies for medical devices.
Framework development for optimal risk evaluation in AI-enabled RPM.

Main Results:

Distinguishing between acceptable risk and optimal risk is vital for AI-enabled RPM.
An optimal risk framework balances risk reduction costs with technological benefits.
This framework guides policymakers in ensuring patient safety and maximizing AI advantages.

Conclusions:

Policymakers must adopt an optimal risk framework for AI-enabled RPM.
This approach ensures patient safety and promotes innovation in healthcare technology.
Balancing risk and benefit is essential for the responsible integration of AI in healthcare.