Big Data Capabilities

Big Data Capabilities and Challenges Big data presents a lot of promises as well as challenges in the healthcare industry. On the one hand, my interaction with and research in big data revealed the potential benefits of improving operational efficiency as well as care outcomes. Pastorino et al. (2019) discuss the use of big data in precision medicine through intervening in high-risk and high-cost patients to ensure effective care and efficiency as well. This approach is useful in finding areas of underperformance and determining necessary improvements. For example, big data may be used and can be beneficial to determine the average hospital duration and wait times for patients. Another big technology largely used today by many hospitals is the use of telemedicine where doctors can perform patient assessments at the bedside using the control of remote robots while they are physically away in their offices. Another is Radiology departments have been using the Picture Archiving and Communications System (PACS). This system electronically stores images and reports, instead of using the old methods of manually filing, retrieving, and transporting film jackets which were used for storing x-ray films until the late 1990s. The PACS  is an example of Big data and technology created to expedite patient care based on the fast diagnostic delivering reports.  This information can then be used to develop an intervention to enhance patient throughput and improve efficiency.  On the other hand, a challenge that is faced in big data for clinical use is the problem of having different definitions and descriptions of different data objects. Hospitals measure their data and define it in different terms although there are some standards on care assessment. Thew (2016) identifies this as a source of frustration since data may not be defined the same way. An example that I have seen is the compatibility of data for the same measure but defined differently. For example, fall rates may be expressed as a number such as 10 falls in one month. However, they should further be expressed as fall rates per 1000 bed days. If one department presents data defined in the number of falls and another as fall rates per 1000 bed days, analyses become more complex and challenging. Another major issue is the Cyberattack and whole system shutting down, and people stealing patient information.  Strategies that can be applied to deal with the nuances of data definition, standards on data measurement, and reporting should be implemented. Agencies such as the Centers for Medicare and Medicaid Services (CMS) have defined standards for some measures such as readmission rates. Standardizing data makes it easier to analyze through data mining and interpretation (McGonigle & Mastrian, 2017). Therefore, standards should be extended to the different types of data used for clinical systems. Standardization will reduce the risk of errors in analysis and also increase analytical accuracy.  References McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning. Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European Journal of Public Health, 29(Supplement_3), 23-27. Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. Health Leaders.

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