Importance of Statistical forecasting
Statistical forecasting plays a major role in decision analysis especially in the quantitative healthcare decision analysis process as it helps to anticipate changes that are likely to occur in the future as well as how similar issues were resolved in the past. A major importance of statistical forecasting is that it acts as the basis of planning. Through it, healthcare institutions can generate a planning process to determine what actions are deemed necessary in specific situations given the right conditions. Since management is unable to see into the future, forecasting creates information that makes it easier to anticipate possible outcomes and engage in effective planning.
Forecasting further plays the role of promoting organizations if well implemented. Since the activities performed within an organization are designed in such a way to allow accomplishment of set objectives, forecasting ensures that the appropriate time and effort is spent on specific activities to ensure they yield the expected outcomes (Sharma, 2020). Through statistical forecasting, the healthcare industry can collect data about likely outcomes in the future and then position itself to benefit from them, thus promoting the organization.
Although statistical forecasting has its benefits, its success is greatly determined by how well it is implemented and this can be achieved following a set of steps.
- Developing the basis
The healthcare organization must start by conducting a systematic investigation to collect information regarding the health industry, the products present, the state of the economy and other factors that could affect the success of the healthcare institution (Abraham & Ledolter, 2019). The information can then be used to determine what operations will have the desired outcomes.
- Estimation of future operations
The information collected through the systematic investigation conducted is then passed on to the management. The information is of great significance as it offers insight on the state of the industry and the economy, all of which can be used by the management to come up with quantitative estimates of what the future scale of business operations for the healthcare institution might look like (Athanasopoulos & Hyndman, 2018). The data collected, together with the planning premise helps in making forecasts of what operations are likely to achieve the expected results.
- Regulation of forecasts
Here, the manager uses the information available regarding actual operations and compares them against the forecast created from the data collected (Athanasopoulos & Hyndman, 2018). This process is crucial as it helps to identify any operations that may deviate from the forecast, the reason behind the deviation, and what needs to be done in order to resolve it.
- Review of forecasting process
Lastly, the management examines the various procedures used and engages in activities aimed at making improvements through forecasting.
Success also relies heavily on the techniques of forecasting used. Although research is yet to identify a universally applicable method of forecasting, there are various methods that have promising results on different organizations. For the healthcare industry, health institutions can opt for a single method of forecasting or combine various methods and use them alongside each other.
- Historical analogy
The method relies on information about analogous conditions that took place in the past. a new medical facility of organization for instance can collect information about a similar, but more advanced organization in the same field (Tan & Sheps, 2019). The developed institution’s history will offer information on the challenges and opportunities present during its early stages as well as steps taken to reach the position it is now.
Other than assessing other organization, the historical analogy approach also focuses on the organization history itself. Information on customer complaints, proposed innovations and other data can help to look back over historic events and determine an appropriate course of action to take in future.
- Survey Method
Surveys can be used to collect information on past information, suggestions from employees, customer preferences and other information that is needed for statistical forecasting. The method is ideal in that it does not only rely on past information but also current occurrences as well as likely future outcomes. Another advantage is that, through surveys, the information collected is not only as a result of what is anticipated to occur in future, but also what customers want (Athanasopoulos & Hyndman, 2018). For a healthcare institution, surveys can give insight on the challenges that exist in the institution as well as what the caregivers and patients think would be a possible solution. The information thus makes it easier to come up with forecasts that are designed for the specific organization and not all health institutions in general.
Surveys can be conducted to gather information on the intentions of the concerned people. For example, information may be collected through surveys about the probable expenditure of consumers on various items. Both quantitative and qualitative information may be collected by this method.
- Opinion Poll
The approach often involves a panel of professional discussing a certain topic. Their input is considered relevant as, being professional, they have the information and the experience to make credible conclusions. The panel can also discuss opinions from other professionals and this helps to do away with inaccurate information (Sharma, 2020). In a medical setting, opinion polls can help to enhance the quality of service by determining what forms of technology to incorporate, how to go about entertaining patients in waiting lobbies and also how to improve overall patient satisfaction.
References
Abraham, B., & Ledolter, J. (2019). Statistical methods for forecasting. New York: Wiley.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice ; [a comprehensive indtroduction to the latest forecasting methods using R ; learn to improve your forecast accuracy using dozenss of real data examples.]. Lexington, Ky
Sharma, P (2020) “Forecasting: Roles, steps and techniques in the management function” retrieved from, http://www.yourarticlelibrary.com/management/forecasting/forecasting-roles-steps- and-techniques-management-function/70032
Tan, J. K. H., & Sheps, S. B. (2019). Health decision support systems. Gaithersburg, Md: Aspen Publishers.
