Healthcare Data Analytics: Methods of Matching Scarce Resources with Uncertain Patient Demand, Optimized Budgeted Nursing Staffing with Random Patient Demand
Alexander 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).
Because of the inevitable variability of the patient census (uncertainty), the planned (budgeted) staffing is usually:
(i) either not enough to deliver proper quality of care or
(ii) is excessive, resulting in idle time for some employees and/or pay under contractual obligation for the idle time.
This webinar is providing a methodology called the ‘newsvendor’ framework that helps in planning (budgeting) the staffing level that minimizes the overall cost of under and over staffing.
Areas Covered
- The main Concept and Some Definitions
- The “newsvendor” framework approach
- Optimized annual staffing level
- Optimized monthly staffing level
- Optimized staffing for caregivers’ skills mix
- Comparison of the 3 main methodologies for modelling staffing with variable patient demand
Course Level- Intermediate
Who Should Attend
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
Typically, nursing staffing is based on the past historical average daily patient demand (midnight census), and the average required daily hours of care per patient depending on the acuity level (supply of resources).
However, the variation and the uncertainty of the supply and demand in healthcare settings creates two types of problems:
- Over-staffing over the planned level which hurts operation margins;
- Under-staffing over the planned level, which requires overtime and/or premium pay that also hurts margins and causes lower quality of care.
The latter problem affects patients’ and staff satisfaction.
The objective of this webinar is providing an overview and examples of application of the methodology called the “newsvendor” framework.
This methodology helps to determine the optimal staffing for the specified time periods for hospital units with randomly fluctuating patient census.
The optimal staffing provides the total minimal possible cost of over- and under-staffing.
Topic Background
Workforce management significantly impacts the cost efficiency and quality of care. Indeed, workforce cost typically absorbs more than 50% of total hospital’s operating revenue. An accurate assessment of the required staffing that best matches the highly variable patient demand is an integral part of the hospital’s budgeting and planning process. Healthcare administrators must accomplish multiple clinical and quality goals while simultaneously developing realistic staffing plans and budgets. Random fluctuations of the daily patient demand (census) make the staffing planning a big challenge to many hospitals’ administrators.
-
$200.00
-
