Medicine is a complex, safety-critical and highly interactive system. Despite efforts to provide safe, effective care, adverse events still occur - clinicians make diagnostic and therapeutic errors, system constraints impact the coordination and delivery of care, and patients suffer unexpected complications and injuries. Our current understanding of the nature of medical adverse events, and our ability to develop durable preventative or mitigating strategies has been hampered by a somewhat out-dated and inadequate model of the clinical system in which care is delivered, particularly with respect to risk and reliability. Distinctly absent from such models are the interactions and inter-dependencies between different system components (staffing, instrumentation, protocols, procedures, access to and quality of information, communication modes, and scheduling cycles, system-wide volume and acuity, throughput pressures), and an adequate representation of how risk varies as a function of time and across different phases of clinical care; in short, the dynamics of risk in these clinical settings. This creates significant challenges for risk managers, providers and clinical leaders to effectively manage the risks of their clinical environment, and formulate safety policies and protocols in order to mitigate risk. In other high-risk industries such as the nuclear power, aerospace and chemical processing, experts have turned toward probabilistic modeling and simulation techniques help understand and manage systems-based risks associated with both standard- and novel- operating conditions. Collectively, the modeling, simulation and resulting analysis are referred to as probabilistic risk analysis. The work proposed here is intended to: 1. Establish the technical merit and feasibility of using formal probabilistic modeling and simulation to assess system-based risk in high-priority, high-risk areas of clinical care; 2. Identify areas of clinical care to which probabilistic modeling and simulation is most applicable for quality and safety monitoring 3. Demonstrate the feasibility of adapting existing analytic software (originally designed for use in industrial and aerospace risk assessment activities) to facilitate wide-spread use of these quantitative risk modeling techniques by healthcare and patient safety specialists. ? ? ? ?
