|M.Sc Student||Khabia Ohad|
|Subject||Nursing Man Power Allocation in Hospitals' Inpatients Units|
- Staff Vs. Quality of Care
|Department||Department of Industrial Engineering and Management||Supervisor||Professor Issachar Gilad|
|Full Thesis text - in Hebrew|
According to logical assumptions there is direct tie between a good medical treatment and quality of service and the nursing manpower dedicated to a patient who is treated in a hospital unit. The allocation of manpower inputs and a suitable caregiver, should match patient's medical needs in order to determine the success of the medical treatment. A need of a formal system for a systematic allocation procedure to obtain the best mix of nurses to handle a mix collection of patients in a unit has been a long time desire of hospital managements.
The goal of this study was to demonstrate a methodology to calculate the best mix of nurses per shift for a forecasted collection of patients, in a given unit. Forecasting is based of measured performance and statistical evaluation procedures. The procedure enables the managerial level to address the nursing workforce to quality of nursing treatment in hospitalization units. The study provides a computerized tool based on a cross-sectional simulation model. It provides the calculated value for full shifts of nurses to a work shift, as FTE (Full Time Equivalents) for a planning period chosen on the user’s screen. The system enables the user with a choice between five options of nurse-allocation policies; it also provides a way to evaluate grading for the quality of nursing treatment. The model is comprised of seven main stages: (1) building a standard process of patient classification; (2) issuing measured treatment times for nursing tasks; (3) forecasting the unit’s patient mix during a shift; (4) defining nursing treatment policies for nurse allocation; (5) developing & constructing a shift-size simulator; (6) defining & measuring the quality of nursing treatment; and (7) allocating a mix of nursing manpower according to the desirable treatment quality.
The model has been tested by application with data of 3 hospital units in Sheba Medical Center: 2 internal units and 1 surgical unit. The study indicates a clear recommendation for hospital management; it will be fruitful to re examine the current traditional nurse-allocation approach due to poor performance. It is advised however to act toward establishing managerial decisions making for workforce allocation, reallocation of nurses in hospital units - by alike quantitative engineering methods.