Big data has piqued the imagination of many people over the past ten years due to the opportunities it holds. The term “big data” in health care encompasses a broad range of health data collected from many sources, such as medical imaging, electronic health records (EHRs), genetic analysis, payor data, drug research, medical devices, and wearables (Kulkarni, 2020). It differs from standard computerized medical and patient health data utilized for decision-making in three ways. That is, it is vast, information travels quickly and covers the different areas of health care, and is quite diverse in architecture and qualities (Stinson, 2018). Big data provides health care professionals and organizations with opportunities to revolutionize health care services and provide tailored treatment by integrating biological and health care data.
Big Data for Timely and Targeted Care
Big data will significantly influence health care because of its ability to aggregate data on human health from different sources, allowing health care professionals to be precise when assessing and managing patients. Patients can benefit from big data analytics by receiving more tailored and focused treatment. According to Kulkarni (2020), evaluating health files, laboratory data, wearable data, medical applications, genome testing, and demographic statistics can reveal clues about patients that allow physicians to devise accurate treatment plans. For example, researchers are using bid data analytics to incorporate genomic data into modern clinical practice (He et al., 2017). Practitioners can choose the optimal line of therapy for patients immediately using genetic sequence insights, saving patients money on unneeded testing and procedures.
In the United States, acute myocardial infarction (AMI) is among the leading cause of mortality. The condition arises when blood supply to the cardiac muscle is interrupted, resulting in heart muscle injury or even death (Alexander & Wang, 2017). Consequently, evidence suggests that the health care expenses connected with individuals who have been diagnosed with the disease may be decreased using new techniques for processing datasets and more efficient, individualized therapeutic strategies like bid data analytics (Alexander & Wang, 2017). The more individualized the care, the less expensive and more satisfied the patient becomes.
Big Data for Combatting Health Mysteries
Big data analysis can provide health care practitioners with crucial insights regarding the essence of chronic illnesses such as sepsis. Sepsis is the major driver of hospital admissions in the United States annually, with an estimated yearly cost of over $25 billion (Lindholm & Searle, 2016). Researchers throughout the U.S. can harness big data using inventive techniques to assist health providers in diagnosing and effectively managing sepsis with more consistency as this problem is threatening America’s health care sector.
Similarly, scientists are leveraging machine learning and big data to develop real-time alert technologies that can support medical practitioners in making quick diagnoses and providing better care. The technologies can interpret sepsis patterns by utilizing artificial intelligence algorithms, notably computer applications that monitor and find trends in data and learn from them (van Wyk et al., 2017). Predictive intelligence can alert health personnel to the possibility of sepsis when this knowledge is cross-checked with actual telemetry information from the client’s treatment process.
Big Data Analysis for Preventative Care
Another way that health care providers can use big data for significant impact is by looking retrospectively at discharge data. Providers can gather anonymous data to analyze thousands of patients discharged from health care facilities for a particular period (Kulkarni et al., 2020). The results can be used to illuminate the state of hospitals or clinics and the efficacy of their services. Caregivers can use insights from such data in the U.S. to improve their treatment approaches, specifically in emergency departments. In one big data study, hospital medication errors were attributed to frequent symptoms, an increased rate of false-positive test outcomes, and inadequate public knowledge (Muthu et al., 2020). In other words, an increase in the volume of health care data raises the need for a more reliable, precise, and low-cost illness prevention solution.
Therefore, medical big data processing adds to a better understanding of patient demographics at the highest risk for sickness, allowing for a preventative strategy for treatment. As such, medical big data analytics can help discover isolated cases of individuals utilizing considerably more health services than usual. Big data analysis can help uncover practices and procedures that provide subpar outcomes or whose expenses are high in comparison to the success rates (Kulkarni et al., 2020). It may be deployed to teach, enlighten, and urge people to take charge of their health. Combining clinical and financial data makes it possible to emphasize the efficacy and efficiency of therapeutic interventions.
The most evident advantage of big data utilization is its support in observing patients’ health using health devices and IoT. According to Kalid et al. (2017), big data is essential for remote patient monitoring. For instance, clinicians may conduct procedures despite being geographically separated from the patient using high-velocity real-time data and robots. Big data is essential in robotic surgical procedures and increase early assessment, telemedicine, and virtual nursing support. Big data combined with telemedicine can enhance the experiences of patients and physicians in ways. Some of the benefits are: Clients can avoid long queues, clinicians do not spend time on pointless appointments or documentation, and patients can be tracked and counseled at any location, irrespective of time or distance. Additionally, hospital treatment and re-hospitalization avoidance, practitioners can forecast urgent medical occurrences and stop patients’ situations from deteriorating, and telehealth helps to cut expenses while also improving service quality.
Big Data Limitations
Big data has a huge potential to enhance clinical practice in the United States. Accessibility to and monitoring patient data from all over the world, it is assumed, will result in the creation of complete discoveries that could drive better clinical and community health effects worldwide (Muthu et al., 2020). There are always several limitations that prevent big data from reaching its maximum capabilities. Conversely, in a world where health care is a profitable venture and the U.S. care system is built on the capacity, the rivalry between medical facilities, research institutes, and clinical experts can lead to rejection or unwillingness to exchange patient data (Canaway et al., 2019). Furthermore, even if there was no rivalry, the profusion of multiple electronic health records (EHR) systems has led to cross-platform interoperability difficulties. Since there is no standardized format for collecting EHR data, exchanging data frequently necessitates the involvement of third-party contractors, which is expensive for practitioners and unethical when exchanging confidential patient records.
Big data in nursing practice improves treatment, capabilities, and funding, resulting in technologies that enhance the patient experience. However, it will necessitate collaboration and innovation among stakeholders, including practitioners, insurers, drug companies, government and legislators, and research and scientific groups. A collaborative approach would enable these stakeholders to rethink the quality and construction of their processes (Stinson, 2018). They must provide the digital architecture needed to store and consolidate huge amounts of health care data. More importantly, they must spend on human capital, including I.T. specialists, data architects, scientists, and machine learning engineers, to lead Americans into this promising and fascinating realm of population health and wellness.
Finding experts with knowledge in statistics, information science, or informatics is still a complex undertaking at the moment. Currently, there are few experts who are well-versed with the skills needed to collect and analyze big data from health care. A uniform process for data input must be in effect to ensure that all data input is unified by the people in charge of data entry (Stinson, 2018). A standard format for data collection and storing will ascertain that health care organizations have consistent data even if they change their data entry experts.
As previously stated, big data in health care comprises a wide variety of health data gathered from multiple sources. It must be properly managed and analyzed to get useful information from this data. Otherwise, searching for a solution through studying vast data is akin to looking for a path in a maze. All phases of processing large data come with their own set of obstacles that can best be overcome by adopting powerful computational technologies for big data analytics. Thus, to deliver appropriate remedies for enhancing health outcomes, medical practitioners must be adequately outfitted with the necessary infrastructure to collect and interpret big data in an organized manner.
Alexander, C. A., & Wang, L. (2017). Big data in health care: A New frontier in personalized medicine. Am J Hypertens Res, 1(1), 15-18. Web.
Canaway, R., Boyle, D., Manski-Nankervis, J. A., & Gray, K. (2022). Identifying primary care datasets and perspectives on their secondary use: A survey of Australian data users and custodians. BMC Medical Informatics and Decision Making, 22(1), 1-19. Web.
He, K. Y., Ge, D., & He, M. M. (2017). Big Data Analytics for Genomic Medicine. International journal of molecular sciences, 18(2), 412. Web.
Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., & Muzammil, H. (2017). Based real time remote health monitoring systems: A review on patients prioritization and related “big data” using body sensors information and communication technology. Journal of Medical Systems, 42(2), 30. Web.
Kulkarni, A. J., Siarry, P., Singh, P. K., Abraham, A., Zhang, M., Zomaya, A. Y., & Baki, F. (2020). Big data analytics in health care. Springer.
Lindholm, C., & Searle, R. (2016). Wound management for the 21st century: Combining effectiveness and efficiency. International Wound Journal, 13 (2), 5–15. Web.
Muthu, B., Sivaparthipan, C. B., Manogaran, G., Sundarasekar, R., Kadry, S., Shanthini, A., & Dasel, A. (2020). IOT based wearable sensor for diseases prediction and symptom analysis in health care sector. Peer-to-peer Networking and Applications, 13(6), 2123-2134. Web.
Stinson, C. (2018). Healthy data: Policy solutions for big data and A.I. innovation in health. Mowat Centre for Policy Innovation.
van Wyk, F., Khojandi, A., Kamaleswaran, R., Akbilgic, O., Nemati, S., & Davis, R. L. (2017). How much data should we collect? A case study in sepsis detection using deep learning. In 2017 IEEE Health care Innovations and Point of Care Technologies (HI-POCT) (pp. 109-112). IEEE. Web.