ABSTRACT
INTRODUCTION: The increasing frequency of pandemics, demand for healthcare and costs of healthcare services require efficient health systems with integrated care via a command centre that ensures a centralised and coordinated approach to exercise effective leadership. DESCRIPTION: We present a case study using the conceptual framework of Franklin to describe the novel system-based engineering approach of the Saudi National Health Command Centre (NHCC) including its features and outcomes measured. DISCUSSION: The NHCC is structured into four departments and four zones with real-time data integration and visualisation on 88 dashboards. To empower leadership, it harnesses artificial intelligence affordances such as machine learning algorithms to enhance functionality, decision-making processes and overall performance. This allows for the rapid assessment of available resources and to monitor healthcare system efficiency at diverse levels of clinical and system indicators. Enhanced proactive capacity management has contributed to reducing lengths of stay, average supply chain lead time and surgery waiting list; early bending of the COVID-19 curve resulting in a low mortality rate; increasing bed capacity; deploying medical staff and mechanical ventilators rapidly; rolling out the COVID-19 vaccination programme and improving patient satisfaction. CONCLUSION: Integrating a healthcare system with a command centre provides healthcare leaders with the necessary infrastructure to create synergy between people, processes and technologies. This substantially improves both patient and service outcomes. It also allows for immediate care coordination and resource allocations and safeguards ease of access to care.
ABSTRACT
Objectives: This paper aims to measure the impact of the implemented nonpharmaceutical interventions (NPIs) in the Kingdom of Saudi Arabia (KSA) during the pandemic using simulation modeling. Methods: To measure the impact of NPI, a hybrid agent-based and system dynamics simulation model was built and validated. Data were collected prospectively on a weekly basis. The core epidemiological model is based on a complex Susceptible-Exposed-Infectious-Recovered and Dead model of epidemic dynamics. Reverse engineering was performed on a weekly basis throughout the study period as a mean for model validation which reported on four outcomes: total cases, active cases, ICU cases, and deaths cases. To measure the impact of each NPI, the observed values of active and total cases were captured and compared to the projected values of active and total cases from the simulation. To measure the impact of each NPI, the study period was divided into rounds of incubation periods (cycles of 14 days each). The behavioral change of the spread of the disease was interpreted as the impact of NPIs that occurred at the beginning of the cycle. The behavioral change was measured by the change in the initial reproduction rate (R0). Results: After 18 weeks of the reverse engineering process, the model achieved a 0.4 % difference in total cases for prediction at the end of the study period. The results estimated that NPIs led to 64 % change in The R0. Our breakdown analysis of the impact of each NPI indicates that banning going to schools had the greatest impact on the infection reproduction rate (24 %). Conclusion: We used hybrid simulation modeling to measure the impact of NPIs taken by the KSA government. The finding further supports the notion that early NPIs adoption can effectively limit the spread of COVID-19. It also supports using simulation for building mathematical modeling for epidemiological scenarios.
