Statistics and Statistical Forecasting Process In Healthcare

Statistical Forecasting Process

Forecasting process involves a number of steps that need to accomplish. These steps assist in broadly define and identify the tools and need of health forecasting. According to Wright, Lawrence and Collopy (1996), healthcare forecasting process requires the use of framework containing dynamic process. The process involves seven main steps. The first step involves identifying the ideas and concepts which address a significant health condition which is of significant cost and great burden to the health care service.  This offers a precise health outcome specification to be forecast and a clear forecasting horizon of definition. The second step entails the use of literature to acknowledge highly correlated and casual variables which are related with the identified health results measures in step 1 (Ganguly & Nandi, 2016). The third step involves data sources identification for both measures of health outcome and all of the possible predictors, and ascertaining the completeness and availability of data. The fourth step involves preparing the sets of data for primary statistical analysis that include descriptive patterns and the forecast algorithms development. Some primary activities include data management and cleaning, and the supplementary variables generation for further analysis. Step five involves the generation of predictive models and their validation by use of various sets of same historical data. The sixth step entails determining and evaluating the final indicators lists required for great predictive model founded on the practical access to other data. The final step involves developing tailor-made and very unique health forecast services for unique client or purpose and for to update the model periodically (Ganguly & Nandi, 2016).

Example of Statistical Analysis Tools Used for Forecasting

Techniques of forecasting can be groups in two extensive groups that include qualitative and quantitative. Quantitative techniques are highly used in healthcare organization. These techniques involves mathematical models that include neuro network, moving average, expert system, regression, straight line projection, simulation, and exponential smoothing among others. Some of the forecasting tools that will be used to enhance data analysis in healthcare system include a time series. Time series refer to gathering of sequentially measured observations over time. Thus, time series forecasting is thus a statistical model method or tool where the same variables past observations are analyzed and collected to create a model describing the underlying relation (Ganguly & Nandi, 2016). There are excess of techniques to time series modeling which include traditional statistical methods which comprise of Autoregressive Integrated Moving Average (ARIMA), exponential smoothing, and moving, or nonlinear complex models that include the Autoregressive Conditional Heteroscedastic (ARCH) model, Threshold Autoregressive (TAR) model, and bilinear model. Nevertheless, as a result of its implementation and understanding simplicity, the linear models popularity as an applied tool has exceeded its nonlinear counterparts by far. The time series essence lies in the reliance of the adjacent observation and the analysis of time series is concerned with the dependence analysis and can offer foundation to various managerial decisions (Ganguly & Nandi, 2016).

Another tool to be used in forecasting analysis is the Quantile Regression Model (QRM). Quantile regressions refer to linear-regression models extensions that fail to assume the dependent variable normality. They model the provisional quintiles as predictors’ functions, specifying modifications in any provisional quintile. Contrary to linear regression models, QRMs contain the aptitude to typify the association between the independent variables and dependent variable, especially in the distribution extremes. QRMs contain common medical reference charts application, and could be utilized in initial medical diagnosis to acknowledge uncommon subjects by offering robust regressions for approximating extreme values. Quantile Regression Models in addition have the ability of forecasting and predicting extreme chronic health condition such as asthma. Some of the possible predictions include rate of admission (Soyiri & Reidpath, 2013).

The third tool is the fractional polynomials models (FPM). FPM is a probabilistic method that can be used for forecasting extreme health conditions or situations.  FPM is also said to be employed in modeling particular dependent variables categories in a linear data distribution, and therefore, target particular groups more accurately. FPM is used in categorization which provides clear advantages since it permits a full non-linear relationship representation between outcome and predictor variables. This model can be extended to an extensive range of health conditions and situations (Soyiri & Reidpath, 2013).

Role of Statistical Forecasting in the Qualitative Healthcare Decision Analysis Process

Health forecasting involves foretelling health situation of forewarning future events and disease episodes. It can as well be regarded as a kind of preventive care or preventive medicine which involves planning in public health and is focused as facilitating provision of healthcare services in populations. Health foretelling has been frequently applied to visits in emergency department, admissions, and daily attendance in hospitals. Forecasting is a crucial component in the medicine practice with its chief purpose being to improve both individual patient outcome and provision of health service. For instance some forecasting models integrate rule-based model which predicts threats based on environmental situations, with an anticipatory intervention care to offer information that is then communicated. This service allows care providers and patients to take precautions in enhancing delivery of health service and reduction of disease events. Healthcare forecasting is based on four main principles that include the focus, measuring of errors and uncertainty, healthcare forecasting horizon and the data aggregation nature and how it impact accuracy.

Forecasting start with identification of healthcare problem, and gathering of the necessary primary and secondary data. Data collection is done accurately using accredited tools and by certified researchers to ensure high level of accuracy, validity and reliability. The data is effectively analyzed mostly using quantitative research methods. The analysis results provides clear picture of the situation at hand. Predictive model validated with historical. Once validated, these models are used to make prediction of what may happen in the future if the situation is not harnessed. The models are also used to determine what may happen when different possible solutions are adopted. Based on the prediction results, the healthcare providers are able to make a decision on the best measure to take to enhance health care performance. This implies that statistical forecasting provide clear information of the situation on the ground and give possibilities of events that may follow when various measures are employed. They therefore provide a guide in decision making process (Wright, Lawrence &Collopy, 1996).

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