|M.Sc Student||Wissmark Sharon|
|Subject||Improving the Quality of Treatment in the Emergency|
|Department||Department of Industrial Engineering and Management||Supervisors||Professor Avraham Shtub|
|Mr. David Sinreich (Deceased)|
|Full Thesis text - in Hebrew|
Improvement and efficiency of the Emergency Department (ED) has been a high profile issue in the past few years. Various studies were performed on the subject. Some of these studies used IE techniques such as simulation and data mining.
Simulation is well known as a strong analytic tool that permits "What if….?" questions and demonstrates the consequences without affecting the current system. Simulation also helps in establishing general time estimations, which cannot be obtained by observations, especially in complex systems such as the ED.
Data mining is an expanding and developing area. This tool was used in medical research for finding patient classification and high-risk profile populations.
One of the subjects yet to be researched is predicting load and capacity planning in the short term. This subject could lead to improved quality of treatment in the ED - which is the goal of this current study.
This research addresses the subject of prediction and capacity planning in 3 parts:
The relevant data was placed in the models and the model results were compared to the actual load that was needed at that time (obtained from the simulation results). The models were tested in different resolutions, to check their accuracy and try and improve it. The model was tested on one resource - the nurses. A statistical analysis found no difference between the predicted load (the model) and the current load (the simulation).
The accuracy of prediction of the load for an 8-hour resolution stands at 14%, which is below the goal of 15% accuracy required for a shift.
Validation of the tool was not conducted due to the complications of acquiring data from other analyses and reanalyzing the data classification and simulation model. Validation will be possible once a uniform patient classification is generated.