HealhtyAIMR-HAMNCT07390461
Effectiveness of Artificial Intelligence Integrated Mixed Reality-based High-Alert Medications Management Simulation Program
A study testing Artificial Intelligence integrated Mixed Reality-based Simulation Program and Standard medication-management education in people with Healhty, run by Chonnam National University Hospital.
Trial summary
The goal of this clinical trial is to learn if a Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program (AIMR-HAM) helps hospital nurses manage high-alert medicines (HAMs) more safely. MR mixes real and virtual elements to let nurses practice in realistic scenarios. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Healhty
- What is being tested?
- Artificial Intelligence integrated Mixed Reality-based Simulation Program and Standard medication-management education
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 60 participants
- Where
- No sites listed yet
- Study dates
- 4 Mar 2026to 6 Mar 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Nurses with at least one to six years of clinical experience.
- Those who understand the purpose and procedures of this study and have given written consent to participate.
- Those who have no physical or cognitive limitations in using mixed reality devices.
- ④ Those who are able to communicate in Korean and understand and respond to questions.
Who cannot join
- Those who do not wish to participate in the study. ② Those who have participated in education related to high-alert medications within the past six months.
- Those who are unable or have difficulty participating in the mixed reality education program due to visual, hearing, or neurological impairments, or adverse effects such as dizziness or motion sickness.
- Those who voluntarily withdraw from the study midway through.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
The goal of this clinical trial is to learn if a Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program (AIMR-HAM) helps hospital nurses manage high-alert medicines (HAMs) more safely. MR mixes real and virtual elements to let nurses practice in realistic scenarios.
The main questions are:
Read more
Does the AIMR-HAM improve nurses' medication safety skills? Does the AIMR-HAM lower medication errors and improve clinical performance?
Researchers will compare two groups to answer these questions:
Intervention group: AIMR-HAM Control group: standard education only
Who can take part:
Nurses who work at large hospitals and have 1 to 6 years of clinical experience.
About 60 nurses will join the study.
What participants will do:
Attend the assigned training (AIMR-HAM or standard education only). Complete short tests and surveys before and after training to measure skills, communication, and clinical reasoning.
Report any medication errors that occur during the study. Why this matters: The study will show whether AIMR-HAM training can improve how nurses handle HAMs and make patient care safer.
Read the detailed description
This trial evaluates whether a Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program (AIMR-HAM) improves high-alert medications (HAMs) management skills, clinical performance (simulation-based observed assessments), communication, and medication error rates among hospital nurses with 1-6 years of clinical experience. The trial aims to assess the effectiveness of AIMR-HAM in enhancing practical competency and patient safety in real-world clinical practice.
Study design and randomization The study is a single-center, parallel-group, randomized controlled trial with approximately 60 participating nurses. Participants are randomly assigned 1:1 to the intervention or control group using a computer-generated randomization schedule (e.g., block randomization). Randomization is performed by an independent data manager to maintain allocation concealment. Outcome assessors are blinded to group assignment (assessor-blinded).
Intervention - AIMR-HAM (intervention group)
Overview: The intervention combines MR headset-based, scenario-driven hands-on practice.
Key components:
- Orientation
- Pre-briefing
- AIMR simulation
- Debriefing
Technical note: The MR setup integrates virtual elements with the physical environment to deliver visual and auditory cues; device make/model and software version can be specified in the protocol field.
Control group: The control group receives the hospital's standard medication management education (lectures, case discussions, and workshops). The schedule and total contact time are matched to the intervention group to control for time and attention.
Participant procedures and timeline The study flow is: screening → written informed consent → baseline (pre-intervention) assessment → randomization → assigned training (MR + standard education or standard education only) → immediate post-training assessment → follow-up assessment (for example, 1 month post-training) → study completion. At each assessment point, participants complete the same set of evaluations to allow comparison over time.
Data collection methods
: Clinical performance and skills are measured using standardized performance checklists and observer rating scales during simulated assessments. Trained evaluators, blinded to allocation, conduct these assessments.
Self-report instruments (confidence, communication, cognitive workload) are collected via electronic or paper questionnaires and entered into the study database.
Medication error data are collected from the hospital incident reporting system and participant self-reports during the study period; duplicate reports are reconciled through data review and classification.
The MR system automatically records participant interaction logs (timestamps, actions, decision points) which can be used as supplementary analytic data.
Analysis overview
Primary analyses follow the intention-to-treat principle. Continuous outcomes (e.g., competency scores) are compared using t-tests or linear regression; repeated measures are analyzed with mixed-effects models to account for within-participant correlations.
Binary outcomes (e.g., presence of medication error) are analyzed using logistic regression or risk estimates.
Pre-specified covariates (for example, baseline scores or clinical unit) are included in adjusted analyses as needed. Sensitivity analyses address missing data (e.g., multiple imputation) and protocol deviations.
Safety and ethics The study has received institutional review board (IRB) approval. All participants provide written informed consent before enrollment. Any discomfort related to MR use (such as dizziness or nausea) is monitored and managed per protocol; serious adverse events are reported to the IRB and hospital oversight bodies promptly. Personal data are de-identified or encrypted and stored with restricted access.
Quality assurance and reliability measures
Instructors and evaluators receive standardized training to ensure consistent delivery of the intervention and scoring of outcomes.
Randomization, data entry, and data handling follow standard operating procedures. Data undergo regular monitoring and periodic quality checks; independent data quality audits may be performed if indicated.
Conclusion and potential impact This trial will determine whether AIMR-HAM leads to measurable improvements in nurses' medication management competency and reduces medication errors compared with standard education. Findings may inform hospital education practices and support wider adoption of AIMR-HAM for clinical skill training.
- Conditions
- Healhty
- Being tested
- Artificial Intelligence integrated Mixed Reality-based Simulation Program, Standard medication-management education
- Keywords
- High-alert medications, nurse, mixed reality, artificial intelligence, medication safety
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence integrated Mixed Reality-based Simulation ProgramOther
Participants in the intervention arm receive Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program
Standard medication-management educationOther
Participants receive the hospital's standard medication-management education (didactic lectures, case discussions, and workshops)
| Study group | Role | What they receive |
|---|---|---|
| Arm 1 | Experimental | Participants receive a Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program Receives: Other: Artificial Intelligence integrated Mixed Reality-based Simulation Program |
| Arm 2 | Active comparator | Participants receive the hospital's standard medication-management education Receives: Other: Standard medication-management education |
What is being measured
The primary outcome is the main question the study is designed to answer.
High-Alert Medication clinical performance
The high-alert medication clinical performance competency assessment will utilize a questionnaire developed by our research team. The tool consists of 23 questions, of which 4 will vary depending on the simulation progress, resulting in a final assessment measuring 19 questions. Each item will be graded by the instructor on a scale of 0 for failure or incorrect execution, 1 for insufficient or inadequate execution, and 2 for accurate execution. The developed items will undergo a validity assessment by six experts before being revised and supplemented for use. Higher scores indicate greater clinical performance in high-alert medication.
Measured: Baseline, immediately post-intervention
Secondary outcomes (3)
Global Interpersonal Communication Competence
communication competence will be measured using the Global Interpersonal Communication Competence (GICC) scale developed by Heo Kyung-ho (2003), which was modified and supplemented by Lee Hyun-sook and Kim Jong-gyeong (2010). It consists of 15 items, each rated on a 5-point Likert scale ranging from 1 (not at all) to 5 (very much), with higher scores indicating greater communication competence. In the study by Lee Hyun-sook and Kim Jong-gyeong (2010), the Cronbach's ⍺ was .83.
Measured: Baseline, immediately post-intervention
Clinical Reasoning Competency
Clinical reasoning competency will be measured using the Clinical Reasoning Competency Scale (CRCS) developed by Bae et al. (2023). It consists of 22 items, each rated on a 5-point Likert scale ranging from 1 (not at all) to 5 (very much). A higher score indicates greater clinical reasoning competency. In the study by Bae et al. (2023), Cronbach's ⍺ was .92.
Measured: Baseline, immediately post-intervention
Medication Safety Competence
Medication safety competency will be measured using the Medication Safety Competence Scale (MSCS) developed by Park and Seomun (2021). It consists of 36 items and six subscales (patient-centered medication management, problem situation improvement, managing influencing factors, managing crisis situations, interdisciplinary collaboration, and responsibility as a nursing professional). Each item is rated on a 5-point Likert scale (1 = "not at all" to 5 = "very much"), with a total score of 36 to a maximum of 180. A higher total score indicates a higher level of medication safety competency. The reliability of the MSCS was Cronbach's α = .96.
Measured: Baseline, immediately post-intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Feb 2026
- Study start4 Mar 2026 (estimated)
- Primary completion4 Mar 2026 (estimated)
- Study completion6 Mar 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for HealhtyStudies of the same condition, open and under way.
- 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
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind
- Who is masked
- Participant, Investigator, Outcomes assessor
- Enrolment
- 60 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chonnam National University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jinkyung Park, Chonnam National University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07390461
- Sponsor's protocol ID
- CNUH-2025-435
- Other IDs
- RS-2024-00345750
- Acronym
- AIMR-HAM
- Status last verified
- Jan 2026
- First submitted
- 28 Jan 2026
- Last updated
- 5 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Bae, J., Lee, J., Choi, M., Jang, Y., Park, C. G., & Lee, Y. J. (2023). Development of the clinical reasoning competency scale for nurses. BMC nursing, 22(1), 138. Elendu, C., Amaechi, D. C., Okatta, A. U., Amaechi, E. C., Elendu, T. C., Ezeh, C. P., & Elendu, I. D. (2024). The impact of simulation-based training in medical education: A review. Medicine, 103(27), e38813. Fernández-Alcántara, M., Escribano, S., Juliá-Sanchis, R., Castillo-López, A., Pérez-Manzano, A., Macur, M., Kalender-Smajlović, S., García-Sanjuán, S., & Cabañero-Martínez, M. J. (2025). Virtual Simulation Tools for Communication Skills Training in Health Care Professionals: Literature Review. JMIR Medical Education, 11(1), e63082. Frost, J., Delaney, L., & Fitzgerald, R. (2020). Exploring the application of mixed reality in nurse education. BMJ Simulation & Technology Enhanced Learning, 6(4), 214. Fu, Y., Hu, Y., & Sundstedt, V. (2022). A systematic literature review of virtual, augmented, and mixed reality game applications in healthcare. ACM Transactions on Computing for Healthcare (HEALTH), 3(2), 1-27. Gaffney, T. A., Hatcher, B. J., & Milligan, R. (2016). Nurses' role in medical error recovery: an integrative review. Journal of clinical nursing, 25(7-8), 906-917. Han, Y., Chen, J., Xu, Y., Huang, P., & Hou, L. (2024). Nurse-led medication management as a critical component of transitional care for preventing drug-related problems. Aging Clinical and Experimental Research, 36(1), 151. Hodkinson, A., Tyler, N., Ashcroft, D. M., Keers, R. N., Khan, K., Phipps, D., Abuzour, A., Bower, P., Avery, A., & Campbell, S. (2020). Preventable medication harm across health care settings: a systematic review and meta-analysis. BMC medicine, 18(1), 313. Jeon, H. G., & Jeong, H. W. (2025). Effectiveness of a Mixed Reality Simulation Program for Dyspnoea Care on New Nurses' Clinical Competency: A Mixed-Methods Study. Nurse Education in Practice, 104397. Mardani, A., Griffiths, P., & Vaismoradi, M. (202
Study at a glance
From the registry record, last updated by the sponsor on 5 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effectiveness of Artificial Intelligence Integrated Mixed Reality-based High-Alert Medications Management Simulation Program
- ClinicalTrials.gov ID
- NCT07390461
- Acronym / sponsor ID
- AIMR-HAM / CNUH-2025-435
- Conditions
- Healhty
- Keywords
- High-alert medications, nurse, mixed reality, artificial intelligence, medication safety
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- Chonnam National University HospitalUniversity, hospital or other
- Study start
- 4 Mar 2026 (estimated)
- Primary completion
- 4 Mar 2026 (estimated)
- Study completion
- 6 Mar 2026 (estimated)
- First posted
- 5 Feb 2026 (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.
The goal of this clinical trial is to learn if a Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program (AIMR-HAM) helps hospital nurses manage high-alert medicines (HAMs) more safely. MR mixes real and virtual elements to let nurses practice in realistic scenarios.
The main questions are:
Does the AIMR-HAM improve nurses' medication safety skills? Does the AIMR-HAM lower medication errors and improve clinical performance?
Researchers will compare two groups to answer these questions:
Intervention group: AIMR-HAM Control group: standard education only
Who can take part:
Nurses who work at large hospitals and have 1 to 6 years of clinical experience.
About 60 nurses will join the study.
What participants will do:
Attend the assigned training (AIMR-HAM or standard education only). Complete short tests and surveys before and after training to measure skills, communication, and clinical reasoning.
Report any medication errors that occur during the study. Why this matters: The study will show whether AIMR-HAM training can improve how nurses handle HAMs and make patient care safer.
Detailed description
This trial evaluates whether a Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program (AIMR-HAM) improves high-alert medications (HAMs) management skills, clinical performance (simulation-based observed assessments), communication, and medication error rates among hospital nurses with 1-6 years of clinical experience. The trial aims to assess the effectiveness of AIMR-HAM in enhancing practical competency and patient safety in real-world clinical practice.
Study design and randomization The study is a single-center, parallel-group, randomized controlled trial with approximately 60 participating nurses. Participants are randomly assigned 1:1 to the intervention or control group using a computer-generated randomization schedule (e.g., block randomization). Randomization is performed by an independent data manager to maintain allocation concealment. Outcome assessors are blinded to group assignment (assessor-blinded).
Intervention - AIMR-HAM (intervention group)
Overview: The intervention combines MR headset-based, scenario-driven hands-on practice.
Key components:
- Orientation
- Pre-briefing
- AIMR simulation
- Debriefing
Technical note: The MR setup integrates virtual elements with the physical environment to deliver visual and auditory cues; device make/model and software version can be specified in the protocol field.
Control group: The control group receives the hospital's standard medication management education (lectures, case discussions, and workshops). The schedule and total contact time are matched to the intervention group to control for time and attention.
Participant procedures and timeline The study flow is: screening → written informed consent → baseline (pre-intervention) assessment → randomization → assigned training (MR + standard education or standard education only) → immediate post-training assessment → follow-up assessment (for example, 1 month post-training) → study completion. At each assessment point, participants complete the same set of evaluations to allow comparison over time.
Data collection methods
: Clinical performance and skills are measured using standardized performance checklists and observer rating scales during simulated assessments. Trained evaluators, blinded to allocation, conduct these assessments.
Self-report instruments (confidence, communication, cognitive workload) are collected via electronic or paper questionnaires and entered into the study database.
Medication error data are collected from the hospital incident reporting system and participant self-reports during the study period; duplicate reports are reconciled through data review and classification.
The MR system automatically records participant interaction logs (timestamps, actions, decision points) which can be used as supplementary analytic data.
Analysis overview
Primary analyses follow the intention-to-treat principle. Continuous outcomes (e.g., competency scores) are compared using t-tests or linear regression; repeated measures are analyzed with mixed-effects models to account for within-participant correlations.
Binary outcomes (e.g., presence of medication error) are analyzed using logistic regression or risk estimates.
Pre-specified covariates (for example, baseline scores or clinical unit) are included in adjusted analyses as needed. Sensitivity analyses address missing data (e.g., multiple imputation) and protocol deviations.
Safety and ethics The study has received institutional review board (IRB) approval. All participants provide written informed consent before enrollment. Any discomfort related to MR use (such as dizziness or nausea) is monitored and managed per protocol; serious adverse events are reported to the IRB and hospital oversight bodies promptly. Personal data are de-identified or encrypted and stored with restricted access.
Quality assurance and reliability measures
Instructors and evaluators receive standardized training to ensure consistent delivery of the intervention and scoring of outcomes.
Randomization, data entry, and data handling follow standard operating procedures. Data undergo regular monitoring and periodic quality checks; independent data quality audits may be performed if indicated.
Conclusion and potential impact This trial will determine whether AIMR-HAM leads to measurable improvements in nurses' medication management competency and reduces medication errors compared with standard education. Findings may inform hospital education practices and support wider adoption of AIMR-HAM for clinical skill training.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, investigator, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Arm 1 | Experimental | Other: Artificial Intelligence integrated Mixed Reality-based Simulation ProgramParticipants receive a Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program |
| Arm 2 | Active comparator | Other: Standard medication-management educationParticipants receive the hospital's standard medication-management education |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (4 listed)
- Nurses with at least one to six years of clinical experience.
- ④ Those who are able to communicate in Korean and understand and respond to questions.
Key exclusion criteria (3 listed)
- Those who do not wish to participate in the study. ② Those who have participated in education related to high-alert medications within the past six months.
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 |
|---|---|---|
| Artificial Intelligence integrated Mixed Reality-based Simulation ProgramParticipants in the intervention arm receive Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program | Other | Arm 1 |
| Standard medication-management educationParticipants receive the hospital's standard medication-management education (didactic lectures, case discussions, and workshops) | Other | Arm 2 |
Primary outcome
High-Alert Medication clinical performance
Time frame: Baseline, immediately post-intervention
The high-alert medication clinical performance competency assessment will utilize a questionnaire developed by our research team. The tool consists of 23 questions, of which 4 will vary depending on the simulation progress, resulting in a final assessment measuring 19 questions. Each item will be graded by the instructor on a scale of 0 for failure or incorrect execution, 1 for insufficient or inadequate execution, and 2 for accurate execution. The developed items will undergo a validity assessment by six experts before being revised and supplemented for use. Higher scores indicate greater clinical performance in high-alert medication.
3 secondary outcomes
Global Interpersonal Communication Competence
Time frame: Baseline, immediately post-intervention
Clinical Reasoning Competency
Time frame: Baseline, immediately post-intervention
Medication Safety Competence
Time frame: Baseline, immediately post-intervention
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Feb 2026.
- Results publicationBae, J., Lee, J., Choi, M., Jang, Y., Park, C. G., & Lee, Y. J. (2023). Development of the clinical reasoning competency scale for nurses. BMC nursing, 22(1), 138. Elendu, C., Amaechi, D. C., Okatta, A. U., Amaechi, E. C., Elendu, T. C., Ezeh, C. P., & Elendu, I. D. (2024). The impact of simulation-based training in medical education: A review. Medicine, 103(27), e38813. Fernández-Alcántara, M., Escribano, S., Juliá-Sanchis, R., Castillo-López, A., Pérez-Manzano, A., Macur, M., Kalender-Smajlović, S., García-Sanjuán, S., & Cabañero-Martínez, M. J. (2025). Virtual Simulation Tools for Communication Skills Training in Health Care Professionals: Literature Review. JMIR Medical Education, 11(1), e63082. Frost, J., Delaney, L., & Fitzgerald, R. (2020). Exploring the application of mixed reality in nurse education. BMJ Simulation & Technology Enhanced Learning, 6(4), 214. Fu, Y., Hu, Y., & Sundstedt, V. (2022). A systematic literature review of virtual, augmented, and mixed reality game applications in healthcare. ACM Transactions on Computing for Healthcare (HEALTH), 3(2), 1-27. Gaffney, T. A., Hatcher, B. J., & Milligan, R. (2016). Nurses' role in medical error recovery: an integrative review. Journal of clinical nursing, 25(7-8), 906-917. Han, Y., Chen, J., Xu, Y., Huang, P., & Hou, L. (2024). Nurse-led medication management as a critical component of transitional care for preventing drug-related problems. Aging Clinical and Experimental Research, 36(1), 151. Hodkinson, A., Tyler, N., Ashcroft, D. M., Keers, R. N., Khan, K., Phipps, D., Abuzour, A., Bower, P., Avery, A., & Campbell, S. (2020). Preventable medication harm across health care settings: a systematic review and meta-analysis. BMC medicine, 18(1), 313. Jeon, H. G., & Jeong, H. W. (2025). Effectiveness of a Mixed Reality Simulation Program for Dyspnoea Care on New Nurses' Clinical Competency: A Mixed-Methods Study. Nurse Education in Practice, 104397. Mardani, A., Griffiths, P., & Vaismoradi, M. (202
- Individual participant dataSharing planned: Undecided
Related trials for Healhty
Recruiting
Insulin-Mediated Glucose Uptake and Organ Perfusion Assessed by Total-Body PET During GIP and GLP-1 Infusion
Type 2 Diabetes, Healhty
Active, not recruitingPhase 1
Evaluate the Pharmacokinetics, Pharmacodynamics, Safety and Tolerability of BGM1812 Injection Following Single and Multiple Subcutaneous Administration in Normal to Overweight or Obese But Otherwise Healthy Men and Women
Healhty, Overweight or Obesity
RecruitingObservational study
Effect of Chronotype on Balance and Cognitive Function
Healhty
Recruiting
Oromucosal Oxytocin and Vasopressin Modulation of Emotional Face Attention
Healhty
Recruiting
Chewing, Eating Rate, and Weight Status in Children
Healhty
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.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence integrated Mixed Reality-based Simulation Program and Standard medication-management education in people with Healhty, run by Chonnam National University Hospital.
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 4 inclusion and 3 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: opening soon, as of the sponsor's last update on 5 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Hwigon Jeon, Ph.D. student (+82-10-5121-0700, tjdans779@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
High-Alert Medication clinical performance (time frame: Baseline, immediately post-intervention). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07390461, which also lists 1 linked publication.
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 5 Feb 2026; AzTrials last refreshed its copy on 30 Oct 2026.