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.
Exploring the Importance of Statistics in Healthcare
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.
The Use of Machine Learning in Diagnosis
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 process of decision-making in terms of treatment methods and the application of specific frameworks. The statistical knowledge used by nurses in day-to-day operations implies knowledge of health risks for certain age groups and making decisions in treatments based on their effectiveness.
How Are Statistics Used in Healthcare
The evaluation of large amounts of data health care workers encounter, like patients’ previous use of medications, and earlier admissions to hospitals is eased by using machines to store the data and help with decision-making. The application of statistical data to health care provides quality health care services by ensuring that none of the valid information is missing. In the same way, the application of statistical data offers safety for the patients. The research shows that statistical data could be used to measure drug safety alert response and defect adverse drug reactions to ensure patient safety (Islam et al., 2018). In terms of health promotion, statistical data and machine learning could be improved in the future to develop cost-efficient evaluation procedures and interventions (Michie et al., 2017). Such techniques could positively impact the population’s health by increasing the speed and reach of the health care system and making health care accessible for vulnerable populations. The knowledge of statistical data and its precise application to various cases lays a foundation for a successful work process and gives the individual an opportunity to show his expertise and improve his leadership skills.
Conclusion – Application of Statistics in Healthcare
Summing up, the statistical data, knowledge, application, and interpretation in health care are crucial. The data is used to provide quality health care services and to ensure patients’ safety. Statistical data could be collected in various forms, from direct interviews with the patients to fitness trackers. To provide effective use of patient data, health care organizations might introduce electronic systems that include machine learning. Machine learning could be used in many ways, from predicting surviving chances and making a diagnosis in a shorter amount of time to improving the quality of simulation exercises for the staff. Statistical application to health care implies that none of the vital information would be dismissed from the case. The basic knowledge of statistical data helps nurses when making decisions on treatment procedures based on the previous data on effectiveness.
In conclusion, this essay explored the topic of statistical application in the health care field and discussed its significance and how it is utilized in daily hospital work. It should be noted that the recent movement towards the application of statistical data is only possible because of a large amount of collected and organized data from the last decades. Further data collection would only improve the situation by making the evaluation and intervention processes faster, more precise, and more accessible to all layers of the population.
References
Beam A.L., & Kohane, I.S. (2018). Big data and machine learning in health care. Journal of American Medical Association. 319(13), 1317–1318.
Islam, M., Hasan, M., Wang, X., Germack, H., & Noor-E-Alam, M. (2018). A systematic review on healthcare analytics: Application and theoretical perspective of data Mining. Healthcare, 6(2), 54.
Michie, S., Yardley, L., West, R., Patrick, K., & Greaves, F. (2017). Developing and evaluating digital interventions to promote behavior change in health and health care: Recommendations resulting from an international workshop. Journal of Medical Internet Research 19(6), e232.