Foundation Verulava Maglakelidze 2017 p 147 For hospitals labeled as safety net

Foundation verulava maglakelidze 2017 p 147 for

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Foundation) (Verulava, & Maglakelidze, 2017, p. 147). For hospitals labeled as safety-net hospitals, the burdens of uncompensated or charity care are higher than others. Safety net hospitals serve large uninsured and under-insured patient populations. Owing to the top financial burden in safety-net hospitals, many in recent years have closed or reconstructed to survive. According to Adler-Milstein et al. (2015), two-thirds of all hospitals are part of the safety net, while 25 % are uncompensated care in the United States (p. 2176). In the past two years, four hospitals in Georgia have closed due to high levels of uncompensated care. The most recent on is the Lower Oconee Community Hospital in South Georgia, a 25-bed critical hospital. CEO Karén O'Neal said that the hospital was experiencing significant difficulties with the cash flow, mainly due to the 23% uninsured population in the area and hopes it will be open again as an emergency care center (Verulava & Maglakelidze, 2017, p. 150). This is the case around Georgia, where high levels of uninsured without access to a primary care physician come to the hospital for services, burdening the available time and resources of these hospitals. Supporting Research The Big Data technology in health care today is targeted towards high-level and fast-track treatment. For example, more attention should be paid to the quality of care carried out following health changes in the United States. Due to big- data analytics and computing technologies, doctors are paid for the skills to cure diseases of patients quickly and keep their health in good shape and not for the treatment process.
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INFORMATION TECHNOLOGY 17 The analysis of big data is another critical approach to the use of big data expenses. Several studies draw on the detailed review of statistics, such as the number of chronically ill patients, grievances against individual doctors, second visit numbers, and disease indexes. The studies show how Big data plays a significant part in healthcare. Predictive Analytics and Quick Diagnosis In the Systematic Review of the Concurrence of Big Data Analytics and Healthcare, Mehta and Pandit (2018) describe how the demand for wearables has risen annually, and that it may amount to $25 billion by 2021. Wearables today are used for the study and submission to the personal physician of all data obtained. While there are no health problems for a tracked user, large quantities of data collectors help build versatile and evolving databases. Then the machine will make suggestions for each patient with artificial intelligence. As a result, neural networks can evaluate monitoring data and predict disease susceptibility, help patients find out and timely measurements. Thus, all illnesses and strategies for avoiding them can be accurate. Through a complicated examination, the doctor will predict for any patient's way of life the outcome of a particular treatment plan. Therefore, cases of big data healthcare contain mainly predictive analytics (p. 58). Chiefly, the use of wearables for the processing of medical data simplifies the way Big Data operates and opens a new space for its use.
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