The primary requirement in designing air conditioning systems in healthcare facilities is eliminating contaminants. It is considered one of the crucial health elements in building design, particularly in the presence of many airborne diseases such as COVID-19. The purpose of this numerical research is to simulate various ventilation designs for a hospital room model by taking into account results obtained by previous researchers. Four designs with three airflows, 9, 12, and 15 ACH (Air Change per Hour), are applied to explore the capacity of the ventilation system to remove contaminants. The objective is to determine the influence of airflow and the diffuser location distribution on the pollutants elimination represented by carbon dioxide. The Reynold Averaged Navier–Stokes (RANS) equations and the k-ε turbulence model were used as the underlying mathematical model for the airflow. In addition, boundary conditions were extracted from ASHRAE (American Society of Heating, Refrigeration, and Air-Conditioning Engineers Society) ventilation publications and relevant literature. Contrary to what was expected, this study’s results demonstrated that increased ventilation alone does not always improve air distribution or remove more contaminants. In addition, pollutant removal was significantly affected by the outlet’s location.

Thermal comfort is increasingly recognized as vital in healthcare facilities, where patients spend 80–90% of their time indoors. Sensing, controlling, and predicting indoor air quality should be monitored for thermal comfort. This study examines the effects of ventilation design on thermal comfort in hospital rooms, proposing four distinct ventilation configurations, each with three airflow rates of 9, 12, and 15 Air Changes per Hour (ACH). The study conducted various ventilation simulation scenarios for a hospital room. The objective is to determine the effect of airflow and the diffuser location distribution on thermal comfort. The Reynolds-Averaged Navier–Stokes (RANS) equations, along with the k–ε turbulence model, were used as the underlying mathematical representation for the airflow. The boundary conditions for the simulations were derived from the ventilation standards set by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) and insights from previous studies. Thermal comfort and temperature distribution were assessed using indices like Predicted Percentage Dissatisfaction (PPD), Predicted Mean Vote (PMV), and Air Diffusion Performance Index (ADPI). Although most of the twelve scenarios failed to attain thermal comfort, two of those instances were optimal in this simulation. Those instances involved the return diffuser behind the patient and airflow of 9 ACH, the minimum recommended by previous studies. It should be noted that the ADPI remained unmet in these cases, revealing complexities in achieving ideal thermal conditions in healthcare environments. This study extends the insights from our prior research, advancing our understanding of ventilation impacts on thermal comfort in healthcare facilities. It underscores the need for comprehensive approaches to environmental control, setting the stage for future research to refine these findings further.