Please follow the instruction below Zero plagiarism 5 references 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. 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. NOTE!!!!!!!!!!!!!! For 7th edition APA – which is what we are using – one of the big changes is in the use of et al – you have to use it now if you have MORE than two authors.  Previously it was more than five In general, scholarly sources have clear authors and dates.  So try to avoid sources with no apparent author and no date. Usually this is a problem for web site materials – not so much for sources in the library. Rule of thumb – use the library most of the time.  Remember if there is no author noted, the website owner is the author – so the American Nurses Association is considered the author of the Code of Ethics, for example.

The use of big data in healthcare has the potential to bring about significant benefits in clinical systems. One potential benefit is the ability to identify patterns and trends in large datasets that can lead to improved patient outcomes. For example, by analyzing a large volume of patient data, healthcare professionals can identify risk factors for certain diseases or conditions, allowing for early intervention and prevention. This can lead to better patient care and reduced healthcare costs.

Additionally, big data can be used to enhance clinical decision-making. By analyzing data from various sources, such as electronic health records, genetic testing, and medical imaging, healthcare professionals can gain a more comprehensive view of a patient’s health and make more informed treatment decisions. This can lead to improved accuracy and effectiveness in diagnosis and treatment plans.

Moreover, the use of big data can enable personalized medicine. By analyzing individual patient data, such as genetic information and medical history, healthcare professionals can tailor treatment plans to specific patients, taking into account their unique characteristics and needs. This can lead to more targeted and effective treatments, minimizing the risk of adverse events and optimizing patient outcomes.

Despite the potential benefits of using big data in clinical systems, there are also challenges and risks that need to be considered. One potential challenge is the ethical and legal implications of data privacy and security. With the increase in data volume and complexity, there is a greater risk of unauthorized access, data breaches, and misuse of personal health information. This raises concerns about patient confidentiality and trust in the healthcare system. Healthcare organizations must ensure robust data protection measures, such as encryption and access controls, to safeguard patient data and comply with privacy laws and regulations.

Another challenge is the interoperability and standardization of data. Healthcare systems often use different electronic health record systems and data formats, making it difficult to integrate and analyze data from multiple sources. This can hinder the ability to derive meaningful insights from big data and limit its potential benefits. Efforts are being made to establish interoperability standards and promote data sharing among healthcare systems, but further progress is needed to overcome this challenge.

In order to mitigate the challenges and risks associated with using big data in clinical systems, several strategies can be employed. One strategy is the implementation of comprehensive data governance policies and procedures. This includes establishing clear guidelines for data collection, storage, and use, as well as conducting regular audits and assessments to ensure compliance with data protection regulations. Additionally, healthcare organizations can invest in advanced data analytics tools and technologies that can help identify and address potential data security vulnerabilities.

Furthermore, collaboration and partnership between healthcare organizations and technology companies can help overcome interoperability challenges. By working together, they can develop standardized data formats and protocols, as well as interoperability solutions that enable seamless data exchange and analysis. This can enhance the ability to derive meaningful insights from big data and improve clinical decision-making.

In conclusion, the use of big data in clinical systems has the potential to bring about significant benefits, such as improved patient outcomes, enhanced clinical decision-making, and personalized medicine. However, there are also challenges and risks, including data privacy and security concerns, as well as interoperability issues. By implementing strategies such as robust data governance policies, advanced analytics tools, and collaboration between healthcare organizations and technology companies, these challenges and risks can be effectively mitigated, allowing for the successful use of big data in healthcare.