Effectiveness of Artificial Intelligence Integrated Mixed Reality-based High-Alert Medications Management Simulation Program

ConditionHealhty
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
AgeNot listed
SponsorChonnam National University Hospital

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

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.

Disqualifiers

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 design

Design model

Parallel

Treatments tested in this trial

  • Artificial Intelligence integrated Mixed Reality-based Simulation Program

    Other intervention

    Participants in the intervention arm receive Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program

  • Standard medication-management education

    Other intervention

    Participants receive the hospital's standard medication-management education (didactic lectures, case discussions, and workshops)

Treatment groups

60 Participants
are divided into 2 treatment groups
Group A: Arm 1Experimental treatment 1 intervention
Group B: Arm 2Active comparator 1 intervention

Trial outcomes

Primary outcomes

1

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.

Time frame
Baseline, immediately post-intervention

Secondary outcomes

1

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.

Time frame
Baseline, immediately post-intervention
2

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.

Time frame
Baseline, immediately post-intervention
3

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.

Time frame
Baseline, immediately post-intervention

Other outcomes

Locations

This trial has no locations.

Sponsors and contacts

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