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Validity : 15th Apr'24 to 25th Apr'24
Introduction into Discrete event simulation methodology (DES). Part 1. Dynamic supply & demand balance and capacity problems
- The concept of simulation and its use in healthcare services
- Demonstrating manually a simple discrete event simulation model: step-by-step
- Reviewing components of a simulation model
- Demonstrating simulation models for capacity analysis and supply and demand in healthcare settings, and patient flow
- Applying DES to Queuing examples considered in the previous webinar as well as presenting other examples beyond the capability of analytic queuing theory.
Overview of the webinar
Discrete event simulation (DES) is the most powerful methodology used in process data analytics. The following examples will be covered: (i) Outpatient clinic: centralized or separate locations? (ii) Outpatient clinic: non-steady-state operations, (iii) Outpatient Clinic: limited queue size with leaving ‘inpatient’ patients, (iv) Outpatient Clinic: Time-varying arrival rates, (v) separate or interchangeable (shared) operating rooms for competing emergency and scheduled surgeries, and some others.
Method: DES using ProcessModel simulation software. Building simple simulation models and playing various scenarios to discover and illustrate some basic management principles. Comparisons with analytic queuing models will also be provided.
Who should attend?
Quality engineers/analysts, Nursing Managers, Chief Nursing Officers, Directors and VP of quality and operations improvements of healthcare organizations interested in learning practical methods of data analytics for optimal nursing staffing, required number of exam rooms and pieces of equipment.
Why should you attend?
While you could find a rich literature on various aspects of discrete event simulation (DES), it is typically presented as a fictitious example in a format which is difficult to use or replicate by a layman.
You should attend this webinar because it is focused on examples from real hospital practice, not the fictitious examples. All presented examples are illustrated using powerful DES software ProcessModel by the instructor with dozens years of hospital experience as a lead simulation consultant. His experience, in particular with DES, 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 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 for the graduate course on Healthcare delivery system and data analytics.
Faculty - Mr.Alexander Kolker
Alexander Kolker holds a Ph.D. in applied mathematics and statistics. He is an expert in advanced data analytics for operations management, computer simulation modelling 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)-Midland & Lancashire.
Alexander has also completed four business consulting projects using simulation modelling for the optimal staffing and capacity analysis for: Boston Consulting Group, Children’s Hospital of Wisconsin, Ohio Hospital Association, and US Bank Corporation.