Researching of Big Data Risks and Rewards

Data-driven analytics in the healthcare industry offers numerous potential benefits for improving medical services. According to Fat and Ramadas (2018), big data can be used to predict disease outcomes, prevent comorbidities, lower treatment costs, and manage patient records efficiently. Reduced recency bias and using real-time information offer advantages in terms of productivity and time, which, in turn, influence the quality of medical services. As Kraus et al. (2018) claim, “the adaption and integration of heterogeneous data sources have a major impact on the advancement of precision medicine” (p. 241). Further benefits of clinical big data analytics include identifying the right target group or health problem and observing patterns that can be helpful for intervention program development. Big data’s importance in healthcare is evident, and in many countries, strategies are implemented to maintain databases for disease treatment and management.

Nevertheless, along with potential benefits, using big data poses several challenges for clinical systems that need to be overcome. For instance, risks are related to data privacy and security since identifiable information can be stolen, hacked, and misused (Wang et al., 2018). Another challenge is data classification, and accommodation since searching for specific information can be laborious, and data needs to be compatible for effective use. Furthermore, algorithmic issues can be observed, along with the implementation of unified documentation (Kraus et al., 2018). As Fat and Ramadas (2018) state, when clinical big data analytics is adopted on the regional and global levels, it can impact “security, standards, language and terminology” (p. 2). Therefore, the challenges mentioned above need to be tackled to ensure a comprehensive approach to big data in healthcare.

Different strategies can be implemented by healthcare institutions to succeed in big data analytics in clinical settings. For instance, AI-based methods and machine learning techniques can refine and improve information processing. Natural language processing (NLP), neural networks, and other advanced tools can help the industry overcome the challenges related to data classification and accommodation (Wang et al., 2018). Furthermore, a proactive strategy aims at predicting future health-related risks and providing solutions. In particular, as Durcevic (2020) claims, business intelligence (BI) solutions and tools can predict the risk of diabetes and acute medical events. In the nursing field, big data can be used for establishing a structured knowledge system, which will help the practicing nurses expand and share their competencies for better healthcare provision.

The assessment of big data opportunities and risks in a clinical setting is crucial for improving analytical strategies. In particular, a unified system of clinical data can help nurses to make efficient decisions due to access to more information collected and stored due to a data-driven approach. According to Glassman (2017), “data and care quality go hand in hand,” and informatics competency allows nurses to manage knowledge and eliminate errors (p. 45). Another suggested strategy is implementing clinical big data analytics on several levels, such as IT infrastructure, organizational, managerial, and strategic fields (Ngiam & Khor, 2019). Overall, new tools and techniques are being implemented to help medical personnel improve their services.

Nevertheless, reliance on technology not only offers benefits but indicates risks for medical care organizations. The Healthcare industry, among many others, takes steps to incorporate big data into operations and improve the quality of services. Advanced technologies can revolutionize medical care and offer financial advantages to organizations. For a successful big data analytics implementation, the challenges need to be addressed, and proactive action should be taken to improve healthcare

References

Durcevic, S. (2020). 18 examples of big data analytics in healthcare that can save people. Datapine. Web.

Fatt, Q. K., & Ramadas, A. (2018). The usefulness and challenges of big data in healthcare. Journal of Healthcare Communications, 3(2), 1-4. Web.

Kraus, J. M., Lausser, L., Kuhn, P., Jobst, F., Bock, M., Halanke, C., Hummel, M., Heuschmann, P., & Kestler, H. A. (2018). Big data and precision medicine: Challenges and strategies with healthcare data. International Journal of Data Science and Analytics, 6(3), 241-249. Web.

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3-13. Web.

Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45-47. Web.

Ngiam, K. Y., & Khor, W. (2019). Big data and machine learning algorithms for healthcare delivery. The Lancet Oncology, 20(5), e262-e273. Web.