Abstract for: Analysis of A Patient Backlog-Resources model For Pandemic Surge and Response

Managing healthcare backlogs requires understanding the complex feedback structures that drive system behaviour over time. During pandemic surges, increasing patient loads, resource constraints, and quality of service create nonlinear effects that influence patients backlog. This work applies sensitivity and loop dominance analysis to study backlog under different policy interventions and identifies the impact of quality on long term backlog dynamics. Two different methods of Loop dominance are deployed 1) Loops that matter 2) Loop Eigenvalue Elasticity Analysis. We show that the pandemic and subsequent management actions can create long-term consequences, particularly from extended overtime work and compromises in the quality of service. A comparison of loop dominance using LTM and LEEA reveals distinct insights into system behaviour. The LTM method identifies the dominant loops at different stages of backlog evolution, while LEEA highlights the stabilizing and destabilizing roles of feedback loops throughout the pandemic. This work analyzes a pandemic-driven patient backlog model, focusing on overtime, resource constraints, and quality-of-care effects. Loop dominance analysis identifies key balancing and reinforcing feedback loops under different policy settings. A comparison of LTM and LEEA shows areas of agreement and challenges in interpretation of loop dominance.