Healthcare Data Analytics: Methods of Matching Scarce Resources with Uncertain Patient Demand - Introduction to the Queuing Analytic Models in Healthcare Settings

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 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

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 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.

By attending the webinar you’ll learn:

• Why and when to use queuing modeling in healthcare settings
• Historic background of queuing modeling in healthcare settings
• How queuing modeling was used in the Radiation department of a major hospital
• How various patient arrival rates affect waiting time and the length of the queue
• Why the buffer capacity is needed as a hedge against randomness
• How the unit’s size affects waiting time (the scale effect).
• What are the assumptions and limitations of analytic queuing models?
• What is the alternative when analytic queuing models are not applicable

• What is queuing theory?
• Queuing System Characteristics
   • Servers
   • Arrival Patterns
   • Service Patterns
• Measures of Queuing System Performance
• Multiple exercises and demonstrations with discussions and explanations of the underlying fundamental management principles using the instructor-provided Excel spreadsheet:
   • Waiting time and the number of exam rooms with different patient arrival rates
   • The buffer capacity as a hedge against randomness,
   • Steady-state queuing vs. non-steady queuing
   • 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

This webinar will provide valuable insights to: 

• Nursing Managers
• Chief Nursing Officers
• Directors, VP of Quality and Operations improvements
• Healthcare Data Analysts, Healthcare IT Professionals
• Healthcare Administrators, Operation Managers, and Executives
• Directors, VP of Quality and Operations Improvements
• Healthcare Consultants, Healthcare Policymakers, Healthcare Researchers
• Individuals Responsible for Healthcare Resource Allocation and Management