Utilization of Big Data in Healthcare Field

Big data is slowly but surely gaining its popularity in healthcare. It is anticipated that big data will bring evolutionary discoveries in regard to drug discovery research, treatment innovation, personalized medicine, optimal patient care, etc. In turn, this will lower the cost of healthcare and enhance patient outcomes.

Big data helps in infectious disease surveillance. For instance, Peddoju, Kavitha, & Sharma, (2017) discuss use of big data in pneumonia monitoring. In order to prevent complications in children that can result from pneumonia, it is important to identify the symptoms and to provide the appropriate treatment fast. The cloud will connect all the doctor’s information, and when the patient’s symptoms/diagnosis/treatment are entered and stored in cloud, cloud computing then can securely share this information with other providers. This will enable other providers to access these data, which will give them better suggestions on how to diagnose and treat other children with pneumonia.

Big data also helps in chronic disease management. Poorejbari, Vahdat-Nejad, & Mansoor (2017) discuss use of cloud computing in monitoring diabetes patients and enhancing their quality of life. Patients with a history of diabetes type 2, who are not feeling well, should check blood glucose, blood pressure, and heart rate at home. Sensors that collect these data then direct data to home context manager, which will notify the patient via smart devices about the high-risk factors, adequate solutions, and treatments. All of the patient’s measured parameters are stored in a diabetes management system, so in the case that the patient required medical attention, the provider will be able to access it in cloud and use it in supporting any medical decision.

Big data is also being utilized 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-related data within the care continuum enables providers to predict patient’s clinical, financial, and social risks. Patients should be grouped based on demographics, vital signs, laboratory results, progress notes, problem lists and diagnosis, procedure codes, allergy lists, medication data, etc. All these parameters can be helpful in predicting and managing patients’ outcomes.

Big data is used in healthcare to fight opioid use. Big data helps providers and public health officials to utilize behavioral analytics in order to recognize and manage risk factors for opioid use among their patients. In addition, data sets are used for tracking prescription drugs and patient outcomes in order decrease the number of unnecessary prescriptions. Also, patients who had multiple surgeries and had been using opioid prescription during recovery will be monitored closely, since they have a greater chance of becoming addicted to opioids. Therefore, providers and public health officials propose that “combining medical records with patient behavior and history to determine risk factor using big data tools, ” will be of a great assistance in fighting opioid use.

Big data analytics is also used in mental health management, where it plays an important role in “mining hidden behavioral and emotional patterns in messages, or “tweets, ” posted on Twitter” by users. This helps mental health providers to “detect a disease-related emotion pattern” and it helps them talk to their patients who suffer from depression and understand their emotions and thoughts better, but it can also help them predict the probability of developing some psychological conditions in the future.