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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,...

Nursing Evaluation

Nursing Evaluation

The evaluation stage signals the end of the nursing process. The nurse gathers evaluative data to assess whether or not the patient has attained the expected results. Whereas the nurse collects data in the nursing assessment to identify the patient's health concerns, the evaluation stage data determines if the indicated health issues are resolved. Evaluative data collection includes two sections: the data acquired to evaluate patient outcomes and the time criteria for data collection.

Nursing Implementation

Nursing Implementation

Implementation is the execution of the nursing care plan developed during the planning phase.
The five steps to implementing effective nursing care include reassessing the patient, reviewing and revising the existing nursing care plan, organizing the resources and care delivery, anticipating and preventing complications, and implementing nursing interventions.

Role of Communication in the Nursing Process III: Evaluation and Documentation

Role of Communication in the Nursing Process III: Evaluation and Documentation

A successful patient outcome depends mainly on the evaluation stage of the nursing process. Evaluation determines effectiveness by reviewing what was done previously after the completion of nursing interventions. Every time a healthcare professional steps in or administers treatment, they must reassess or evaluate the action to ensure the intended result. During the evaluation phase, there are three probable patient outcomes:

Nursing Clinical Information System

Nursing Clinical Information System

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Nursing Process for Patient and Caregiver Teaching III: Evaluation and Documentation

Nursing Process for Patient and Caregiver Teaching III: Evaluation and Documentation

Evaluation of the teaching process enables the nurse to determine if the patient's learning needs were met and if training was effective. If the expected outcomes are not met, the care plan is revised, and additional education or reinforcement is provided. Nurses can ask questions after the session or obtain feedback to assess the patient's understanding of the topic.
Nurses can use several methods to evaluate patient outcomes. For example, oral questions can assess cognitive learning,...

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Related Experiment Video

Updated: Jan 13, 2026

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

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

Published on: December 6, 2024

A large language model-powered reflective AI agent for evidence-based nursing education: Design and evaluation.

Shuqi Yang¹, Manfei Shi¹, Yuhang Qian²

¹School of Nursing, Fudan University, Shanghai, China.

Nurse Education in Practice

|January 11, 2026

Summary

A new Evidence-Based Nursing Expert (EBN-Expert) system significantly outperformed general large language models in nursing education assessments. This AI tool enhances reflective learning and supports evidence-based practice training for nursing students.

Keywords:

Agent Artificial intelligence Evidence-Based Practice Large language model Nursing education

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Last Updated: Jan 13, 2026

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

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

Published on: June 13, 2025

Area of Science:

Artificial Intelligence in Education
Nursing Education Technology
Domain-Specific AI

Background:

Evidence-based practice (EBP) is crucial in nursing but challenging for students to master.
Traditional methods struggle to develop critical thinking for EBP.
General large language models (LLMs) offer limited support for EBP's specific needs.

Purpose of the Study:

To develop and evaluate the Evidence-Based Nursing Expert (EBN-Expert), a specialized AI agent.
To support reflective learning in evidence-based nursing education.
To assess EBN-Expert's performance against general LLMs.

Main Methods:

A comparative evaluation study using standardized nursing exam questions.
Developed EBN-Expert based on Evidence-Based Nursing textbook content.
Compared EBN-Expert against ChatGPT-o1, DeepSeek-R1, and Kimi using 124 test items.

Main Results:

EBN-Expert significantly outperformed general LLMs (P < 0.001), achieving the highest score.
EBN-Expert excelled in methodological and interpretive reasoning questions.
Perfect accuracy in true/false and strong performance in multiple-choice questions were noted.

Conclusions:

Domain-specific AI tools like EBN-Expert show promise for nursing education.
EBN-Expert's curriculum alignment and accuracy support EBP training.
The system offers a scalable and trustworthy approach to advancing nursing education.