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Sepsis, the tenth-leading cause of death in the United States, accounts for more than $16.7 billion in annual health care costs. A significant factor in these costs is hospital length of stay. The lack of standardized hospital discharge policies and an inadequate understanding of sepsis progression have resulted in unnecessarily long hospital lengths of stay. In this paper, a general model of when to discharge a patient with pneumonia-related sepsis from the hospital is presented. The model is parameterized using patient-based disease progression data from a large clinical study in order to characterize optimal discharge policies for various problem instances. In the presented experiments, patient health is represented by SOFA scores, which are commonly used to assess sepsis severity. Control-limit policies for specific patient cohorts defined by age and race are demonstrated. [ABSTRACT FROM AUTHOR]

In the second article of a two-article sequence, the focus is on a simulation model for screening and treatment of breast cancer in U.S. women of age 65+. The first article details a natural-history simulation model of the incidence and progression of untreated breast cancer in a representative simulated population of older U.S. women, which ultimately generates a database of untreated breast cancer histories for individuals in the simulated population. Driven by the resulting database, the screening-and-treatment simulation model is composed of discrete-event simulation (DES) and system dynamics (SD) submodels. For each individual in the simulated population, the DES submodel simulates screening policies and treatment procedures to estimate the resulting survival rates and the costs of screening and treatment. The SD submodel represents the overall structure and operation of the U.S. system for detecting and treating breast cancer. The main results and conclusions are summarized, including a final recommendation for annual screening between ages 65 and 80. A discussion is also presented on how both the natural-history and screening-and-treatment simulations can be used for performance comparisons of proposed screening policies based on overall cost-effectiveness, the numbers of life-years and quality-adjusted life-years saved, and the main components of the total cost incurred by each policy. [ABSTRACT FROM PUBLISHER]