Queueing theory and modeling provide us with “closed-form” analytical solutions to problems involving, reasonably enough, queues. Indeed, this type of performance-focused modeling is central to properly planning and sizing infrastructure and facilities of many types, from a new hospital building with interconnected services to servers, bridges, and routers on a distributed communications network, checkout registers at a retailer, toll booths (and lanes) on an interstate, conveyor belts at an airport, or teller and drive-through windows at a bank. Complex models can be joined to form queueing networks.
A pressing need exists to become more evidence-based, and to practice and deliver healthcare accordingly. Given fast-paced technological and regulatory changes, being able to develop a probabilistic outlook as to the range of possible outcomes in decision-making is increasingly important. This material introduces Bayes’ theorem, which is central to reassessing probabilities in light of accumulating evidence.
In staff scheduling, a nurse manager may be trying to staff her floor in the best way for the coming week. What can happen if this is done informally, by a “seat of the pants” approach?