Big data is slowly but surely gaining popularity in healthcare. Big Data is expected to bring scalable discoveries in drug discovery research, therapeutic innovation, personalized medicine, optimal patient care, etc. This in turn will reduce the cost of healthcare and improve patient outcomes. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essayBig data helps in infectious disease surveillance. For example, Peddoju, Kavitha, and Sharma (2017) discuss the use of big data in pneumonia surveillance. In order to prevent complications that may arise from pneumonia in children, it is important to identify the symptoms and promptly provide appropriate treatment. The cloud will connect all the doctor's information, and when the patient's symptoms/diagnosis/treatment are captured and stored in the cloud, cloud computing can then securely share this information with other providers. This will allow other providers to access this data, giving them better suggestions on how to diagnose and treat other children with pneumonia. Big data also helps in the management of chronic diseases. Poorejbari, Vahdat-Nejad, and Mansoor (2017) discuss the use of cloud computing to monitor diabetic patients and improve their quality of life. Patients with a history of type 2 diabetes who are not feeling well should check their blood sugar, blood pressure, and heart rate at home. The sensors that collect this data then direct it to the home context manager, who will inform the patient via smart devices about high risk factors, adequate solutions and treatments. All measured patient parameters are stored in a diabetes management system. So, in case the patient needs medical care, the provider will be able to access it in the cloud and use it to support any medical decisions. Big Data is also used. in population health management. In this case, big data is used to group patients based on “identified characteristics so that each can be treated based on individual risk profiles.” Collecting patient data across the continuum of care allows providers to predict patients' clinical, financial, and social risks. Patients should be grouped based on demographics, vital signs, laboratory results, progress notes, problem and diagnosis lists, procedure codes, allergy lists, medication data , etc. All of these parameters can be useful in predicting and managing patient outcomes. Big data is being used in healthcare to combat opioid use. Big data helps providers and public health officials use behavioral analytics to recognize and manage risk factors for opioid use in their patients. Additionally, data sets are used to track prescription medications and patient outcomes to reduce the number of unnecessary prescriptions. Additionally, patients who have had multiple surgeries and used an opioid prescription during recovery will be closely monitored, as they are at greater risk of becoming addicted to opioids. Therefore, providers and public health officials suggest that “combining.