The original concept of a Decision Support System (DSS) defined it as a computerized supporting supplement for decision-makers. It serves to gather and analyze large amounts of data to expand the capabilities but not to replace the course of the initial decision. Such a system can be easily programmed for user needs and output data in graphical form, for example, to forecast a company’s future income. Business Intelligence (BI) is a generalized term that is used to describe a combination of analytical tools, methodologies, and applications. (Sharda 2019). DSS originated from BI, but in fact, remains the most used system at the moment. Reporting plays a huge role in the use of Business Intelligence, according to which the user must determine for himself whether a particular situation deserves attention, and only then can apply analytical methods.
The key difference between these two definitions is that DSS is a computerized system for obtaining information that helps make decisions in an organization or business community. At the same time, BI acts as a tool that uses various programs that help in organizing and managing data or valuable information within an organization. While DSS helps the user detect errors existing in the program and analyze information for decision-making, BI helps with automatic analysis and, therefore, gives suggestions that have only the implementation stage left. Another difference is that DSS requires more time to start implementation since the processes mostly involve manual processing. BI requires less time to complete all functions since the computer thinks for itself and therefore does not require manual control.
Analytics can be considered a process of developing practical solutions or recommendations for actions based on information obtained from historical data. The meaning of the word analytics has seriously expanded and no longer serves to designate individual components of computer technologies previously available under specific labels. (Sharda 2019). Today, analytics is a combination of technology, scientific methods, and statistics combined to solve specific problems. Entire industries resort to data analytics to develop reports on what is happening and predict future consequences. Almost all managers begin to understand how important the analytical component is for developing their field and turn to technological systems to simplify these processes.
In the domain of healthcare, analytics covers a wide range of applications, from diagnostics to effective fraud prevention. One of the examples is an analytic approach to the falls of the senior population. Wounds received by falling reduce the number of the elderly population and become a crucial problem for the group the population over 65 years of age. Accordingly, such a situation significantly increases the government’s costs for treating the elderly. According to the statistics, the direct costs of falls were estimated at $34 billion in 2013 alone. (Sharda 2019). At the moment, falls are one of the leading factors of both fatal and non-fatal injuries in people over 65 years old. It is also worth noting that they increase the risk of disability in this population group by up to 50 percent. Currently, there is no strict identification methodology for identifying a group of people prone to injuries from falls since this statistic cannot be as objective as cancer or diabetes mellitus. Nevertheless, such analytical studies have helped raise awareness of the problem and take concrete steps to reduce the risks of falls (for example, increased utilization of physical therapy services).
Reference
Sharda, R., Delen, D., & Turban, E. (2019). Analytics, data science, & artificial intelligence: Systems for decision support (11th ed.). Pearson Education Limited.