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Key Lashawn Harper

Big Data in Healthcare: Navigating the Potential Rewards and Inherent Risks

One of the most important benefits of using big data within clinical systems is improving patient care through predictive analytics. Analyzing massive datasets in healthcare allows for identifying typical patterns and predicting trends associated with disease outbreaks and potential health crises before they occur (Del Giorgio Solfa & Simonato, 2023). For example, big data analytics can assist in predicting which patients are susceptible to developing certain conditions, such as diabetes and heart disease, allowing for early intervention and personalized treatment plans. Based on these initiative-taking measures, it is possible to cut healthcare costs.

Key issues and challenges concern data privacy and security associated with big data use in healthcare systems. The special concern in this regard is that the extensive storage and processing of delicate health data of patients can be subject to security breaches and unauthorized access to PHI, or personal health information (Shojaei et al., 2024). Additionally, the complexity of healthcare data systems makes it a time-consuming and arduous task for care providers to apply and interpret data, which can eventually lead to patients being incorrectly diagnosed, which can ultimately cause harm.

Remediating the problem of handling extremely substantial amounts of data requires the establishment of a more rigorous approach toward data governance and administration. According to Glassman (2017), a working strategy could be the implementation of cybersecurity architecture that includes encryption, access control, and regular security audits to ensure the privacy of patient data. In addition, investing in doctors’ and nurses’ education on how to use and interpret big data is key for the proper and effective utilization of patient information. Another way to minimize the risks posed by big data use in clinical systems is the employment of identification techniques for conducting data analysis (Thew, 2016). By encrypting identifiable information, healthcare institutions can process customer data for deriving trends and patterns without putting individual privacy at stake.

Reply 2

Hillary 

Main Post

Potential Benefit of Using Big Data

Big data is a large complex data set which can be analyzed using various techniques such as mining and predictive analytics to yield significantly more data than smaller sets of data or data sets that are not integrated (Wang et al., 2018; Thew, 2016). By analyzing huge volumes of data across a range of healthcare networks big data analytics can support clinical decision making through predictive analytics (Wang et al., 2018). Predictive capability is created when organizations utilize generative platforms to combine data warehouses with predictive algorithms which help users make optimal decisions (Wang et al., 2018). For instance, large genetic and clinical datasets may be analyzed using big data predictive analytics to determine the most effective therapies for individuals, as demonstrated in a study examining personalized therapy for individuals with hypertension (Saini & Kanna, 2023). Through the analysis of a dataset including data from more than a million individuals the research was able to group patients based on clinical or genetic traits and ultimately personalize treatment strategies for each subgroup which led to improved outcomes when compared to conventional approaches (Saini & Kanna, 2023). The use of predictive analytics in the healthcare setting can lead to early identification of disease, identification of specific subgroups who benefit from personalized care, and optimizing clinical processes by improving patient flow and efficiently allocating resources (Saini & Kanna, 2023). Big data analytics and predictive analytics can be used to facilitate clinician decision support by giving clinicians timely and relevant information, personalized suggestions, and alerts or reminders (Saini & Kanna, 2023).

Potential Challenge of Using Big Data

The use of big data in healthcare poses several challenges including information security and privacy, data management, regulatory compliance, cost, system compatibility, and scalability. The lack of standardization of data systems is a potential challenge to big data analytics, which may impact and undermine the reliability of datasets used to generate predictive analysis and clinical decision-making support (Thew, 2016). For example, in a healthcare system with multiple data sources, including EMRs, wearable devices, medical imaging and medical sensors all of the data may be coming from different, possibly incompatible systems. If the different sources are using different data formats or definitions for variables it could lead to inconsistencies and errors in the analysis. The integration of data from various sources is key to creating well informed datasets or data-warehouses (Saini & Kanna, 2023). However, disparate data sources can use different coding schemes or data structures which make it difficult to integrate data effectively. To combat this an organization might hope to create an operating system to support big data use integration and the storage options but this can be yet another issue (Thew, 2016; Wang et al., 2018). Even when creating an in-house system for big data storage and analytics healthcare systems need to consider the issue of data sharing agreements for integrating patient records from previous providers, as well as system compatibilities (Frey, 2018). To facilitate data system standardization, as well as the creation and storage of data-warehouses a large-scale storage solution and a way to integrate previously collected data is necessary (Wang et al., 2018; Frey, 2018).

Mitigation Strategy

To mitigate the challenge posed by the lack of data system standardization healthcare organizations could implement a comprehensive data governance framework. The data governance framework should establish clear protocols, standards and procedures for data management (Wang et al., 2018). Data governance is an extension of IT governance and focuses on utilizing data resources to create business value and is essential to supporting the use of big data analytics (Wang et al., 2018). To establish a strong data governance protocol the organization must first create clear missions, goals, procedures and metrics. This ensures that the data will be understood, trusted and accessible which will allow the data to be used effectively for analysis and decision making (Wang et al., 2018). Health care organizations should review the data gathered in all areas of their system to determine the data’s value and prioritize it in their analytics framework, this will minimize the cost and complexity as well as reducing redundance (Wang et al., 2018). The University of Kansas Hospital showed how establishing a successful data governance committee can facilitate the management of data availability, usability, integrity and security, therefore ensuring the success of big data analytic initiatives (Wang et al., 2018). With strong data governance practices healthcare organizations can achieve standardized data definitions, data quality and create a roadmap for future organizational intelligence and data governance (Wang et al., 20

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