Analysis of the Installed Productive Capacity in a Medical Angiography Room through Discrete Event Simulation

Operating Room Performance Optimization Metrics: a Systematic Review

Literature proposes numerous initiatives for optimization of the Operating Room (OR). Despite multiple suggested strategies for the optimization of workflow on the OR, its patients and (medical) staff, no uniform description of 'optimization' has been adopted. This makes it difficult to evaluate the proposed optimization strategies. In particular, the metrics used to quantify OR performance are diverse so that assessing the impact of suggested approaches is complex or even impossible. To secure a higher implementation success rate of optimisation strategies in practice we believe OR optimisation and its quantification should be further investigated. We aim to provide an inventory of the metrics and methods used to optimise the OR by the means of a structured literature study. We observe that several aspects of OR performance are unaddressed in literature, and no studies account for possible interactions between metrics of quality and efficiency. We conclude that a systems approach is needed to align metrics across different elements of OR performance, and that the wellbeing of healthcare professionals is underrepresented in current optimisation approaches.

Reduction of the Cycle Time in the Biopsies Diagnosis Through a Simulation Based on the Box Müller Algorithm

Anatomic pathology services study disease in hospitals on the basis of macroscopic and microscopic examination of organs and tissues. The focus of this research investigation was on improving clinical biopsy diagnosis times through simulation based on the Box-Muller algorithm to reduce the waiting time in the diagnosis of clinical biopsies. The data were provided by a hospital in San José (Costa Rica). They covered 5 years and showed waiting times for a pathological diagnosis that for some biopsies were close to 120 days. The correlation between the main causes identified and the cycle time in the biopsy diagnostic process was defined. A statistical analysis of the variables most representative of the process and of the waiting times was carried out. It followed the DMAIC structure (Define, Measure, Analyse, Improve, Control) for the continuous improvement of processes. Two of the activities of the process were identified as being the main bottlenecks. Their processing times had a normal distribution, for which reason a Box-Muller algorithm was used to generate the simulation model. The results showed that waiting times for a diagnosis can be reduced to 3 days, for a productive capacity of 8 000 biopsies per annum, optimizing the logistics performance of health care.

Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.