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

Overview

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.

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: 1. Orientation 2. Pre-briefing 3. AIMR simulation 4. 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.