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Objectives <p>The nursing management of adult urology patients in day surgical settings has undergone rapid development. This study aimed to (1) retrieve, evaluate, and summarize the best available evidence regarding the perioperative nursing management in adult patients undergoing urological day surgery, and (2) develop a functional prototype of an evidence-based nursing management mobile application (APP) using an artificial intelligence (AI)-assisted pathway.</p> Methods <p>This research was conducted in two phases. First, in April 2025, a best evidence summary was conducted across many databases utilizing the PIPOST tool. Clinical guidelines, systematic reviews, expert consensus documents, clinical decision tools, and evidence summaries were included and screened. Formal quality appraisal was conducted for clinical guidelines, systematic reviews, and expert consensus documents using appropriate appraisal tools. Second, an AI-augmented platform called Manus AI was used to create an app prototype with the best evidence summary. This platform made it possible to design the architecture, organize the material, and create user interface mock-ups.</p> Results <p>The best evidence summary included 21 studies that were thematically classified into eight nursing management domains: nursing organization management, pre-hospital nursing management, preoperative nursing management, intraoperative nursing management, postoperative nursing management, discharge management, nursing follow-up and nursing quality management. This framework was successfully used to create a completely structured app prototype. The eight domains are reflected in the app prototype. Preliminary feedback from 12 clinical nurses indicated that the prototype was clinically meaningful and may support standardized perioperative nursing management in adult urology day surgery.</p> Conclusion <p>This study summarizes the current best evidence and demonstrates a novel AI-empowered pathway for translating evidence into a practical digital tool. The developed app prototype offers a promising foundation for standardizing nursing care in urological day surgery. Future studies are needed to determine whether the app prototype could improve protocol adherence and patient outcomes.</p> Clinical trial number <p>Not applicable.</p>

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An evidence-based nursing management app prototype for adult urology day surgery: an AI-driven development pathway

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

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  • Qinghong Fang,
  • Yan Zhao,
  • Meiyu Lai,
  • Guangqian Hu,
  • Muli Li,
  • Xiaojun Zhou,
  • Yunyan Rao,
  • Xiaoyin Ma

摘要

Objectives

The nursing management of adult urology patients in day surgical settings has undergone rapid development. This study aimed to (1) retrieve, evaluate, and summarize the best available evidence regarding the perioperative nursing management in adult patients undergoing urological day surgery, and (2) develop a functional prototype of an evidence-based nursing management mobile application (APP) using an artificial intelligence (AI)-assisted pathway.

Methods

This research was conducted in two phases. First, in April 2025, a best evidence summary was conducted across many databases utilizing the PIPOST tool. Clinical guidelines, systematic reviews, expert consensus documents, clinical decision tools, and evidence summaries were included and screened. Formal quality appraisal was conducted for clinical guidelines, systematic reviews, and expert consensus documents using appropriate appraisal tools. Second, an AI-augmented platform called Manus AI was used to create an app prototype with the best evidence summary. This platform made it possible to design the architecture, organize the material, and create user interface mock-ups.

Results

The best evidence summary included 21 studies that were thematically classified into eight nursing management domains: nursing organization management, pre-hospital nursing management, preoperative nursing management, intraoperative nursing management, postoperative nursing management, discharge management, nursing follow-up and nursing quality management. This framework was successfully used to create a completely structured app prototype. The eight domains are reflected in the app prototype. Preliminary feedback from 12 clinical nurses indicated that the prototype was clinically meaningful and may support standardized perioperative nursing management in adult urology day surgery.

Conclusion

This study summarizes the current best evidence and demonstrates a novel AI-empowered pathway for translating evidence into a practical digital tool. The developed app prototype offers a promising foundation for standardizing nursing care in urological day surgery. Future studies are needed to determine whether the app prototype could improve protocol adherence and patient outcomes.

Clinical trial number

Not applicable.