In the modern era of technological advances, data is collected every day from different devices, applications, and systems for use in various industries, including healthcare. The collection of broad semi-structured, unstructured, and structured data is collectively referred to as big data analytics. With the availability of sources, such as data sensors, GPS devices, user-generated maps, data from computer systems, such as weblogs; social networks such as Twitter, Facebook, and Tumblr; Internet searches; and mobile data contents such as text messages, the healthcare sector is one among many that have been confronted with the need for managing these resources. Consequently, the health industry has developed big data analytics techniques and tools for handling these massive forms of heterogeneous data. Although in many facilities, big data is developed in-house, the current paper seeks to evaluate the broader scope of big data use, mainly to perform clinical functions for the improvement of patient care.
In recent years, predictive analytics has been identified as the primary element of business intelligence. In healthcare, prediction appraisal is facilitated by the use of big data. The process relies on the application of statistical approaches, such as machine learning and data mining, to evaluate historical facts and current information to forecast future outcomes. Roy (2016) confirms that machine learning is the most preferred technique in healthcare since it extracts data based on human understanding. Therefore, it identifies recurrent patterns and modifies the program to function appropriately. Within the hospital context, predictive techniques provide clinical decisions support by availing the right information to practitioners and care providers. For instance, doctors can use the information to determine patient information such as the risk of readmission for a recurring condition. Consequently, healthcare providers can utilize the data to make patient care decisions that guarantee favorable outcomes for individuals.
In healthcare informatics systems, accurate patient diagnosis and information are essential components that determine the best possible outcomes. Consequently, the accurate analysis of complex forms of data using big data influences the medical framework that is classified into five pathways, namely right living, right care, right provider, right innovation, and right value. Right living signifies a healthier life whereby a patient can make appropriate use of data mining to make better lifestyle choices to improve one’s well-being. Right care guarantees that all care providers will receive the same information on a patient; hence, the individual will be accorded the most suitable treatment available. The right provider ensures that healthcare givers obtain accurate information of clients based on data from sources such as socioeconomic data, public health statistics, and medical equipment. Accordingly, practitioners can perform a targeted survey to establish methods of identifying and availing improved treatment options for patients.
Right innovation recognizes the emergence of new treatments, medical conditions, and therapeutic techniques and confirms that they will continue to evolve. Similarly, advances in patient service delivery, such as useful research and development techniques and enhanced medical interventions will be identified as new forms of promoting health and well-being. Systems such as the national social insurance system allow for the pathway to detect the information. Lastly, the right value guarantees that patients receive the best available services based on their social insurance system. To ensure the appropriate use of data, specific measures can be utilized, including recognizing and destroying data waste, manipulating and misinterpreting data, and improving resources.
The Hadoop ecosystem is a function of big data that is utilized to store reliable, cost-efficient, and accessible data using a java based file system. With the need for digitization of unstructured data, the Hadoop system is used to obtain meaningful information such as clinical research and procedures, and patient care. In the healthcare sector, the Hadoop ecosystem is available for use in various capacities such as hospital network, monitoring of patient’s vitals, and healthcare intelligence. According to Kumar, & Singh the NoSQL database, a function of Hadoop ecosystem, is used in numerous hospitals to collect real-time client information from sources such as payroll and finances to classify an individual as high or low-risk patients. The Hadoop Distributed File System (HDFS) contains particular components such as Spark, Hive, Impala, Flume, and HBase frameworks that are utilized in numerous hospitals to monitor patients’ vitals including blood sugar levels, blood pressure, heartbeat per minute and respiratory rate. To complete these functions, the HDFS components transform massive amounts of unstructured data that is then produced by sensors. Likewise, Hadoop systems, namely MapReduce, Hive, and Pig technologies generate data on symptoms, disease, medicines, geographic regions, and healthcare opinions to mention but a few. The information is useful for insurance companies and healthcare intelligence usage.
Given this information, it is evident that there is a high demand for data analysts in the healthcare sector primarily because many hospitals are relying on big data to collect and retrieve information to enhance patient care delivery. Service delivery, improving the quality of lives of people, and working closely with people have been the guiding aspects for the career path that I would like to choose. Hence, working as a healthcare provider is my ideal career, particularly, in the role of a registered nurse. In my opinion, nurses work closely with patients as compared to other healthcare providers. Therefore, they can affect significant change in patients through clinical practices and utilization of theories such as humanistic nursing. In the future, I would like to ascend to the role of the nurse leader, where I will serve clients in various capacities, including the implementation of advanced research-based care practices. Moreover, I will be able to mentor other nurses in professionals and inspire patient-centered practices.
Advances in technology have led to the inception of big data analytics that facilitates the collection, storage, and appraisal of structured, semi-structured, and unstructured data. Students attending college or university, unfortunately, don’t get information on big data in health care fast enough. Not many of them are taught to do analytical research in this field. While essay writing remains the key priority for professors, Nursery students will stay behind. If you have a choice to write some papers or attend additional classes in big data and related disciplines, I recommend to pay someone online for essays and focus on new information. I have chosen writemypaperhub.com among the professional services, and you can choose a company that suits you most. Ask for help when essay writing is not your priority, and pay more attention to advancement in technology. With sources such as GPS devices, computer systems such as for weblogs, internet searches, and social networks such as Facebook and Tumblr, various industries can access pertinent information that can be used to complete business functions. One such sector is the healthcare sector that has incorporated big data in its operations, such as electronic health record keeping and predictive analytics. Besides, big data classifies health information into five pathways namely right living, right care, right provider, right innovation, and right values to obtain accurate patient information, ensure a patient receives best possible treatment, and give clients worth in terms of care delivery. Moreover, the invention of the Hadoop ecosystem concerning healthcare has led to the use of big data analytics in varying capacities like monitoring patients’ vitals, hospital network, and healthcare intelligence. Consequently, it is notable that big data not only collects, stores, and retrieves vital data, but also it has improved service delivery in the healthcare sector.