Applied clinical informatics · 2026
Development and Application of a Nurse-Led Clinical Decision Support System for Safe Intravenous Medication Administration: A Nonrandomized Controlled Trial.
СтатьяКлиническое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhang Fuling, Yun Wenjuan, Qin Lili
Аннотация
BACKGROUND: Intravenous medication administration is a high-risk clinical procedure, where medication errors can lead to adverse consequences. Evidence-based clinical practice guidelines provide recommendations for the administration and monitoring of intravenous infusions. These guidelines are being increasingly integrated into clinical decision support systems (CDSS). The development of CDSS should emphasize nurses as core users, closely align with their clinical workflows, and ultimately create practical, user-friendly tools through thoughtful interface design, functional logic, and intelligent alert mechanisms.
OBJECTIVES: We aimed to design and develop a clinical decision support tool based on the Data-Information-Knowledge-Wisdom model, which minimizes infusion errors by providing real-time alerts and standardizing workflows.
METHODS: A nonrandomized trial (May-July 2024) in a tertiary hospital compared traditional practices (n = 1,204) with a CDSS (n = 1,207) using 300 clinical rules and a personal digital assistant interface. Outcomes included error rates, severity, nurse satisfaction, and efficiency.
RESULTS: The CDSS reduced errors by 56.8% (16.69-7.21%, p < 0.001), eliminated severe errors (Level 3-4), improved nurse satisfaction (mean: 69.1/85 on a 17-85 scale), and reduced prescription processing time by 41%.
CONCLUSION: This nurse-led CDSS enhances infusion safety and efficiency, offering a scalable solution. Artificial intelligence-driven predictive error detection could further optimize outcomes.
OBJECTIVES: We aimed to design and develop a clinical decision support tool based on the Data-Information-Knowledge-Wisdom model, which minimizes infusion errors by providing real-time alerts and standardizing workflows.
METHODS: A nonrandomized trial (May-July 2024) in a tertiary hospital compared traditional practices (n = 1,204) with a CDSS (n = 1,207) using 300 clinical rules and a personal digital assistant interface. Outcomes included error rates, severity, nurse satisfaction, and efficiency.
RESULTS: The CDSS reduced errors by 56.8% (16.69-7.21%, p < 0.001), eliminated severe errors (Level 3-4), improved nurse satisfaction (mean: 69.1/85 on a 17-85 scale), and reduced prescription processing time by 41%.
CONCLUSION: This nurse-led CDSS enhances infusion safety and efficiency, offering a scalable solution. Artificial intelligence-driven predictive error detection could further optimize outcomes.