Healthcare Data Analytics: Methods of matching scarce resources with uncertain patient demand: Introduction into the Queuing Analytic models

Waiting lines in healthcare are everywhere. Queuing theory is one of the widely used tools of Data Analytics/Operations Research. It is a quantitative approach to the analysis of the properties of waiting 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 time 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, as well as the effect of the unit’s size on waiting time (the scale effect).
Assumptions and limitations of analytic queuing models will also be highlighted and summarized.
Tool
Excel spreadsheet 

• What is queuing theory?  Why queuing theory?
• Queuing System Characteristics
• Population Source
• Servers
• Arrival Patterns
• Service Patterns
• The average number of customers/patients in the queue
• The average wait time in the queue
• Measures of Queuing System Performance
• Model Formulations
• Multiple exercises and demonstrations with discussions and explanations of the underlying fundamental management principles using the instructor-provided Excel spreadsheet.

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. You should attend this webinar because it is focused on examples from real hospital practice without using the complicated formulas and mathematics of the probability theory. All presented examples are aided by the provided Excel spreadsheet.

The instructor’s hospital experience, in particular with queuing analytics, 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?” Springer_Briefs, NY, 2012. This book was used as a 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 (USA) for the graduate course on Healthcare delivery systems and data analytics.

• Nursing Managers
• Chief Nursing Officers
• Directors
• VP of quality and operations improvements

Alexander Kolker holds a Ph.D. in applied mathematics and statistics. He is an expert in advanced data analytics for operations management, computer simulation modeling, and staffing optimization with a main focus on healthcare applications. Alexander is the lead editor and author of 2 books, 8 book chapters, 10 journal papers, and a speaker at 18 international conferences & webinars in the area of operations management and data analytics.

As an adjunct faculty at the UW-Milwaukee Lubar School of Business, he developed and taught a graduate course Business 755-Healthcare Delivery Systems-Data Analytics.

He worked 12 years for GE (General Electric) Healthcare as a Data Scientist and CT Detector design engineer, 3 years for Froedtert Hospital, the largest healthcare facility in the Southern state of Wisconsin, and 5 years for the Children’s Hospital of Wisconsin as a lead computer simulation and system improvement consultant.

Currently, he is teaching a 12-session online course “Healthcare Operations Research and Management Science” for the UK, National Health System (NHS)-Midland & Lancashire.

Alexander has also completed four business consulting projects using simulation modeling for optimal staffing and capacity analysis for Boston Consulting Group, Children’s Hospital of Wisconsin, Ohio Hospital Association, and US Bank Corporation.