Introduction

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 Webinar

• 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 methodology frameworks for modeling staffing with variable patient demand

Why should you attend?

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.

Who Will Benefit

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.