Alexander Kolker
Expert in Healthcare Information Technology and Data AnalyticsAlexander 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 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.
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.
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.
In 2022 he taught a 2-semester 12-session online course “Healthcare Operations Research and Management Science” for the UK, National Health System (NHS)-Midland & Lancashire Support Unit.
Alexander has also completed four business consulting projects using simulation modeling for optimal staffing and capacity analysis for: US Bank, Boston Consulting Group, Children’s Hospital of Wisconsin, and Ohio Hospital Association.
Recorded-webinar by: Alexander Kolker
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Introduction into Discrete Event Simulation Methodology (DES): Resource Utilization and Staffing & Scheduling Problems
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Introduction into Discrete Event Simulation Methodology (DES): Part 1 - Dynamic Supply & Demand Balance and Capacity Problems
Discrete event simulation (DES) is the most powerful methodology used in healthcare data analytics. It is widely used in situations that are too complex because of random and non-random variabilities and multiple feedback loops typical in healthcare settings. Most queuing models presented in the previous webinar cannot be applied to such processes because of a violation of the main queuing assumptions. Simple simulation models will be presented as well as playing various scenarios to discover and illustrate some fundamental management principles. Comparisons with analytic queuing models presented in the previous webinar will also be provided.
Method: DES using Process_Model simulation software
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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
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