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

Time: 08:00 AM PDT | 11:00 AM EDT
Duration: 90 Minutes
Webinar Id: 801508
Instructor: Alexander Kolker 
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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 the different number of servers will be presented.

The use of queuing analytics will be demonstrated for calculation 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 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 Tool

Excel spreadsheet and some simple analytic formulas for queues with random vs. non-random patient arrivals.

Why should you Attend: While one could find a 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 worldwide: Kolker, A., "Healthcare Management Engineering in Action" 2nd Ed., Springer Nature, 2024. The 1st Edition 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 system and data analytics.

Areas Covered in the Session:

  • 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 Single
    • Server (M/M/1)
    • Multiple Servers (M/M/s)
  • Multiple exercises and demonstrations with discussions and explanations of the underlying fundamental management principles using the instructor-provided Excel spreadsheet

Who Will Benefit:
  • Nursing Managers
  • Chief Nursing Officers
  • Directors and VP of Quality and Operations Improvements of Healthcare Organizations

Speaker Profile
Alexander Kolker Kolker holds a Ph.D. in applied mathematics. He is an expert in advanced data analytics for operations management, computer simulation, and staffing optimization with the 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 Southern state of Wisconsin, and 5 years for Children’s Hospital of Wisconsin as a lead computer simulation and system improvement consultant.

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

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