Trends and Patterns of ICU E-Referrals in Saudi Arabia during 2020–2021: Results from the National Saudi Medical Appointments and Referrals Centre

<b>Background/Objectives:</b> Intensive care unit (ICU) e-referrals are an important indicator for exploring potential deficiencies in critical care resources. This study aimed to examine the epidemiology and patterns of ICU e-referrals across all regions of Saudi Arabia during the COVID-19 pandemic using routinely collected data from April 2020 to December 2021. <b>Methods:</b> This descriptive epidemiological study analyzed data from the Saudi Medical Appointments and Referrals Centre (SMARC). This study reveals novel regional ICU e-referral patterns for critical cases using national unique digital health data, adding insights beyond the existing literature. Variables included age, sex, referral timing, reason, specialty, and region of origin. Descriptive statistics and mapping of administrative areas were performed based on e-referral request rates per 10,000 population. <b>Results:</b> During the study period, 36,619 patients had ICU e-referral requests. The mean age was 54.28 years, with males constituting 64.81% of requests. Out-of-scope e-referrals comprised 71.44% of requests. Referrals related to medical specialties, such as cardiology and pulmonology, were the most common (62.48%). Referral patterns showed peaks in July-August 2020 and May 2021. The Northern Border and Albaha areas had the highest request rates per population, potentially reflecting a higher proportion of severe cases requiring ICU-level care compared to other regions. <b>Conclusions:</b> The temporal pattern and geographic distribution of ICU e-referrals mirrored previously reported critical COVID-19 cases in Saudi Arabia. Preventive measures and vaccination programs contributed to a significant decline in ICU e-referral requests, suggesting a positive impact on controlling severe COVID-19 cases. Population-adjusted analysis revealed regional disparities, highlighting the importance of considering population size in healthcare resource management and policy.