Waiting lines in healthcare are everywhere. Queuing theory is one of the main tools of Data Analytics/Operations Research. It is a quantitative approach to the analysis of the properties of waiting for lines (queues) when patients’ arrival (demand for service) and service time (supply) are random values. A set of examples from real hospital practice (the radiology department, Froedtert Hospital, WI) and an outpatient clinic with a different number of servers will be presented.
The use of queuing analytics will be demonstrated for the calculation of the waiting lines and the number of exam rooms with different patient arrival rates, the need for buffer capacity as a hedge against randomness, steady-state queuing vs. non-steady state, as well as the effect of the unit’s scale on waiting time.
Assumptions and limitations of analytic queuing models will be highlighted and summarized in Tool.
Excel spreadsheet and some simple analytic formulas for queues with random vs. non-random patient arrivals.
Why you should Attend:
While one could find rich literature on various aspects of queuing theory, it is typically presented as an academic mathematical development full of complicated equations which have a limited application for practical use.
One should attend this webinar because it is focused on examples from real hospital practice, not the complicated formulas and mathematics of the probability theory. All presented examples are aided by the provided Excel spreadsheet by the instructor with real hospital experience. His experience, in particular with queuing modeling, was presented in his widely cited book with more than 10,000 sales worldwide: Kolker, A., "Healthcare Management Engineering: What does this fancy word really mean?" SpringerBriefs, NY, 2012.
This book was used as the main text for the training course by the National Health System, the UK, as well as by the Lubar School of Business at the University of Wisconsin-Milwaukee for the graduate course on Healthcare delivery systems and data analytics.
Areas Covered in the Session: