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Advanced analytics is a data analysis process that applies to machine learning, deep learning, business process automation, and other statistical methods to analyze clinical and business processes information from various data sources. Advanced analytics does not only use in traditional data analysis but is also used to predict events. This future prediction seems to be significantly crucial for correct decision-making in health care organizations. Advanced analytic is the most valuable resource to different health care setups because it can evaluate some of the more common problems that traditional systems cannot do (Hanna et al., 2021). For instance, when are patients or their families most likely to exhaust during patient’s procedures? What type of new value propositions could be suitable from the business perspective?
No doubt, in the modern era, every health care organization needs long-term endeavors for improving quality and performance. Like other fields, health care analytics has upgraded, and it is divided into several components such as statistical, visualization, data profiling, etc. Data visualization presents data in a graphical format that makes data analysis more understandable and accessible across organizations. As an administrator, I would use this application to generate revenue by introducing new services in my department. For this purpose, we would need an apparent collaboration with the data analytics department. The data analytics specialist will use our present data and predict our future services revenue through a graphical format that would be helpful to present in front of higher management.
Furthermore, population health management is one of the essential applications of health care analytics. Because health care organizations do not have only aim to improve the internal services but also have the fundamental responsibility to prevent the communities from infections. It could be possible through extensive data management of specific diseases and infections. For example, after COVID-19, most healthcare organizations implemented advanced data analytics systems to prevent communicable diseases in society. Similarly, clinical decision support is another tool of health care analytics. In other words, the analytics system plays a vital role in organizations’ decision-making. Previously, incorrect data was a significant barrier for health care to make correct decisions because due to doubtful statistics, the direction was unclear.
Finally, as an administrator, it is necessary to properly implement a health care analytical system. We should hire specialist persons who can run this system. Moreover, we should introduce an advanced analytics system where technical persons make other employees trained to access this system easy to better services and health status.
Advanced and predictive analytics surrounds the idea of identifying many different approaches and techniques to implementing an appropriate prediction model for health care organizations. Data mining which is similar to predictive analytics has a strong predictive component. They are similar because they both entail sophisticated mathematical and statistical approaches necessary to analyze large sums of data. Data mining is able to take large data volumes and turn it into useful patterns that show relationships to the data. As an administrator, I would use data mining to make a large set of data that does not have an identifiable trend, show a useful trend that would have a positive impact on healthcare. An example would be looking at how the average weight of patients over time has changed and correlating that to certain diseases to see if there is a noticeable trend.
Clinical decision support is the process of providing evidence to support decision-making by clinicians. This looks like providing real-time information and insight and predicting likely patient outcomes. Fraud prevention is another application of advanced analytics that uses algorithms to find patterns in past data that are identifiable as fraudulent behavior. This flags the suspicious activity before any financial burdens (Strome, p. 186, 2013). In order to make a healthcare organization an analytical healthcare organization, it is valuable to choose and implement an appropriate model, evaluate the model’s performance, and then finally deploy the solution. From doing this the organization gains insight on how to be a better performing organization with quality improvement at the forefront of the initiative.
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