When You Wake In The Morning You May Reach For Your C 949241

When You Wake In The Morning You May Reach For Your Cell Phone To Rep

When you wake in the morning, you may reach for your cell phone to reply to a few text or email messages that you missed overnight. On your drive to work, you may stop to refuel your car. Upon your arrival, you might swipe a key card at the door to gain entrance to the facility. And before finally reaching your workstation, you may stop by the cafeteria to purchase a coffee. From the moment you wake, you are in fact a data-generation machine.

Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth. As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare.

In this Discussion, you will consider these risks and rewards. Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples. APA FORMAT MIN 3 RESOURCES

Paper For Above instruction

Big data has revolutionized various sectors, particularly healthcare, by enabling the integration and analysis of vast amounts of information to improve clinical outcomes and operational efficiency. One of the most significant benefits of incorporating big data into clinical systems is the enhancement of personalized medicine. Personalized medicine involves tailoring treatment plans based on an individual patient's unique genetic makeup, lifestyle, and environment. Big data facilitates this by aggregating and analyzing large datasets from diverse sources, such as genomic information, electronic health records (EHRs), and wearable device data. For example, machine learning algorithms can analyze genetic variations to predict the most effective cancer treatments for individual patients, leading to higher success rates and fewer side effects (Ristevski & Chen, 2018). This targeted approach not only improves patient outcomes but also reduces healthcare costs associated with trial-and-error therapies. The ability to leverage big data in this manner exemplifies its potential to transform healthcare into a more precise and effective practice.

However, the implementation of big data in clinical systems presents substantial challenges and risks. One primary concern is data privacy and security. Sensitive health information is a prime target for cyberattacks, and breaches can lead to significant harm for patients, including identity theft and loss of trust in healthcare providers. For instance, high-profile data breaches, such as the 2017 WannaCry ransomware attack on the UK's National Health Service, exposed the vulnerabilities of digital healthcare systems (Kellermann & Jones, 2013). These incidents underscore the critical need for robust cybersecurity measures and strict compliance with regulations like HIPAA. Another risk is the potential for algorithmic bias, where datasets used to train machine learning models may not be representative of diverse populations, leading to disparities in healthcare delivery. For example, a predictive model trained predominantly on data from one demographic group may perform poorly for others, exacerbating health inequities (Obermeyer et al., 2019).

To mitigate these challenges, healthcare organizations can implement comprehensive data governance frameworks emphasizing data privacy, security, and ethical use. Strategies such as encryption, secure data storage, regular security audits, and staff training on data handling protocols are essential. Additionally, ensuring diverse and representative datasets during the development of predictive models can reduce bias. Engaging multidisciplinary teams, including ethicists, data scientists, and clinicians, fosters responsible AI deployment and aligns technological advancements with patient-centered care. For example, integrating regular bias assessments into AI model workflows can identify and correct disparities, promoting equitable healthcare outcomes. By adopting these strategies, healthcare providers can harness the benefits of big data while safeguarding patient rights and promoting trust in digital health solutions.

References

• Kellermann, A. L., & Jones, S. S. (2013). What It Will Take To Achieve The As-Yet-Unfulfilled Promises Of Health Information Technology. Health Affairs, 32(1), 63–68. https://doi.org/10.1377/hlthaff.2012.0691
• Obermeyer, Z., Powers, B., Vogwait, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
• Ristevski, B., & Chen, M. (2018). Big Data Analytics in Healthcare. Journal of Medical Systems, 42(7), 1–7. https://doi.org/10.1007/s10916-018-0992-0