Optimized Budgeted Nursing Staffing with Random Patient Demand

Date:
Monday, September 26, 2022
Time:

08:00 AM PDT | 11:00 AM EDT

Duration:
90 Minutes
Instructor:
Alexander Kolker 
Webinar Id:
20968
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Overview:

The objective of this webinar is providing an overview and examples of application of the data analytics methodology called the ‘newsvendor’ framework. This methodology helps to determine the optimal staffing solutions for the specified time periods for hospital units with randomly fluctuating daily patient census. The ‘newsvendor’ model is widely used for problems in which the optimal inventory level should be determined for a specified time period with an uncertain (random) demand. However, the use of the ‘newsvendor’ framework was rather limited in healthcare management. At the same time, this is a fruitful area of application of the ‘newsvendor’ framework.

Why you should Attend: Given the dynamic nature of the healthcare supply and demand, the variation and the uncertainty creates two types of problems:

  • Over-staffing, which hurts operation margins
  • Under-staffing, which requires overtime and/or premium pay that also hurts margins and causes lower quality of care
  • The latter problem adversely affects patients and staff satisfaction
Nursing managers typically estimate staffing needs and the staffing budget based on the past historical average number of patients (usually midnight census).

Because of the inevitable variability of the patient census (uncertainty), the resulting staffing is usually: (i) either not enough to deliver proper quality of care or (ii) is excessive, and results in some idle time and/or pay under contractual obligation for nothing to do.

Reference: Kolker, A., The Optimal Workforce Staffing Solutions With Random Patient Demand in Healthcare Settings. In Encyclopedia of Information Science and Technology, 4-th Ed, IGI-Global, chapter 322, pp. 3711-3724, 2017

Areas Covered in the Session:
  • Main Concept and Some Definitions
  • The “newsvendor” framework approach
    • Optimized annual staffing level
    • Optimized monthly staffing level
    • Optimized staffing for caregivers’ skills mix
    • Three methodology frameworks for modeling staffing with variable patient demand

Who Will Benefit:
  • Nursing Managers
  • Chief Nursing Officers
  • Directors
  • VP

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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