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Current Trends in Nursing II01:30

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,...
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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.
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Related Experiment Video

Updated: Jul 8, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Patient Perspectives on AI-Drafted Electronic Portal Messages.

Kellie Owens1, Athmeya Jayaram1, Anand Chowdhury2

  • 1The Hastings Center for Bioethics.

JAMA Network Open
|July 7, 2026
PubMed
Summary

Patients are comfortable with artificial intelligence (AI)-drafted messages if clinicians review them, prioritizing efficiency and clear communication. Transparency about AI use is key for building patient trust in healthcare.

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Last Updated: Jul 8, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Patient Communication

Background:

  • Patient portal messaging volume has surged, contributing to clinician burnout.
  • Health systems are exploring large language models (LLMs) to draft patient message replies.
  • Little is known about patient perceptions of AI in healthcare communication.

Purpose of the Study:

  • To understand patient perceptions of AI-drafted electronic patient portal messages.
  • To identify implications for patient-centered implementation of AI in healthcare communication.

Main Methods:

  • Qualitative study using vignette-based prompts with 40 adult patients from an academic health system.
  • Interviews explored patient preferences for messaging, AI comfort, tone, content, and disclosure.
  • Recruitment prioritized diverse and previously less satisfied patient groups.

Main Results:

  • Patients prioritize timely problem resolution in portal messaging.
  • High comfort with AI-drafted messages was reported, contingent on clinician oversight.
  • Message tone and content preferences varied, driven by context rather than AI vs. human authorship.
  • Patients generally supported AI disclosure for transparency and trust.

Conclusions:

  • Patient support for AI in portal messaging is conditional on clinician oversight and accountability.
  • Implementation should focus on efficiency, context-sensitive standards, and transparent disclosure.
  • Monitoring downstream effects on quality, equity, and patient safety is crucial.