Transforming Nursing Practice Through Artificial Intelligence: The Effectiveness of Artificial Intelligence-Based Learning in Drug Dose Calculation on Knowledge, Clinical Decisions, and Self-Efficacy
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 56
- 试验地点
- 1
- 主要终点
- Nurses' Knowledge of Drug Calculation
研究概览
简要总结
The purpose of this study is to evaluate how an Artificial Intelligence -assisted learning platform affects nurses' ability to calculate medication dosages accurately. Drug calculation is a critical skill in nursing, and errors can significantly impact patient safety.
While traditional teaching methods are standard, they may not provide the personalized feedback needed for such a high-stakes task. This study compares two groups of nurses: one group using an Artificial Intelligence-driven software that provides interactive scenarios and real-time guidance, and another group receiving traditional classroom instruction.
The researchers aim to determine whether the AI approach leads to:
Improved theoretical knowledge of drug calculations. Enhanced clinical decision-making during medication administration. Increased nurses' confidence (self-efficacy) in performing these tasks in real clinical settings.
In addition, a qualitative component conducted using focus group discussions to explore participants' acceptance, perceived usefulness, usability, and overall perceptions of the AI-assisted learning platform. This qualitative inquiry provides a deeper insight into nurses' experiences, attitudes toward AI integration in education, and their opinions regarding the effectiveness of the teaching and learning strategies used within the platform.
详细描述
Medication administration errors are a significant challenge in nursing practice, particularly in high-acuity environments such as cardiovascular and critical care units. This study evaluates the effectiveness of an Artificial Intelligence-driven educational intervention designed to bridge the gap between theoretical knowledge and clinical application in drug calculations.
Study Design
This study employed a mixed-methods design comprising a quasi-experimental pretest-posttest approach with a control group, complemented by a qualitative focus group component. Participants were allocated to either an experimental group receiving Artificial Intelligence-assisted learning or a control group receiving traditional instruction
The Intervention (ٍStudy Group)
Participants in the experimental group used Artificial Intelligence-assisted learning software designed to enhance their educational experience through several advanced features. The software provides Adaptive Learning Paths, which adjust calculation complexity in accordance with the nurse's performance. Additionally, it offers Real-Time Feedback, ensuring immediate corrections and step-by-step guidance for complex drug dosing. Lastly, the software incorporates Artificial Intelligence-based Clinical Simulations that create high-pressure clinical decision-making scenarios for learners.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Health Services Research
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Nurses working in multiple clinical settings, including medical-surgical, cardiovascular, or critical care units..etc.
- •Nurses are responsible for medication administration and drug dosage calculations as part of their daily clinical duties.
- •Willingness to participate in the Artificial Intelligence-assisted learning program and sign the informed consent.
排除标准
- •Nurses who had recently received specific training in drug-calculation or had any prior exposure to AI-based educational tools (within the last 6 months)
研究组 & 干预措施
Artificial Intelligence-Assisted Learning Group
Use an Artificial Intelligence-assisted platform providing scenario-based learning and real-time feedback for drug calculations.
干预措施: Artificial Intelligence-Assisted Drug Calculation Platform (Device)
Traditional Learning Group
Participants receive the standard curriculum through traditional lectures and paper-based practice sessions.
干预措施: Traditional Nursing Education (Other)
结局指标
主要结局
Nurses' Knowledge of Drug Calculation
时间窗: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
A 16-item assessment tool designed to evaluate the theoretical and practical knowledge of nurses regarding drug calculation principles (e.g., unit conversions, flow rate, and dose calculations). Each correct answer is scored "1" and each incorrect answer is scored "0". Scale Range: The total score ranges from a minimum of 0 to a maximum of 16. Interpretation: Higher scores indicate a better outcome (greater mastery of calculation principles). High (13-16): Competent level (\> 80%). Moderate (10-12): Acceptable but incomplete knowledge (60%-80%). Low (0-9): Deficient understanding (\< 60%).
次要结局
- Nurses' Drug Calculation Decision-Making Scale(Baseline (Pre-test) and 2 weeks post-intervention (Post-test))
- General Self-Efficacy Scale(Baseline (Pre-test) and 2 weeks post-intervention (Post-test))
- Nurses' Perception and Satisfaction with Artificial Intelligence-Assisted Learning (Qualitative)(2 weeks after the completion of the AI-assisted training)
研究者
Mohamed Fakhry Ahmed Salem
Lecturer of Medical-Surgical Nursing
Alexandria University