Healthcare Data Analytics: Methods Of Matching Scarce Resources With Uncertain Patient Demand, Optimized Budgeted Nursing Staffing With Random Patient Demand

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 various numbers of providers will be presented using an Excel spreadsheet. The use of queuing analytics will be demonstrated for the calculation of the waiting time and the number of exam rooms in the Radiology department with the various patient arrival rates and the requested number of X-ray exams, 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 for admission and queues with random vs. non-random patient arrivals.

Assumptions and limitations of analytic queuing models will be highlighted and summarized.

• What is 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.
• Multiple exercises and demonstrations with discussions and explanations of the underlying fundamental management principles:
    - Real-life case from the hospital radiology department
    - Performance characteristics for multiple providers (2, 3, and 4)
    - Could providers’ utilization be near 100%?
    - Splitting resources. What is better: combined or separate resources?
    - Steady-state vs. non-steady-state queue
    - Effect of the variable daily patient arrivals
    - Performance of scale. Does unit size make a difference?
    - ‘Excessive’ capacity, ‘Improved efficiency’, and access to care. The role of the buffer capacity.
    - Non-random (scheduled) arrivals

Course Level - Intermediate

While one could find rich literature on the various aspects of queuing theory, it is typically presented as an academic mathematical development full of complicated equations based on the probabilities theory.

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

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 estimation of a balance between patient wait time and providers’ utilization, required number of exam rooms, and pieces of equipment.

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.