Big Data is slowly but surely gaining popularity in the healthcare industry. Big data is expected to bring evolutionary breakthroughs in drug discovery research, treatment innovation, personalized medicine, optimal patient care, etc. In turn, this will reduce healthcare costs and improve patient outcomes. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essayBig data helps in the surveillance of infectious diseases. For example, Peddoju, Kavitha, and Sharma (2017) discuss the use of big data in pneumonia monitoring. To prevent complications that can arise from pneumonia in children, it is important to identify symptoms and provide appropriate treatment quickly. The cloud will connect all the doctor's information, and when the patient's symptoms/diagnosis/treatment are entered and stored in the cloud, cloud computing can then securely share this information with other providers. This will give other providers access to this data, which will give 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 in monitoring diabetes patients and improving their quality of life. Patients with a history of type 2 diabetes who feel unwell should check their blood sugar, blood pressure, and heart rate at home. Sensors collecting this data then direct the data to the home context manager, who will alert the patient via smart devices about high-risk factors, appropriate solutions and treatments. All measured parameters of the patient are stored in a diabetes management system, so in case the patient requires 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 according to individual risk profiles”. Collecting patient data across the care continuum allows providers to predict patient clinical, financial and social risks. Patients should be grouped by demographics, vital signs, lab results, progress notes, problem and diagnosis lists, procedure codes, allergy lists, medication data, etc. All of these parameters can be useful for predicting and managing patient outcomes. Big Data is 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 among their patients. Additionally, the datasets are used to track prescription drugs and patient outcomes to reduce the number of unnecessary prescriptions. Additionally, patients who have had multiple surgeries and used prescription opioids during recovery will be monitored closely, as they are more likely to become addicted to opioids. Therefore, providers and public health officials propose that “combining medical records with patient behavior and medical history to determine risk factors using big data tools” will greatly assist in the fight against opioid use. Here.