Development and Effectiveness Verification of an AI Application for Pressure Injury Nursing Based on Clinical Judgment Model
Development and Effectiveness Verification of an AI Application for Pressure Injury Nursing Based on Clinical Judgment Model
This clinical trial aims to develop a pressure ulcer nursing AI mobile application based on a clinical judgment model and verify its effectiveness among clinical nurses. The study consists of algorithm development, application design, implementation, and a comparative evaluation of nurses using the AI app versus a control group not given the application. Outcomes include clinical judgment, knowledge in pressure ulcer nursing, educational satisfaction, and pressure ulcer incidence. The investigators expect the AI-driven intervention to improve practical nursing skills, reduce pressure ulcer occurrence, and contribute to patient-centered care in hospital settings.
"1.Background and Rationale Pressure injuries, also referred to as pressure ulcers, are a global healthcare issue, particularly severe among elderly and chronically ill patients. The prevalence is rising due to an aging population, prolonged hospital stays, and chronic disease incidence. Conventional prevention and treatment are often based on subjective nursing judgment and personal clinical experience, which limits consistency and efficiency across care settings.
Recent advancements in artificial intelligence (AI) enable integration of evidence-based decision-making tools into clinical workflows. Existing AI solutions for wound care tend to be rule-based and expert-driven, which constrains their adaptability in real clinical environments.
The clinical judgment model offers a systematic decision-making pathway: observing, interpreting, responding, and reflecting. Integrating this model into an AI mobile application for pressure injury nursing can provide consistent, evidence-based, and context-responsive guidance to nurses, improving patient safety and quality of care. This prospective study aims to develop such an AI-based application and evaluate its clinical educational effectiveness among hospital nurses.
2. Study Objectives
Primary objective:
To evaluate the effect of an AI nursing application, based on the clinical judgment model, on nurses' clinical judgment competencies in pressure injury care.
Secondary objectives:
To measure changes in nurses' knowledge of pressure injury nursing. To assess satisfaction with the AI-based education program. To monitor any change in pressure injury incidence in patients under the care of participating nurses.
3. Study Design
This is a prospective, randomized controlled, parallel-group study. Nurses will be allocated to either:
The development follows the ADDIE instructional design framework:
4. Study Phases and Procedures
Analysis
Review five evidence-based guidelines:
Compare recommendations on prevention, staging, diagnosis, risk assessment, and treatment.
Conduct in-depth, semi-structured interviews (~1 hour each) with 15 wound/ostomy continence nurses with ≥3 years' experience. Topics include risk factors, clinical decision criteria, education improvement needs, and expectations for AI.
Design Develop a pressure injury nursing algorithm aligned to the clinical judgment model (notice → interpret → respond → reflect).
Include stages: prevention, diagnosis/staging, treatment selection, evaluation. Ensure flexibility for varied patient conditions and institutional settings. Validate through expert panel review.
Development Collaborate with software engineers to build a mobile AI app with: risk prediction, wound image staging, educational resources, and decision-support prompts.
Design an intuitive, efficient UI/UX. Embed visual wound classification tools and context-specific alerts.
Implementation Pilot the application with experienced wound care nurses. Identify usability or algorithmic issues, revise prior to trial.
Evaluation Participants: 80 nurses (≥1-year clinical experience), randomized into experimental and control groups (40 each, allowing for dropouts).
Intervention group: 2-hour AI-assisted education plus application practice. Control group: Same duration of lecture-based standard education without AI.
Assessments:
Inclusion Criteria:
Exclusion Criteria: