Wondering how are statistics used in healthcare? On this page, you will find out how the public health sector collects and analyzes statistical data, including using machine learning algorithms. One of the applications of such a method is processing large amounts of data and making predictions. Additionally, the text highlights some challenges and opportunities in using statistical data in healthcare.
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Data and its’ interpretation are vital for statistical application in the field of health care. Nowadays, healthcare systems carry a significant amount of data on patients, including their demographics, treatment plans, methods of payment, and insurance information. This essay will describe the standard application of statistics in health care, and discuss its significance and utilization of statistical knowledge in nurses’ work in a hospital.
The healthcare system’s information on the patients, treatment programs, and payment process could be systematized and organized in the form of statistics. Statistics health care data could be applied in a number of various cases, like providing a view of cancer statistics and how they relate to specific demographics or machine learning algorithms that predict diabetes diagnosis (Islam et al., 2018). The commonly used statistical models are now also used in the machine learning process.
Machine learning helps with the fast analysis of a massive amount of data that humans cannot process. For example, the popular algorithm used by Netflix and Amazon that compares the customers and recommends movies and books for customers that expressed similar interests was used to predict patients’ responses or risk of disease (Islam et al., 2018). The method introduces a new approach to the patients’ personal care, and it is not the only way in which machine learning could be helpful. Machine learning is used as an alternative to costly diagnostic methods for Coronary Artery Disease (CAD), and the results’ level of accuracy is almost equal (Islam et al., 2018). The application of machine learning to Emergency Care is used in event stimulations to improve the Emergency Department operations and provide effective communication and flow in staff groups.
The ability for machine learning appeared as a consequence of a large amount of collected clinical data from the last years. The combination of large amounts of health care data with faster computers further resulted in the form of deep-learning models, which could, for example, detect diabetic retinopathy from retina photos without human interventions (Beam et al., 2018). The process of data collecting includes not only the health care records but also a variety of sources like fitness trackers, genetic testing, etc. Considering nurses’ work in a hospital, statistical knowledge is essential and is utilized frequently in the work process. The data is obtained through interviews with patients; moreover, apart from the facts like age and blood type, nurses ask the patients to provide information about their health habits and close relatives’ health history. The collected data is used to ensure a correct diagnosis and impacts the
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