Enhancing paediatric emergency departments management through advanced monitoring technologies and AI

"Introduction:

This project aims to integrate Artificial Intelligence (AI)and Machine Learning (ML) into Paediatric Emergency Departments (EDs) to address challenges such as long waiting times, diagnosis delays, and limited resources. AI can revolutionize patient management by improving triage, diagnosis, and treatment recommendations, and spotting early signs of diseases.

Additionally, AI can enhance patient involvement with personalised treatment plans and controls processes like drug delivery (Barth, 2024) and surgeries. This study will investigate ML algorithms to provide new insights into patient management in EDs. Research Questions: 1. Can AI algorithms effectively recognise critical patients in Paediatric EDs?

2. How can ML models detect early signs of diseases for sensible diagnosis and treatment?

3. What are the advantages of integrating AI and ML in Paediatric EDs, such as improved patient outcomes, shorter waiting times, and better resource use? Objectives:

1. To design and develop effective continuous monitoring solutions: analyse the most relevant vital signs and physiological parameters to track, incorporating lab results and medical history for full patient analysis.

2. Investigate machine and deep learning approaches in data analysis and detection of anomalies or clinical deterioration.

3. Explore open-source Large Language Models (LLMs) for communication and reporting for clinicians, patients, and carers. 4. Assess the feasibility of effective real-time monitoring post-discharge. Methodology: 1. Literature review and data analysis: Review and evaluate existing research on AI and ML in paediatric EDs.

2. Data collection and preprocessing: Collect and prepare a large dataset of patient medical records for ML models.

3. Model development and validation: Develop and validate AI systems for triage, early diagnosis, and treatment recommendations using several metrics. Outcomes: The proposed project aims to achieve the following outcomes, subdivided into three Work Packages (WP): Integrating Artificial Intelligence (AI) and Machine Learning (ML) in

Paediatric Emergency Departments. Anju Ann Jacob WP-1: Research preparation and Foundations Complete ethical approval processes within six months Publish a systematic review in a reputable scientific journal (Q1 or Q2) Commence primary data collection, investigating the initial research questions. Acquisition of essential skills in AI/machine learning through participation in relevant courses.

WP-2: Research Progress and External Presentations

Publish two high-quality journal articles (Q1 or Q2) within the next six months, contributing to the knowledge in Paediatric Emergency Departments

Present research papers at prominent Biomedical Conferences in the UK, fostering networking opportunities and expert feedback. Collect relevant data for further investigation, with a focus on exploring initial research questions. Initial insights into the effectiveness of AI/machine learning applications in Paediatric EDs.

WP-3: Consolidation and Thesis Completion

Publish two high-quality journal articles (Q1 or Q2) within the next six months, demonstrating the project's progress and impact. Contribute to a reputable scientific publication through the inclusion of a book chapter. Investigation into final research questions, providing a comprehensive understanding of the project's objectives.

Progress towards thesis completion, with each chapters written and submitted for review. Impact:

This project utilises AI to improve paediatric emergency departments by restructuring triage and decision making, identifying critical patients, optimising resources, and reducing errors. Integrating LLMs alongside medical expertise improves communication and post-discharge. References:

Barth, S. (2024) AI in Healthcare: Foresse Medical. Available at: https://www.foreseemed.com/artificialintelligence-in-healthcare (Accessed: 20/02/2024)"