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Analyzing Emergency Department Boarding Variations in Hong Kong During COVID-19 Pandemic Waves


Core Concepts
Residential built environment and sociodemographic profiles impact ED boarding during COVID-19 waves.
Abstract
Title: Analyzing Emergency Department Boarding Variations in Hong Kong During COVID-19 Pandemic Waves Authors: Eman Leung, Jingjing Guan, Kin On Kwok, CT Hung, CC. Ching, CK. Chung, Hector Tsang, EK Yeoh, Albert Lee Institutions: The Chinese University of Hong Kong; Hong Kong Polytechnic University Abstract: Examines ED boarding variations across COVID-19 waves using a hybrid CNN-LSTM model. Structure: Introduction to ED Crowding Challenges: ED crowding impacts patient safety and healthcare system performance. Forecasting Patient Demand in EDs: ARIMA models used for short-term crowding predictions. Transition to Machine Learning Models: Deep learning models applied due to irregular variations caused by COVID-19. Importance of Transfer Learning: Transfer learning enhances model performance across different pandemic phases. Results and Insights: Highest ED boarding between waves four and five with significant predictive features identified. Implications for Healthcare Systems: Preparedness measures, targeted interventions, resource allocation guidance highlighted. Conclusion and Future Directions.
Stats
"The greatest proportion of days with ED boarding was found between waves four and five." "The best-performing model for forecasting ED boarding was observed between waves four and five." "When the model built from the period between waves four and five was applied to data from other waves via deep transfer learning..."
Quotes
"The greatest proportion of days with ED boarding was found between waves four and five." "The best-performing model for forecasting ED boarding was observed between waves four and five." "When the model built from the period between waves four and five was applied to data from other waves via deep transfer learning..."

Deeper Inquiries

How can healthcare systems improve preparedness based on the findings?

Based on the findings of the study, healthcare systems can enhance their preparedness by implementing the following strategies: Enhanced Resource Allocation: Understanding the performance of different forecasting models during various stages of a pandemic can guide resource allocation decisions. Healthcare systems should allocate resources dynamically based on the most effective forecasting model for each phase to ensure optimal resource utilization. Targeted Interventions: Identifying influential factors such as housing-related sociodemographic factors and external built environment features allows healthcare managers to develop targeted interventions. By addressing these specific factors, healthcare systems can mitigate the impact on waiting times and improve overall patient outcomes. Data Integration: Integrating various types of data, including historical patterns, case counts, residential built environment features, and sociodemographic profiles, is crucial for informed decision-making in healthcare planning. Healthcare systems should focus on integrating diverse datasets to gain comprehensive insights into patient demand dynamics. Transferability of Learning Models: Leveraging transfer learning models between different phases of a pandemic can significantly enhance predictive accuracy and save time in developing new models. Healthcare systems should explore transferring learnings from one period to another to optimize forecasting capabilities across varying pandemic waves.

How are potential limitations when transferring learning models across different pandemic phases?

While transferring learning models between different pandemic phases offers several benefits, there are potential limitations that need to be considered: Domain Shifts: Differences in data distribution or feature importance between distinct pandemic phases may limit the effectiveness of transferred models. Generalizability Concerns: The transferability of learning models relies on similarities between source and target domains; if these domains differ significantly in key aspects related to patient demographics or disease characteristics, model performance may be compromised. Model Overfitting: Transferring complex deep learning architectures without proper regularization techniques may lead to overfitting issues when applied across diverse pandemic phases with varying data patterns. Data Drift: Changes in underlying data distributions or trends over time could affect model performance when transferring learnings from one phase to another without accounting for evolving data characteristics.

How can insights from this study be applied to optimize resource allocation in future pandemics?

Insights from this study offer valuable guidance for optimizing resource allocation in future pandemics through the following approaches: Dynamic Resource Planning: Utilize effective forecasting models identified in this study at different stages of a pandemic wave for proactive resource planning and allocation. 2Risk Stratification: Identify high-risk periods based on breach percentages exceeding thresholds using historical patterns and case count information; allocate resources accordingly during these critical periods. 3Targeted Intervention Development: Develop targeted interventions focusing on influential factors like housing-related sociodemographic features identified as significant contributors towards ED boarding breaches during peak periods within a wave 4Continuous Model Evaluation: Continuously evaluate model performance across multiple waves/phases while considering building-level socioecological risk factors; adjust resource allocations based on real-time feedback loops derived from forecast results
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