Forecasting in health care is a functional representation of data adequately describing the process under study and is the basis for determining future values. The forecasting procedures and techniques based on data with non-numerical nature, for instance, predicting the quality attributes are based on the results of non-numerical data statistics.
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One of the widely used forecasting techniques is the regression analysis of the interval data including, the definition and calculation of rational sample size as well as regression analysis of the fuzzy data. It is crucial to overview and analyze the regression analysis as it enables managers and executives to evaluate the patterns within the health care organization and make predictions that will assist their perspective decision-making.
The key point in the analysis and planning in the health care sector is the ability to predict accurately the maximum value of performance indicators that characterize the effectiveness of a company (Keat, Young, & Erfle, 2013). It should be noted that regression analysis is one of the most widely used assays of multivariate statistical parsing. The term regression analysis implies that the dependence of one feature (the result) from a set of independent (factor) signs is subject to analysis (Keat et al., 2013).
In general, the regression analysis is helpful for the decision-making process within the organization due to several reasons. The first point is that the description of the relationship between the variables helps to establish the existence of a possible causal relationship. The second one is that compiling the regression can predict the values of the dependent variable from the values of the independent variables, which allows determining the predictor for the dependent variable.
The general formula for the calculation of indicators is as follows: Y = f(A1X1 + A2X2 + A3X3 + …AnXn). Where Y is the dependent variable and X1, 2, 3…n are the independent ones (Langabeer & Helton, 2015). The formula can be used for a simple example when the organization needs a set of new furniture. The following determinants of the demand for the furniture can be considered: the price, the preferences, the prices of the related commodities, the income, the cost, the number, the future expectations, and other possible factors. As per the results of the analysis, the alteration in the independent variable will influence the dependent one either positively or negatively.
This forecasting model can be applied as well to assess the projections in case the company wants to implement some drastic changes (Langabeer & Helton, 2015). However, it is required to fulfil certain conditions for the correct usage of the regression analysis. The factor attributes should be uncorrelated (no multicollinearity); they are to be calculated precisely in their dimensions (no autocorrelation as well); the characteristic values of one object should not depend on the characteristic values of the other objects. Finally, the resulting attribute should have a permanent variance.
Such a forecasting model would be helpful for the decision-making process within the organization when the moderate prediction horizon and preparation time are present (Langabeer & Helton, 2015). Also, it is used when there is a need to fit a line to several points. For instance, if the company has to determine the cause-and-effect connection between lifestyle options and health state, the regression analysis will be helpful.
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It is worth noting that one of the main stages in the construction of regression equations is the selection of the most significant factors affecting the resulting attribute. It can complicate the decision-making in the future if chosen incorrectly since the stage of constructing the regression model is based on a quality theoretical analysis combined with the use of the statistical techniques.
Typically, the selection of factors has two stages. In the first step, the factors are based on the content analysis as they will significantly affect the results. In the second stage, the qualitative analysis is complemented by the quantitative estimates, which allows selecting statistically significant factors (Langabeer & Helton, 2015).
Unfortunately, the multicollinearity will likely appear in the course of these actions, which will greatly complicate the reliability of the results. When describing the overall effects of multicollinearity, it is important to note that if it appears, the decreases in the accuracy of estimates of regression coefficients will be evident (standard errors of the coefficients will be too large). Therefore, it is probable that certain variables in the analysis will be administered incorrectly. Thus, adding a small number of observations can lead to strong shifts in values.
Managerial Decision-making and Healthcare Industry
It is worthy of noting that the impact of this forecast model in the managerial decision-making process can be versatile. On the one hand, managers tend to estimate the uncertainties engaged in making a prognosis incorrectly. The inadequacy of the model (the lack of precision and accuracy) is one of the aspects that can hinder adequate decision-making (Langabeer & Helton, 2015).
The presence of certain influential surveillances (the removal of which would lead to a sharp change in model parameters) can also complicate the analysis. As described earlier, multicollinearity (strong correlation effects) may adversely affect the results; thus, they cannot be considered as reliable or valid so the decision-maker will be likely to make mistakes in the assessments.
On the other hand, the model facilitates the reduction in the quantity of the raw data. It also enables finding the correlation between the economic patterns and the size of the organization. For instance, the manager can justify the size of the institution and its revenues (Langabeer & Helton, 2015). The forecasting will provide him or her with empirical support for their decision or unveil new directions unnoticed previously. Also, the industry data add relevant information about projecting trends in the healthcare industry about the analytical advantages of the regression model.
Regression analysis is a crucial forecasting method that is widely used in the healthcare industry. For instance, many hospitals utilize this method in a variety of settings. It is used to calculate the implicit costs and to predict the reimbursement stake. Also, it is an important administrative tool in hospitals that enables evaluating the pharmacoeconomics and adjust their outcomes to foster adequate solutions in terms of patient care provision (Langabeer & Helton, 2015).
Further, regression analysis is a helpful tool in risk prognosis related to the cases of individual patients. It can also be used to reveal the statistical relationship (for example, the probability of developing high blood pressure in patients with varied age and weight factors). Thus, the regression analysis is an important technique that can be applied by administrators, managers, and clinicians to define the possible outcomes of their perspective decisions mathematically.
In conclusion, the regression model combines a wide range of versatile functions that describe some regularity. The measured data are most commonly used to build this model; it is accurate in the details, though, often uninterpretable. It can happen either due to a large number of candidate models that are used to build an optimal model or due to its complexity. The disadvantages of the regression analysis lie in the fact that the models that have too little complexity may prove to be inaccurate, and models with excessive complexity can be uninterpretable. Nevertheless, the proper application of this model will help managers to assess the situation properly and make cost-efficient decisions.
Keat, P., Young, P., & Erfle, S. (2013). Managerial economics: Economic tools for today’s decision makers (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
Langabeer, J., & Helton, J. (2015). Health care operations management. Burlington, MA: Jones & Bartlett Publishers.