Design of a Mixed-Reality Application to Reduce Pediatric Medication Errors in Prehospital Emergency Care

Abstract

Children in prehospital emergency care are particularly vulnerable to medication errors, often with serious consequences. A prior study analyzing prehospital pediatric medication dosing errors, conducted after the implementation of a statewide pediatric drug-dosing reference for emergency medical services (EMS), identified an alarmingly high error rate. This significant finding led to the current study, which aims to develop technological interventions to reduce the frequency of medication errors for children during treatment by EMS. The current study focuses on the design and development of a safety strategy to automate medication administration using mixed-reality technology. Simulations were conducted to inform the design process, focusing on three scenarios: cardiac arrest, seizure, and burns. The design team included medical and engineering researchers, paramedics, and emergency medical technicians from multiple emergency medical service agencies. Root cause analysis (RCA) and failure mode and effects analysis (FMEA) were conducted after the simulations were completed. The RCA and FMEA were used to identify and prioritize failure points, which were then addressed in a mixed-reality solution using Microsoft HoloLens 2 to automate and enhance pediatric medication administration in prehospital emergency care. The resulting application will provide real-time assistance to guide paramedics through the complicated medication dosing and administration process using a detailed step-by-step guide, aiming to decrease medication errors and improve medication dosing accuracy.

1. Introduction

Medication errors remain the most common type of error in emergency departments. Children are at increased risk of medication errors since doses are not standardized and must be calculated for each child based on their weight. Emergency department (ED) medication error rates range from 4% to 14%, reaching 39% in pediatric cases [1]. In addition, 7.5 million preventable medication errors are estimated to occur in pediatric patients in the United States each year [2]. Some common medication errors in pediatrics include prescription errors, incorrect weight calculations, incorrect dosage, incorrect medication administration, and human errors such as action-based errors or memory-based errors. In 2001, the American Academy of Pediatrics (AAP) recommended the adoption of reporting and learning systems to identify medication errors as a fundamental step to improve the quality and culture of safety in pediatric care. The Institute of Medicine (IOM) also noted that these medication errors are mainly due to unfamiliarity with the unique needs of pediatric patients. There is still a gap in research studies addressing medication errors in pediatric emergency care [1,2].

A prior study analyzing prehospital pediatric medication dosing errors in Michigan, after the implementation of a statewide EMS pediatric drug-dosing reference, identified an alarmingly high error rate of 35.2% [3]. This finding led to the current research, which focuses on developing technological interventions to reduce the frequency of medication errors for children treated by EMS. A medication error is any preventable event that can cause or lead to the inappropriate use of a drug or harm to the patient while the medication is under the control of a healthcare professional, patient, or consumer [4]. Examples of such errors include inappropriate treatment selection, incorrect medication, incorrect dosage, incorrect weight, gaps in the knowledge of the healthcare provider, and unclear or incomplete instructions.

A recent study on hospital medication errors demonstrated that these errors can occur in any of the five stages of the medication process: prescribing, transcribing, dispensing, administering, and monitoring. Among these stages, the prescribing phase exhibits the highest frequency of medication errors [5]. The transcribing phase often involves errors stemming from communication challenges, such as handwriting issues, unclear orders, the substitution of similar-sounding medication names, and interruptions or distractions. During the dispensing stage, errors may occur when the wrong medication or dosage is provided, the medication is not properly labeled, or it is given to the wrong patient. Incorrect drug administration involves administering the wrong drug, providing medication to the wrong patient or through the wrong route, and neglecting to assess patient allergies or contraindications beforehand. Lastly, in the monitoring stage, errors often involve failures to properly track patient responses to medication or detect adverse reactions [5]. In addition, factors such as limited resources, dynamic environments, and the need for rapid decision making further compound the challenges to drug safety for pediatric patients.

Post-COVID-19 has seen a significant increase in the use of artificial intelligence (AI) tools in the healthcare industry, enabling virtual access to programs and image scans for training purposes. For potential clinical and training purposes, the adoption of immersive technologies is expanding into medical domains by providing real-time support during medication administration and improving accuracy in drug dispensing and patient monitoring [6]. Virtual reality (VR) and augmented reality (AR) simulations offer interactive and engaging training modules for healthcare professionals, reducing the likelihood of human errors by providing realistic practice scenarios. Mixed-reality (MR) technology, in particular, is being explored for its potential to offer real-time, hands-free guidance via a head-mounted display, which can be crucial in dynamic and resource-limited prehospital environments. This integration of immersive technologies is paving the way for innovative solutions to mitigate medication errors and improve patient safety. In the following sections, we examine the literature related to medication errors and the current development of preventive measures. We then analyze the data collected from our simulated trials and discuss the findings. Based on these findings, we introduce a design approach employing MR technology to mitigate medication errors within pediatric prehospital emergency care settings.

2. Existing Works

Pediatric emergencies are of great concern due to the increased vulnerability of children to medication errors. Some studies estimate that approximately 14–31% of pediatric medication errors could cause harm or death. The medication delivery process, which includes prescribing, dispensing, and administration, reveals a concerning prevalence of errors, ranging from 5% to 27% in various studies [2]. In particular, the prescribing stage emerges as the stage with the highest number of errors, including dosage miscalculations, weight-based inaccuracies, improper administration, communication lapses, and knowledge-based errors [1].

To mitigate these risks, strategies such as Computerized Provider Order Entry (CPOE), BarCode Medication Administration (BCMA), and targeted awareness initiatives for healthcare providers have shown efficacy in reducing dosing and administration errors [2]. Devarajan et al. introduced a Quality Improvement Project aimed at reducing pediatric emergency prescription errors by standardizing CPOE, improving dosing knowledge, providing error feedback, and increasing awareness of prescription pitfalls. Their research resulted in a significant reduction in prescription error rates from 8.6 to 4.5 per 1000 prescriptions for the top 10 error-prone antibiotics [7]. For example, Rinke et al. observed a significant decrease in prescribing errors, ranging from 36% to 87%, after the implementation of CPOE alongside Clinical Decision Support (CDS) [8]. Elshayib et al. [9] observed a 48% reduction in the likelihood of errors following the implementation of CPOE systems. Although CPOE systems enhance the functionality of electronic health records, they do not fully automate the medication administration process. Consequently, further research is necessary to explore the impact of CPOE on order volume, prescribing patterns, and the nature and frequency of errors. It is also crucial to establish connections between errors, adverse events, and patient harm.

On the other hand, an integrative review by Elshayib et al. [9] of medication errors associated with CPOE highlighted several contributing factors, such as selection errors from drop-down menus, absence of drug-dosing alerts, entering orders in the wrong patient’s file, and system design flaws, and offered recommendations to address these issues. In emergency departments, BCMA has demonstrated efficacy in mitigating medication administration errors. In a study by Bonkowski et al. [10], pre- and post-BCMA implementation medication administration rates were compared, showing a reduction in error rates to 1.2%, representing a relative reduction of 80.7%. Similarly, Poon et al. [11] investigated medication dispensing errors before and after BCMA implementation, finding a substantial relative reduction in target dispensing errors, ranging from 93% to 96%. Although BCMA primarily aids in verification and identification processes, its integration with enhanced administration systems is necessary to achieve greater overall improvements in drug safety. Regular briefings and consistent training of pediatric nurses on safe medication administration have been shown to reduce medication errors [2].

While these studies have shown considerable reductions in medication errors, interventions did not always achieve the desired safety and healthcare outcomes. A prior study on pediatric prehospital drug-dosing errors revealed that the overall medication error rate by Michigan EMS was 34.7% [3]. In response, the state introduced a pediatric dosing reference in 2014, which outlines specific medication doses and volumes for administration. Despite some improvements in error rates for asthma medications, the overall error rate increased to 35.2%. A similar study analyzing the impact of the pediatric dosing reference (PDR) and the requirement for doses to be drawn into smaller preloaded syringes using a stopcock, as well as the dilution of certain drugs to different concentrations, revealed an overall error rate of 31.2% [3]. These errors were primarily associated with 10-fold dosing errors, dilution errors, and the use of length-based tape. In a similar setting, a mobile app-based solution, implemented during simulated pediatric resuscitation in a prehospital setting, demonstrated a reduction in the mean time for drug preparation and delivery by 40 and 47 s, respectively, resulting in fewer overall medication errors compared to conventional methods [12].

Advancements Using Immersive Technologies in Medical Education and Surgical Applications

As technology has progressed over the years, it has gradually found its indispensable place in medical education. This evolution has been further accelerated by the transition from traditional 2D web interfaces to immersive experiences offered by 3D technologies: AR, VR, and MR [13]. In the healthcare industry, VR, AR, and MR are widely used for patient examination, diagnosis discussions, treatment recommendations, remote assistance from healthcare experts, and training. These technologies have seen significant advances and are increasingly being used in the surgical field, particularly in orthopedics and general surgery [14]. They improve the visual perception of surgeons in the operating room and optimize the visualization of complex 3D data. A study by Ulrich et al. [15], which focused on AR in osteotomy surgeries, demonstrated improvements in surgical performance outcomes. However, usage is accompanied by challenges, including the need for device registration and calibration before surgeries and the discomfort associated with the use of head-mounted displays (HMDs) during procedures.

There has been a surge in research into MR surgical applications, with the advantage of real-time interaction with both the virtual and physical worlds. It allows for a comprehensive study of anatomical structures and supports the training, diagnosis, and treatment of liver diseases. The potential applications of MR in surgery are extensive. These include the use of MR holograms for heart surgery planning, real-time visualization of complex data, and intraoperative navigation in spinal procedures and laparoscopic surgeries. MR technology also aids in needle navigation and screw placement, allowing detailed examination of anatomical structures, which promotes improved training, diagnosis, and treatment. However, further technological development is necessary to improve the viability of AR and MR in neurosurgery on a larger scale, and new imaging techniques should be exploited to fully realize the potential of these technologies to improve surgical outcomes and patient care [14,15].

This shift toward immersive technologies is also seen in pediatrics, where they are used for education, training, and practice. In addition, AR, VR, and MR provide supportive care to alleviate pain and anxiety in children [6]. In pediatric emergencies, accurate weight estimation is essential for proper medication dosing. Common methods used include parental recall, length-based tapes such as the Broselow–Luten tape, and age-based formulas. Among these, weight estimation by parents or legal guardians is highly accurate. Length-based tapes, such as the Broselow tape, generally provide better accuracy compared to age-based formulas [16]. Utilizing immersive technology, Scquizzato et al. [17] introduced a smartphone application equipped with AR features tailored to estimate weight in critically ill pediatric patients, simplifying dosage calculation based on weight in high-stress out-of-hospital emergency situations. Another study by Schmucker M et al. [18] developed a method to identify the size of the patient for dose calculation based on weight in pediatric emergencies, employing object recognition models and augmented reality technology. Furthermore, ongoing research by Waltuch T et al. [19] involves the development of a smartphone application called AiRDose, which offers length-based weight estimations for dosing in children, alongside recommendations for medication dosing, defibrillation energy, and equipment sizing. These estimations are derived from Broselow conversions.

In addition, advances in technology, especially the integration of VR, AR, and MR, are revolutionizing healthcare education and awareness initiatives, offering remote learning and skill enhancement after COVID-19. Yoo et al. [6] conducted a comprehensive review focusing on the expansion of AR, VR, and MR technologies in pediatric care. The research underscores the unique hurdles associated with the deployment of these technologies in pediatric settings, where factors such as patient compliance and self-reporting pose significant challenges. However, educational interventions that use VR, AR, and MR devices have demonstrated potential in creating immersive environments that outperform conventional methods by removing time constraints and ethical considerations while providing unlimited opportunities for practice. A systematic review by Goldsworthy et al. [20] highlighted the increased usage of extended reality (XR) in pediatric intensive care over the past 5 years, especially for the purpose of healthcare education. Immersive technologies appear to be a safe and feasible intervention to decrease pain and anxiety in patients in the Pediatric Intensive Care Unit (PICU). However, compared to the hospital setting, little effort has been made to decrease errors in emergency services.

3. Materials and Methods

3.1. Collection of Data

The data for this study were collected from a prior observational study focused on evaluating prehospital pediatric medication dosing errors after the implementation of a statewide pediatric drug-dosing reference specifically designed for emergency medical services (EMS) in Michigan [3]. Each EMS crew participated in four simulated scenarios: an infant experiencing a seizure accompanied by hypoglycemia, an 18-month-old child with a partial thickness burn, a 5-year-old child suffering from anaphylactic shock, and an infant undergoing cardiac arrest. These simulation sessions were recorded and later evaluated using a standardized scoring sheet. A total of 142 simulations were performed, with the following distribution: 36 for seizures, 35 for burns, 36 for anaphylactic shock, and 35 for cardiac arrest.

A post-simulation analysis revealed that 31.2% of the drug doses were administered incorrectly to the patients. We further examined the data with causes of errors and potential failure modes. We then implemented a robust design thinking process to develop a head-mounted mixed-reality application with the aim of reducing the frequency of medication errors for children during PMA in emergency services. Adhering to Michigan EMS protocols, the MR app included three scenarios—infant cardiopulmonary arrest, infant seizure, and child burns—all performed using a sensor-equipped mannequin without children involved in the study. Each scenario required a doctor and an EMT to respond to simulated emergencies using standard medical tools and the MR device. Qualitative methods such as root cause analysis (RCA) and failure mode and effects analysis (FMEA) were utilized to uncover the underlying causes and routes that led to these errors.

3.2. Root Cause Analysis

A root cause analysis was performed on the data to analyze the observed medication errors. RCA is a widely used framework in healthcare settings to identify and address potential risks and errors. We utilized the systems approach, building upon James Reason’s model of Accident Causation (Swiss Cheese model), along with Henriksen et al.’s framework to systematically categorize and comprehend both active and latent errors within the dataset [21]. Supported by the Agency for Healthcare Research and Quality and the Institute for Healthcare Improvement, we used the RCA methodology to investigate underlying causes and effectively address identified errors and adverse events [22]. Upon conducting RCA, four primary categories of errors were identified from the 142 observed simulations: the presence of air in the syringe, the misinterpretation of dosage dilution steps, incorrect weight considerations, and knowledge gaps.

Simulations recorded via video, delved into participants’ approaches, identifying errors of commission and omission, and solicited feedback on the challenges and factors influencing decision making during medication administration. Figure 1 shows the distribution of overdoses and underdoses across all drugs for the four simulated cases.

3.3. Failure Mode and Effects Analysis

FMEA is used to proactively identify potential failures in various components and processes within the healthcare system. This entails systematically evaluating each failure mode by assigning scores on a scale from 1 to 10 for potential severity (S), likelihood of occurrence (O), and likelihood of detection (D). Subsequently, Risk Priority Numbers (RPNs) are computed ($RPN=S\times O\times D$), allowing for the prioritization of the identified failure modes. Figure 2 presents an RPN scatter plot that illustrates the priority failure modes along with their corresponding S, O, and D values.

3.4. Observations

The errors of commission and omission revealed several critical areas of concern. Unrecognized air frequently entered administration syringes during direct medication draws or transfers to a second syringe for dilution, leading to underdoses due to the air being rarely noticed. This underscores the importance of providing clear guidance on the use of stopcocks prior to medication administration. The failure to check blood glucose levels in seizure cases and the omission of essential medications in a few cases both highlight the need to address the lack of familiarity with dosing procedures and reinforce proper practices. Incorrect weight measurements frequently led to drug-dosing errors, underscoring the critical need for accurate weight assessment, especially given the increased cognitive load during the multistep dilution process.

Furthermore, instances of underdosing and overdosing occurred when participants misinterpreted the PDR dosage instructions or did not follow the prescribed dilution ratios. Some mistook the diluted volume for the volume of the native drug to be administered. The PDR provides comprehensive information on various patient conditions and covers all weight versus age ranges. Knowledge gaps were also identified, including unfamiliarity with dosage protocols and dilution procedures, emphasizing the need for improved training and education in medication administration. These findings highlight the need for improved training and real-time support during scenarios, such as implementing a verification process to ensure that all steps are followed correctly.

4. Results

4.1. Initial Analysis and Design Considerations

From the data analysis, we determined that inaccuracies mainly arose from the misinterpretation of instructions, air bubbles while drawing medication, inaccuracies in measuring the weight of the patient, and knowledge gaps. This study identified underdose and overdose errors, focusing on critical areas to improve medication administration protocols. Keeping all the results in one place, the objective of this study is to automate the traditional medication administration process in prehospital emergency settings to minimize the occurrence of these errors by eliminating the need for traditional questionnaires and MI-MEDIC cards. After categorizing errors and prioritizing the analysis of root causes,

Table 1. Initial design considerations to address failure modes.

Medication Stage	Active Error	Latent Error	Task Description	Current Work	Design Proposal
Weight determination	Incorrect tape Leg positioning	Medical device issues (HSI)	Accurate tape positioning and leg alignment verification are crucial for precise weight calculation, particularly in weight-based dosing scenarios.	[16,17,18,19]	Virtual Checklist Approach: Implementing a checklist for verifying tape alignment and patient leg positioning before length measurement can enhance procedural accuracy in pediatric emergency care. Mixed-Reality Approach: Implementing a scanning mechanism with a custom tape and unique color barcodes. After measuring the length and identifying the color, scan the respective barcode and have a visual representation displaying the weight ranges on a HoloLens 2 device. Mixed-Reality and Machine Learning Approach: Utilizing MR devices for weight measurement via machine learning and object recognition, incorporating Broselow conversions for enhanced accuracy.
Dose determination	Misinterpreting cognitive aids/MI-MEDIC card instructions	Task complexity (individual characteristics)	Interpreting MI-MEDIC card instructions in high-stress prehospital settings, particularly under poor lighting conditions, can escalate the risk of medication dilution errors.	[23,24,25]	Display tailored medication options after entering the patient’s condition and weight. Display MI-Medic card instructions on the field overview for all weight vs. age dosage instructions.
	Picking up wrong drug	Task complexity (individual characteristics)	Pediatricians may inadvertently select the wrong medication due to similarities in appearance or name, resulting in medication errors.	[10,11,26,27]	BCMA: Implementing barcode scanning for medication verification prior to dose preparation. MR approach: Using Deep Convolutional Neural Networks (DCNNs) for reading drug vial labels, alongside Optical Character Recognition (OCR) and Natural language Processing (NLP) for image and text recognition.
Dose preparation	Difficulty seeing air in the syringe	Medical device issues (HSI)	Not verifying air in the syringe before administering, and lack of knowledge about how to draw medication into the syringe and the use of stopcocks.	[28,29,30]	Guide Approach: The application provides a virtual confirmation checklist for possible air in the syringe and the correct volume, along with visual guidance on how to draw medication using a stopcock. Alternate approaches: Prefilled syringes offer advantages but face challenges that include high manufacturing costs, low adoption rates, risks of contamination due to improper storage, and limited real-world data on clinical outcomes. Using sensors to detect air bubbles in the syringe. Limitations not very ideal for prehospital emergency cases, less real-world data on its clinical use
Dose administration	Knowledge deficit about medications and repeat dosage protocols	Knowledge of protocols (individual characteristics)	Lack of familiarity with procedures and medication dosage protocols, leading to potential errors in implementation.	[23,29,31]	MR Guide Approach: Including virtual guide screens for medication dilution procedures and a checklist to ask for a repeat dose. The application should also note the time of medication administration.

outlines the classification of active and latent errors and the design considerations to address them. The RPN scatter plot (Figure 2) shows that incorrect weight and incorrect dose are the primary failure modes with the highest RPN values. Following these, in descending order of impact, are issues such as the choice of the appropriate drug, the administration of the wrong drug, and missing a dose.

To mitigate errors and streamline the dosage preparation and dilution process in critical situations, employing prefilled syringes containing prediluted medication could be beneficial. However, this approach presents challenges such as increased manufacturing expenses and limited adoption rates. Improper storage may lead to contamination risks, with insufficient real-world data on clinical outcomes, particularly regarding contamination. Furthermore, the shelf life of prefilled syringes varies depending on the medication, and incorrect use of a prefilled syringe still causes potential medication errors, such as selecting the wrong syringe or administering an incorrect dose [30].

Given that most errors arise from human mistakes and knowledge gaps, an interactive application that works as a virtual guide by interacting with real-world elements during emergency situations could provide significant benefits while also documenting the actions taken by the pediatrician. Incorporating BCMA systems into prehospital emergencies presents a promising solution to reduce dosing errors by allowing the verification of medication through scanning. Several studies have shown a notable decrease in medication administration errors after the implementation of BCMA in emergency settings and pharmacies [10,11,26]. To streamline the process for pediatricians and ensure that they have access to the correct information quickly, the objective is to present only the relevant details from the MI-MEDIC card based on the patient’s specific condition. Incorporating features such as scanning customized tape instead of the Broselow tape to provide a visual overview of weight information based on color groups, along with interactive guidance overlays and checklists to verify critical factors such as weight, dosage, and medication volume in collaboration with a partner, could be highly advantageous. In this way, the application is more adaptable, allowing users to update the app with alternative medications based on availability. The following section outlines the design and development of MR technology to mitigate medication administration errors by using Microsoft HoloLens 2 in conjunction with BCMA. This intervention, as part of an ongoing research effort, aims to leverage MR capabilities by automating the current medication administration process to effectively reduce medication errors in prehospital pediatric emergency care.

4.2. Design of MR Technology

We aim to design and develop a prototype of a head-mounted MR application using Microsoft HoloLens 2 headsets and its unique ability to seamlessly blend virtual overlays with real-world elements. Our solution revolves around the integration of augmented intelligence to guide paramedics through the complex process of pediatric medication administration. This technology automates the medication verification process and provides virtual assistance to paramedics, aiming to improve the accuracy and safety of PMA medication in EMS. Paramedics can interact with the MR application to select the specific patient condition, access weight-based dosage instructions, and receive step-by-step guidance for medication dilution, alongside checklists at each critical step, prompting confirmation before proceeding. A detailed flow diagram outlining the proposed solution, encompassing both the application-as-a-guide and MR device approaches, is presented in Figure 3. Upon receiving an emergency call, the application guides the paramedic through a visual overview to select the patient’s condition, followed by requesting weight information. The paramedic can launch the application from the app menu on a HoloLens 2 device using hand gestures.

4.3. MR App Development

The development environment for this approach primarily uses Unity as the editor, facilitating the implementation of interactive features. In addition, the scanner is connected to the MR device via Bluetooth. To ensure compatibility, incorporating modules such as Windows Build Support (IL2CPP) and Universal Windows Platform Build Support within the Unity Hub environment makes the app compatible with various platforms, allowing it to run on all Windows devices. To enhance the mixed-reality experience, we use the Mixed-Reality Feature Tool alongside Microsoft Visual Studio Community 2022 for deployment on MR devices. We envision our approach as a comprehensive tool for emergency medical teams to streamline the process of diagnosing patients and selecting the appropriate medications. Figure 4 shows a screenshot of the mixed-reality application on a HoloLens 2 device. In instances where a parent or legal guardian is not available to inform the patient’s weight, the application offers two alternative methods. The first method utilizes a customized tape for length/height measurement and identifying the color group.

The application guides the user through the proper patient leg positioning and measurement technique. The second method is weight estimation using age-based formulas. Although parental recall of patient weight is often the preferred initial method, in cases where the parent is not available, length-based weight estimation using tape is more accurate than the age-based method [16]. However, in cases where the length of the patient cannot be measured, pediatricians proceed with the age-based technique.

In this approach, we use a custom-designed tape embedded with unique barcodes and colors assigned to various weight categories. The MR device is integrated with the barcode tape, which corresponds to distinct weight groups. By scanning these barcodes, pediatricians can quickly and accurately determine the patient’s weight, facilitating precise medication dosing. Upon identifying the color of the tape, the user scans the corresponding barcode, which displays a weight range on a visual screen for confirmation. Once the weight is obtained, the application prompts the user to scan the barcode of the medication to verify the correct identification and concentration. Alternatively, the application can enlarge the vial labels for easy reading by incorporating DCNN and OCR techniques. If scanning is not feasible, medical personnel should consult with a partner before proceeding to draw the medication into the syringe. The application provides visual guidance for drawing the medication into the syringe using a stopcock (Figure 5). An essential safety measure includes inspecting the syringes for air bubbles, with prompts for correction if necessary. Once the medication is administered, the application prompts the user to check blood pressure, followed by a repeat selection of the dosage protocol if needed, ensuring detailed drug administration protocols. Importantly, the application records the time during dose selection for accurate documentation. In this way, the application not only improves the accuracy of medication dosing by providing tailored information based on real-time data but also streamlines the workflow for healthcare providers, making it an invaluable tool in pediatric care.

5. Conclusions

This research emphasizes the need to improve current pediatric medication administration procedures in prehospital emergency medical care settings. From simulation trial data, significant dose errors were identified, predominantly highlighting underdosing and an alarming instance of overdosing. These errors underscore the potential dangers that pediatric patients face during emergency situations, which calls for immediate improvements in medication safety protocols. RCA and FMEA unveiled the underlying reasons for these errors, focusing on human and knowledge-based factors. Furthermore, post-simulation interviews provided valuable insights into the complexities faced by medical teams during medication administration, highlighting the need for targeted interventions to address both active and latent errors. The proposed design intervention presents a promising strategy to reduce medication errors by automating the traditional pediatric medication administration process. It also aims to improve prehospital emergency care by providing tailored information, eliminating the need for questionnaires and MI-MEDIC cards. In addition, the application can be updated as needed to meet evolving requirements. By integrating visual representations of accurate information in real time and employing checklists and BCMA for verification, the solution aims to improve accuracy and safety in medication administration for pediatric patients. This approach ultimately seeks to improve patient outcomes and minimize the risk of adverse events in pediatric emergency care settings. Ongoing research will further validate the effectiveness and feasibility of these technological interventions in real-world clinical scenarios.

6. Limitations and Future Work

Our research endeavors to offer a solution for automating medication administration in prehospital pediatric emergency care and minimizing medication errors through the implementation of an interactive application using MR technology. However, our proposed design does come with certain limitations. Emergency pediatric personnel should receive comprehensive training on the operation of the device. Given the high-stress environment of prehospital emergency sites, medical personnel unfamiliar with mixed-reality technology may encounter challenges, potentially resulting in delays or errors in utilizing the application effectively. Paramedics may experience discomfort wearing HMDs, which could impact their performance and willingness to use the device in emergency care. Effective communication is crucial in this approach, as the device will be worn by paramedics. Continuous communication with EMT personnel is necessary at each step, as the screen is not visible to them.

Furthermore, weight estimation methods such as custom tape or age-based estimation may have varying levels of accuracy, especially in cases of obesity or malnourishment, leading to dosage errors [16]. Future research could explore AI-based patient condition detection and automatic patient weight measurement using MRI technology as a potentially better solution. While the application aims to reduce medication errors by improving safety measures, the effectiveness of the proposed solution will be verified in the subsequent phases of the study, which will include usability testing and randomized controlled trials (RCTs).