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Big Data Risks and Rewards

Big Data Risks and Rewards

Big Data Risks and Rewards

Please respond to each of the 3 posts with 3 APA sources no older than 5 years old. APA format must be exceptional. 

Reply 1

Professor,

How can big data impact prescription errors?  Be specific and provide examples. Who should be on the team to implement this project and why? Support your work with the literature.  

Reply 2

Ruth Niyasimi,

 

Big Data Risks and Rewards

Big data is defined as the process of collecting, analyzing, and leveraging consumer patient, physical, and clinical data that is too vast or complex to be understood by traditional means of data processing. In healthcare, data is generated from medical records, patient portals, government agencies, research studies, electronic health records, and medical devices. The data generated in healthcare is used to make decisions that will have an impact on patient health outcomes (Raghupathi & Raghupathi, 2014). Healthcare is a critical docket in our society since it is tasked with a duty to prevent, diagnose and treat illnesses and diseases affecting the community. In the past, health information was stored on paper but through advancements in technology, things have significantly changed as patient information is stored on Electronic health records (EHR).

            The adoption of big data had significant impacts on customer services and other related issues. According to Raghupathi and Raghupathi (2014), for many years, healthcare has been generating huge volumes of data that was stored in hardcopy. This was a critical step toward improving the quality of healthcare delivery while reducing costs. This huge volume of information is crucial to healthcare because, through digitalization, it has become possible to detect diseases at an early stage and take necessary intervention measures. Secondly, big data enables the ability to enhance continuity, starting when a patient visits a hospital until the last stage of being discharged.  For example, the lab tests taken from those patients and other specialized treatments are stored in a way that other departments can access this information in the future preventing duplicate redoing labs and imaging studies (Adibuzzaman et al., 2017). This cuts down costs while improving service delivery.

            Although big data has had a tremendous impact on the healthcare systems, it has also created some problems. Firstly, the use of technology such as EHR has resulted in security issues and privacy threats. According to McGonigle and Mastrian (2017), technology has enabled the interoperability of healthcare data. Interoperability means sharing important health data across different organizations while ensuring it is presented understandably to the user.  Unauthorized third parties can intersect this information and the Health Insurance Portability and Accountability Act (HIPPA) has shown little concern for patient data breach cases. Another problem is that big data is not static, it requires continuous system updates to ensure that it remains relevant and current. In some cases, few datasets will require updates after electronic health records to store patient/client’s personal details. Some require to be updated after a few seconds while other information might take some time to change. Therefore, sometimes it is hard for mental health specialists to understand the volatility of big data or how often it requires changes. Thus, this might be a challenge for organizations that do not monitor their data assets consistently.

            Several strategies can be implemented to effectively mitigate challenges arising from the use of big data. Ensuring that data is presented in a common manner and type will facilitate the easy transfer of information (Dash et al., 2019). Another strategy for solving the security risks brought by the use of big data is data encryption to ensure information is safe and protected from malicious people. Besides, there is needed to upgrade healthcare systems to ensure information is shared efficiently across authorized organizations. In summary, the adoption of big data in healthcare has improved customer services and ensured easy data retrieval.

References

Adibuzzaman, M., DeLaurentis, P., Hill, J. & Benneyworth, B. (2017). Big Data in Healthcare – the Promises, Challenges and Opportunities from a Research Perspective: A case study with a model database. AMIA Annual Symposium Proceedings Archive, 2017, 384-392.

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big Data in Healthcare: Management, Analysis and Future Prospects. Journal of Big Data6(1), 1-25.

McGonigle, D., & Mastrian, K. (2021). Nursing informatics and the foundation of knowledge. Jones & Bartlett Publishers.

Raghupathi, W., & Raghupathi, V. (2014). Big Data Analytics in Healthcare: Promise and Potential. Health Information Science and Systems2(1), 1-10.

Reply 3

Elia Vazquez,

 

Big Data Means Big Potential, Challenges for Nurses

Big data in health encompasses a wide variety of aspects, clinical, environmental, lifestyle information, it is biologically diverse. These data are collected from single individuals to large cohorts, according to their health and wellness status, all at once or over a period of time. The availability of big data provides an opportunity for the healthcare providers to improve health outcomes while containing costs (McGonigle & Mastrian, 2021). Big data, helps to identify and promptly intervene on high-risk and high cost-patients, this is achieved through effective ways of managing the data to facilitate precise treatment. It helps in detention of heterogeneity in patient responses to treatments and tailoring of healthcare to the specific needs of individuals.

Also, big data in healthcare can contribute by increasing the effectiveness and quality of treatment by identifying early signs and symptoms as well as disease intervention, reducing the probability of adverse reactions. Additionally, it helps in widening possibilities for prevention of diseases by identification of risk factors for disease and improvement of pharmacovigilance.  Patient safety through the ability to make more informed medical decisions based on directly delivered information to the patients is another benefit (Frith & Hoy, 2017).  Predictive analytics can contribute to precise public health by improving surveillance and assessments therefore, gathering a large amount of data, creating enough resources that can be used in epidemiological research. The health needs of the population, the evaluation of population-based intervention and informed policy making are all made possible by the availability of big data however, big data in public health faces several challenges such as security, visualization, and a number of data integrity concerns. Capturing data that is clean, complete, accurate, and formatted correctly for its use in multiple systems is also a big challenge to accomplish, mainly because of poor Electronic Health Records, convoluted workflows, and an incomplete understanding of why big data is important to be captured well and can all contribute to quality issues that will plague data through its lifecycle (Aceto, et al., 2020). The issue of dirty data is another challenge the use of big data in the health care sector faces. Storage of data is another challenge, because as the volume of data grows exponentially, some providers may find it difficult to manage the costs and impacts of on premise data centers. Data security is another problem that most organizations face, resulting from high breaches, hackings and ransomware incidences making healthcare data vulnerable to several attacks.

To minimize these challenges and risks, it is needed to put in place several mitigations to protect this technology. The first mitigation is the adoption of seamless and diverse health care technologies that helps in gaining deeper insights into clinical and organizational processes. It should also facilitate faster and safer protection of healthcare data. Also, the technology should create more efficiency and help improve patient flow, safety, quality of care and the overall patient experience. According to Frith & Hoy (2017), some of the organizations that have used seamless integration in their data operations includes the South Tyneside NHS Foundation Trust, a provider of acute and community health services in Northeast England. Through this, high quality, safe and compassionate care of patients at all times has been improved at the same time enhancement of data protection.

References

Aceto, G., Persico, V., & Pescapé, A. (2020). Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0. Journal of Industrial Information Integration18, 100129.

Frith, K. H., & Hoy, H. M. (Eds.). (2017). Applied clinical informatics for nurses. Jones & Bartlett Learning.

McGonigle, D., & Mastrian, K. (2021). Nursing informatics and the foundation of knowledge. Jones & Bartlett Publishers.


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