Pressure UlcerNCT07132320
Development and Effectiveness Verification of an AI Application for Pressure Injury Nursing Based on Clinical Judgment Model
A study testing Pressure Ulcer Nursing AI Application Training and Traditional Pressure Ulcer Nursing Education in people with Pressure Ulcer and Nursing Education, run by Yonsei University at 1 site in South Korea.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Pressure Ulcer, Nursing Education
- What is being tested?
- Pressure Ulcer Nursing AI Application Training and Traditional Pressure Ulcer Nursing Education
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 95 participants
- Where
- 1 siteSouth Korea
- Study dates
- 15 Nov 2024to 10 Dec 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Registered nurse working at a tertiary hospital, directly engaged in patient care
- ≥3 years experience (algorithm development phase) or ≥1 year experience (evaluation phase)
- Written informed consent
Who cannot join
- Communication difficulties for interviews (analysis phase)
- Unable to understand education for AI application (evaluation phase)
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the detailed description
"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.
- 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.
- Study Design
This is a prospective, randomized controlled, parallel-group study. Nurses will be allocated to either:
- Experimental arm: AI application-based education
- Control arm: Conventional education without AI support Assessments will be conducted pre-intervention, immediately post-intervention, and at three months post-intervention. Data analysis will compare within- and between-group outcomes.
The development follows the ADDIE instructional design framework:
- Analysis - guideline review and expert interviews
- Design - algorithm development based on the clinical judgment model
- Development - AI mobile application construction and educational content integration
- Implementation - pilot testing in a clinical setting
- Evaluation - randomized controlled trial to assess effectiveness
- Study Phases and Procedures
- Analysis Review five evidence-based guidelines:
- EPUAP/NPIAP/PPPIA 2021 Prevention \& Treatment
- Wound Care 2016 Assessment \& Treatment
- NICE 2014 Prevention \& Treatment
- KCE 2013 Treatment
- Korean Nurses Association Clinical Practice Guidelines (Pressure Injury Nursing) 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:
- Clinical judgment (modified Lasater Clinical Judgment Rubric for pressure injuries, score 11-44)
- Knowledge (PZ-PUKT test; 0-39)
- Satisfaction (Likert 1-5, plus qualitative feedback)
- Incidence of pressure injuries in patients under participants' care (if available) Survey time: ~30 minutes. All assessments repeated immediately post-intervention and at 3 months.
- Conditions
- Pressure Ulcer, Nursing Education
- Being tested
- Pressure Ulcer Nursing AI Application Training, Traditional Pressure Ulcer Nursing Education
- Keywords
- Pressure Ulcer, clinical judgment, artificial intelligence, Nursing, Education
- Medical subject headings
- Pressure Ulcer
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Pressure Ulcer Nursing AI Application TrainingBiological
Clinical nurses receive training and guidance via an AI application designed for pressure ulcer nursing based on a clinical judgment model.
Traditional Pressure Ulcer Nursing EducationBiological
Clinical nurses receive conventional pressure ulcer nursing education not involving the developed AI application.
| Study group | Role | What they receive |
|---|---|---|
| Experimental Group | Experimental | clinical nurses receive training and guidance via an AI application designed for pressure ulcer nursing based on a clinical judgment model. Receives: Biological: Pressure Ulcer Nursing AI Application Training |
| Control Group | Active comparator | Clinical nurses receive conventional pressure ulcer nursing education not involving the developed AI application. Receives: Biological: Traditional Pressure Ulcer Nursing Education |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in Clinical Judgment Competence Score
The difference in clinical judgment score measured by the modified Lasater Clinical Judgment Rubric specific for pressure ulcer management.
Measured: Baseline, Post-intervention (approximately 2 weeks)
Secondary outcomes (3)
Change in Pressure Ulcer Nursing Knowledge Score
Difference in nurses' knowledge scores measured by the PZ-PUKT tool.
Measured: Baseline, Post-intervention (approximately 2 weeks)
Change in Educational Satisfaction Score
Difference in nurses' educational satisfaction scores after intervention.
Measured: Baseline, Post-intervention (approximately 2 weeks)
Pressure Ulcer Incidence
Incidence rate of pressure ulcers among patients during the study period.
Measured: Baseline, Post-intervention (approximately 2 weeks)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Aug 2025
- Study start15 Nov 2024
- Primary completion10 Dec 2024
- Study completion10 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pressure UlcerStudies of the same condition, open and under way.
- More trials run by Yonsei UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 95 participants (actual)
Sponsor and investigators
- Lead sponsor
- Yonsei University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07132320
- Sponsor's protocol ID
- 4-2024-0677
- Status last verified
- Aug 2025
- First submitted
- 12 Aug 2025
- Last updated
- 20 Aug 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Effectiveness Verification of an AI Application for Pressure Injury Nursing Based on Clinical Judgment Model
- ClinicalTrials.gov ID
- NCT07132320
- Acronym / sponsor ID
- 4-2024-0677
- Conditions
- Pressure Ulcer, Nursing Education
- Keywords
- Pressure Ulcer, clinical judgment, artificial intelligence, Nursing, Education
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 95 participants (actual)
- Lead sponsor
- Yonsei UniversityUniversity, hospital or other
- Study start
- 15 Nov 2024 (actual)
- Primary completion
- 10 Dec 2024 (actual)
- Study completion
- 10 Dec 2024 (actual)
- First posted
- 20 Aug 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
"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.
- 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.
- Study Design
This is a prospective, randomized controlled, parallel-group study. Nurses will be allocated to either:
- Experimental arm: AI application-based education
- Control arm: Conventional education without AI support Assessments will be conducted pre-intervention, immediately post-intervention, and at three months post-intervention. Data analysis will compare within- and between-group outcomes.
The development follows the ADDIE instructional design framework:
- Analysis - guideline review and expert interviews
- Design - algorithm development based on the clinical judgment model
- Development - AI mobile application construction and educational content integration
- Implementation - pilot testing in a clinical setting
- Evaluation - randomized controlled trial to assess effectiveness
- Study Phases and Procedures
- Analysis Review five evidence-based guidelines:
- EPUAP/NPIAP/PPPIA 2021 Prevention \& Treatment
- Wound Care 2016 Assessment \& Treatment
- NICE 2014 Prevention \& Treatment
- KCE 2013 Treatment
- Korean Nurses Association Clinical Practice Guidelines (Pressure Injury Nursing) 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:
- Clinical judgment (modified Lasater Clinical Judgment Rubric for pressure injuries, score 11-44)
- Knowledge (PZ-PUKT test; 0-39)
- Satisfaction (Likert 1-5, plus qualitative feedback)
- Incidence of pressure injuries in patients under participants' care (if available) Survey time: ~30 minutes. All assessments repeated immediately post-intervention and at 3 months.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Experimental Group | Experimental | Biological: Pressure Ulcer Nursing AI Application Trainingclinical nurses receive training and guidance via an AI application designed for pressure ulcer nursing based on a clinical judgment model. |
| Control Group | Active comparator | Biological: Traditional Pressure Ulcer Nursing EducationClinical nurses receive conventional pressure ulcer nursing education not involving the developed AI application. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Registered nurse working at a tertiary hospital, directly engaged in patient care
- ≥3 years experience (algorithm development phase) or ≥1 year experience (evaluation phase)
- Written informed consent
Key exclusion criteria (2 listed)
- Communication difficulties for interviews (analysis phase)
- Unable to understand education for AI application (evaluation phase)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Pressure Ulcer Nursing AI Application TrainingClinical nurses receive training and guidance via an AI application designed for pressure ulcer nursing based on a clinical judgment model. | Biological | Experimental Group |
| Traditional Pressure Ulcer Nursing EducationClinical nurses receive conventional pressure ulcer nursing education not involving the developed AI application. | Biological | Control Group |
Primary outcome
Change in Clinical Judgment Competence Score
Time frame: Baseline, Post-intervention (approximately 2 weeks)
The difference in clinical judgment score measured by the modified Lasater Clinical Judgment Rubric specific for pressure ulcer management.
3 secondary outcomes
Change in Pressure Ulcer Nursing Knowledge Score
Time frame: Baseline, Post-intervention (approximately 2 weeks)
Change in Educational Satisfaction Score
Time frame: Baseline, Post-intervention (approximately 2 weeks)
Pressure Ulcer Incidence
Time frame: Baseline, Post-intervention (approximately 2 weeks)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Aug 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Pressure Ulcer Nursing AI Application Training and Traditional Pressure Ulcer Nursing Education in people with Pressure Ulcer and Nursing Education, run by Yonsei University at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in Clinical Judgment Competence Score (time frame: Baseline, Post-intervention (approximately 2 weeks)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Aug 2025.