Modern nursing workforce planning must extend beyond bed assignment and cost reduction, emphasizing the need for a proper work-life balance for personnel and ensuring quality patient care. Incorporating specific system features into workload allocation is time-consuming and resource-intensive. If not implemented properly, it could compromise the quality of patient care. This study aims to integrate human and environmental system features, such as dynamism, burnout, and skill-mix, to maintain a high quality of patient healthcare. To accomplish this, a MILP optimization model is integrated within a Montecarlo simulation process to determine impact indicators and confidence intervals for parameters related to patient care and workload distribution. The process is designed cyclically to enhance solution space exploration and account for adverse events, using input data from real hospitals. The results highlight that integrating human and environmental factors in patient care not only enhances workload distribution in healthcare services but also increases system flexibility. This approach enables appropriate responses to adverse events without significantly disrupting routine patient care operations. Additionally, it defines proper confidence intervals for work indicators based on care demand. The findings offer a basis for planning hospital care systems, going beyond shifts and labor regulations, considering the required care as a unit of workload to be allocated within the service. This ensures personnel are positioned where needed, increasing satisfaction levels and reducing waiting times.