Sensitive Analysis

Sensitivity analysis is a technique used to determine the effect of various values ​​of an independent variable on a particular dependent variable under a given hypothesis. In the presentation, I will explain factors that affect the composition of Patton-Fuller health care providers such as increase in wages and change in ratio of nurses and patients. Sensitivity Analysis, Healthcare Case Study According to the income and expenditure statement of medical institutions, the financial ratio has declined. This means that raising employees actually leads to a decrease in net income.

There are various ways to measure and deal with risks. One way is to perform sensitivity analysis. Sensitivity analysis is a way to assign the uncertainty (value or otherwise) of the output of the model to the cause of the various uncertainties of the model input. The related method is uncertainty analysis. It focuses on quantifying the model output uncertainty. Ideally, uncertainty and sensitivity analysis should be performed at the same time. Another way is scenario analysis. This includes the process of analyzing potential future events by considering possible alternative outcomes.

Sensitivity analysis is used to examine changes in the structure of the organizational model. With the help of sensitivity analysis, anyone can determine the sensitivity of change based on the model value of the parameter. Using sensitivity analysis, you can easily evaluate and build models. Sensitivity analysis allows you to check the uncertainty associated with model parameters. When there is a difficult amount, it is difficult to calculate different parameters of the system dynamics model, and it is difficult to estimate the parameter value of the indeterminate model. Sensitivity analysis is an important tool for measuring the accuracy of parameters and it is necessary for the model to be effective and useful in actual scenarios. If you get the result that the model is not sensitive with the help of sensitivity analysis, you can estimate the likelihood in the model, but you can know the reasonable parameter value.