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Objective <p>This study evaluates the effectiveness of an innovative intelligent continuous care model delivered through a multi-platform digital network during the expansion phase of auricular reconstruction. The aim is to inform the development of future artificial intelligence-based care systems.</p> Methods <p>In a zone-randomized design, patients were divided into experimental and control groups. The experimental group received continuous care through a multi-platform system that included WeChat management, educational manuals, instructional videos and electronic irrigation logs. Real-time monitoring and guidance for complications was also provided. Patient satisfaction and complication data were collected through a questionnaire platform.</p> Results <p>The experimental group demonstrated significantly improved knowledge comprehension and adherence (<i>p</i>&lt;0.05), as well as higher treatment satisfaction and lower incidence of complications compared to the control group (<i>p</i>&lt;0.05).</p> Conclusion <p>The multi-platform intelligent continuous care model enhances patient experience and reduces the risk of complications, highlighting the transformative potential of modern communication technology in healthcare. The successful implementation offers valuable insights into the design of more efficient artificial intelligence care systems, signaling a shift toward the integration of technology into healthcare practice.</p> Level of Evidence II <p> This study provides moderate evidence supporting the effectiveness of a multi-platform intelligent continuous care model during the skin expansion phase of auricular reconstruction. The findings provide valuable insights for the design of future artificial intelligence care systems. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors <a href="http://www.springer.com/00266">www.springer.com/00266</a>.</p>

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Application of an Intelligent Continuous Care Model During Skin Expansion in Auricular Reconstruction

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Yingjie Wang,
  • Yan An,
  • Qingfang Meng,
  • Jinxiu Yang,
  • Huizi Liu,
  • Liya Du,
  • Na Liu

摘要

Objective

This study evaluates the effectiveness of an innovative intelligent continuous care model delivered through a multi-platform digital network during the expansion phase of auricular reconstruction. The aim is to inform the development of future artificial intelligence-based care systems.

Methods

In a zone-randomized design, patients were divided into experimental and control groups. The experimental group received continuous care through a multi-platform system that included WeChat management, educational manuals, instructional videos and electronic irrigation logs. Real-time monitoring and guidance for complications was also provided. Patient satisfaction and complication data were collected through a questionnaire platform.

Results

The experimental group demonstrated significantly improved knowledge comprehension and adherence (p<0.05), as well as higher treatment satisfaction and lower incidence of complications compared to the control group (p<0.05).

Conclusion

The multi-platform intelligent continuous care model enhances patient experience and reduces the risk of complications, highlighting the transformative potential of modern communication technology in healthcare. The successful implementation offers valuable insights into the design of more efficient artificial intelligence care systems, signaling a shift toward the integration of technology into healthcare practice.

Level of Evidence II

This study provides moderate evidence supporting the effectiveness of a multi-platform intelligent continuous care model during the skin expansion phase of auricular reconstruction. The findings provide valuable insights for the design of future artificial intelligence care systems. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.