## 4 Results
### Initial Analysis and Design Considerations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Medication Stage** & **Active Error** & **Latent Error** & **Task Description** & **Current Work** & **Design Proposal** \\ \hline \hline \multirow{6}{*}{Weight determination} & \multirow{6}{*}{Incorrect tape positioning} & \multirow{6}{*}{Accurate tape positioning and leg alignment} & \multirow{6}{*}{Current probe misalignment} & \multirow{6}{*}{Current probe misalignment} & \multirow{6}{*}{Overall probe misalignment} \\  & & & & & 1. 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. & \\  & & & & 2. 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. & \\  & & & & 3. Mixed-Reality and Machine Learning Approach: Utilizing MR devices for weight measurement via machine learning and object recognition, incorporating Broselow conversions for enhanced accuracy. & \\ \hline \hline \multirow{6}{*}{Dose determination} & \multirow{6}{*}{Visinterpreting cognitive aids/MI-MEDIC card instructions} & \multirow{6}{*}{Task complexity (individual characteristics)} & \multirow{6}{*}{Test} & Interpreting MI-MEDIC card instructions in high-stress prehospital settings, particularly under poor lighting conditions, can escalate the risk of medication dilution errors. & \\  & & & & 2. Display tailored medication options after entering the patient’s condition and weight. & \\  & & & & 2. Display MI-Medic card instructions on the field overview for all weight vs. age dosage instructions. & \\  & & & & 1. BCMA: Implementing barcode scanning for medication verification prior to dose preparation. & \\  & & & & 2. 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. & \\ \hline \hline \multirow{6}{*}{Dose preparation} & \multirow{6}{*}{Difficulty seeing alive in the syringe} & \multirow{6}{*}{Not verifying air in the syringe} & \multirow{6}{*}{Not verifying air in the syringe before} & \multirow{6}{*}{Not verifying air in the syringe before} & \multirow{6}{*}{Not verifying air in the syringe before} & \multirow{6}{*}{Not verifying air in the syringe before} & \multirow{6}{*}{Not verifying air in the syringe before} \\  & & & & 2. 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. & \\  & & & & 3. 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 & \\ \hline \hline \multirow{6}{*}{Dose administration} & \multirow{6}{*}{Knowledge deficit about medications and repeat dosage protocols} & \multirow{6}{*}{Knowledge (individual characteristics)} & \multirow{6}{*}{Lack of familiarity with procedures and medication dosage protocols, leading to potential errors in implementation.} & \multirow{6}{*}{Test} & \multirow{6}{*}{Test} & \multirow{6}{*}{Test} \\  & & & & 2.1 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. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Initial design considerations to address failure modes. To mitigate errors and streamline the dosage preparation and dilution process in critical situations, employing prefilled syringes containing prefiluted medication could be beneficial. However, this approach presents challenges such as increased manufacturing expenses and limited adoption rates.