Date of Award
Master of Science
Oleg Shylo, Charles Noon
The healthcare industry has recently been given an additional set of guidelines called the Affordable Care Act (ACA). The guidelines (or measurements) will be utilized to assess each hospital compared with other hospitals of similar size, and based on score reimbursed for Medicare payments accordingly. An important measurement in these guidelines pertains to patient satisfaction; therefore, increasing patient satisfaction is an important goal for hospitals. To accomplish this goal, many hospitals are re-evaluating their nurse staffing procedures to try to match patient demand with nurse availability.
Using predictive modeling with optimization, hospital administrators can develop/improve their plans for the future. Each concept provides benefits to the hospital in the utilization of the nursing staff. Predictive modeling uses historical and real time data to forecast plans for the future. This provides the hospital administrators a baseline to estimate the number of nurses needed and the number of nurses to be hired. Optimization provides the best case scenario for the number of nurses required to meet patient demand while minimizing cost to the hospital. This research combines the two ideas into multiple models using AnyLogic and Excel as predictive/analysis tools.
The implementation of these models into a hospital environment provides new insight to the nurse staffing process allowing changes to be made to accommodate new regulations. The models can also provide the ability for management to run “What-If” analysis to understand what the staffing levels should be in a given situation. Results will provide the additional tools required to be prepared for emergencies. Healthcare is an industry where seconds count, and expanding the ability to be prepared is always an asset.
Ramsey, Kelcee Storme, "Using Predictive and Descriptive Models to Improve Nurse Staff Planning and Scheduling. " Master's Thesis, University of Tennessee, 2014.