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Related Experiment Video

Updated: Jan 9, 2026

Development of a Benchtop Model for Evaluating the Compatibility of Wound Dressing Materials with Negative Pressure Wound Therapy Systems
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An Expert Knowledge Algorithm and Model Predicting Wound Healing Trends for a Decision Support System for Pressure

Aya Kitamura1, Aruto Ando2, Gojiro Nakagami2

  • 1Department of Gerontological Nursing, Ishikawa Prefectural Nursing University, Ishikawa, Japan.

JMIR Nursing
|December 9, 2025
PubMed
Summary

Home-visiting nurses can improve pressure injury (PI) management with a new expert-informed algorithm and predictive model. These tools aim to enhance decision-making for better patient outcomes in home care settings.

Keywords:
algorithmcare recommendationclinical decision support systemhome carepressure injury

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Area of Science:

  • Nursing Informatics
  • Wound Care Management
  • Clinical Decision Support Systems

Background:

  • Home-visiting nurses face challenges in selecting optimal pressure injury (PI) management strategies, even with existing clinical guidelines.
  • Clinical decision support systems (CDSS) offer a potential solution to aid nurses' decision-making processes in home-visiting settings.

Purpose of the Study:

  • To develop a wound care algorithm incorporating expert nurse knowledge.
  • To create a predictive model for pressure injury severity changes to support home-visiting nurses in real-time consultations.

Main Methods:

  • Modified an existing algorithm through semistructured interviews with a certified wound expert nurse.
  • Developed a hierarchical Bayesian model to estimate predictive distributions for changes in PI severity (DESIGN-R 2020 score).
  • Assessed algorithm recommendations for applicability and improvement, and evaluated model prediction intervals using training and test datasets.

Main Results:

  • The developed high-expertise algorithm demonstrated high agreement proportions (0.89-0.92) and improvement proportions (0.25-0.76) across assessment rounds.
  • Expected healing levels indicated potential for significant case improvement (2.67-3.25 on a 4-point scale).
  • The predictive model achieved coverage probabilities of 90% prediction intervals ranging from 0.67 to 0.86 in test data.

Conclusions:

  • Successfully developed an expert-informed algorithm and a predictive model for PI severity changes.
  • These tools are foundational for creating clinically applicable CDSS for home-visiting nurses.
  • The developed systems aim to facilitate appropriate pressure injury management in home-care environments.