Background COVID-19 pandemic has exposed the lack of adequate and appropriate quarantine capacity globally. Most countries lack the knowledge and/or capacity to set up and manage quarantine facilities at a national scale. Methods The State of Qatar developed a systematic plan to create and manage quarantine facilities for persons with confirmed or suspected COVID-19 infection or returning travelers and residents. A checklist was developed to streamline the process and to help other institutions requiring such guidance. Results Three distinct stages were identified: acquisition, commissioning and active operations. Steps required for each stage were identified and added to the checklist. Conclusion We share our experience and a checklist for setting up new quarantine capacity at a national level. Such checklists can serve as a critical tool to quickly and efficiently ramp up capacity in this setting.

Background: There are few statistics on dialysis-dependent individuals with end-stage kidney disease (ESKD) in Qatar. Having access to this information can aid in better understanding the dialysis development model, aiding higher-level services in future planning. In order to give data for creating preventive efforts, we thus propose a time-series with a definitive endogenous model to predict ESKD patients requiring dialysis. Methods: In this study, we used four mathematical equations linear, exponential, logarithmic decimal, and polynomial regression, to make predictions using historical data from 2012 to 2021. These equations were evaluated based on time-series analysis, and their prediction performance was assessed using the mean absolute percentage error (MAPE), coefficient of determination (R 2 ), and mean absolute deviation (MAD). Because it remained largely steady for the population at risk of ESKD in this investigation, we did not consider the population growth factor to be changeable. (FIFA World Cup 2022 preparation workforce associated growth was in healthy and young workers that did not influence ESKD prevalence). Result: The polynomial has a high R 2 of 0.99 and is consequently the best match for the prevalence dialysis data, according to numerical findings. Thus, the MAPE is 2.28, and the MAD is 9.87%, revealing a small prediction error with good accuracy and variability. The polynomial algorithm is the simplest and best-calculated projection model, according to these results. The number of dialysis patients in Qatar is anticipated to increase to 1037 (95% CI, 974–1126) in 2022, 1245 (95% CI, 911–1518) in 2025, and 1611 (95% CI, 1378–1954) in 2030, with a 5.67% average yearly percentage change between 2022 and 2030. Conclusion: Our research offers straightforward and precise mathematical models for predicting the number of patients in Qatar who will require dialysis in the future. We discovered that the polynomial technique outperformed other methods. Future planning for the need for dialysis services can benefit from this forecasting.

BackgroundShortening the patient experience time (PET) in the emergency department (ED) improves patient quality and satisfaction and reduces mortality and morbidity. Worldwide, the PET target in the ED is ≤ 6 hours; however, the PET awaiting admission to inpatient Medicine at Hamad General Hospital (HGH) in the Qatar State, through ED is currently 15.3±6.4 (mean ± SD) hours.

Background: The Staff Medical Clinic (SMC) of the Hamad Medical Corporation (HMC) serves the staff members who require healthcare services, but in a crowded environment, the SMC can only meet 75% of that demand. Overcrowding reduces productivity and service quality and increases waiting time. Furthermore, overcrowding in healthcare facilities decreases the experience and satisfaction of patients and healthcare providers. Aim: The main objective of this study was to use simulation modeling to evaluate interventions that could improve SMC waiting time and efficiency. Method: Eighteen months of data on SMC patient flow, staffing, and clinical sessions were collected (January 2018 to June 2019). The patient's journey through the SMC was modeled as a series of processes with assigned durations defined mathematically using the appropriate probability distribution. A simulation flow model was developed considering the locations of the staff and nearby main hospital facilities. An intervention was proposed and evaluated through a simulation. The intervention involved redistributing 25% of the SMC staff into three main satellite clinics located at the facilities where most of the SMC patients came. Results: The proposed intervention decreased crowding by 37%, reduced staffing requirements by 28%, and increased the number of patient slots by 22%, resulting in a net increase in the number of patients served by an average of 1250 monthly, without the need for hiring new additional staffing. Conclusion: Redistribution of the available medical staff to three new satellite clinics reduces workload pressure at all sites and increases clinic capacity without additional costs.