Week 5 6051

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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.

To Prepare:

· Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.

· Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.

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.

https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

https://www.sciencedirect.com/user/identity/landing?code=lSHYxoWmnNR9lwhU0NCDSF7dYsPo-pZnNXwDG-YN&state=retryCounter%3D0%26csrfToken%3D5643394c-e429-4bec-9df5-0f7c8fc363f0%26idpPolicy%3Durn%253Acom%253Aelsevier%253Aidp%253Apolicy%253Aproduct%253Ainst_assoc%26returnUrl%3Dhttps%253A%252F%252Fwww.sciencedirect.com%252Fscience%252Farticle%252Fpii%252FS0040162516000500%253Fvia%253Dihub%26prompt%3Dlogin%26cid%3Datp-a7def609-f019-4c01-ae25-ac77f7ad42fe

https://www.youtube.com/watch?v=4W6zGmH_pOw

APA format and at 3 references

Then respond to two peers by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.

in apa format and 2 references

peer 1

Nursing informatics is a common part of any medical structure. Specifically, the specialty involves integrating healthcare information and knowledge with technology to better patient outcomes (McGonigle & Mastrian, 2022). In most instances, clinical systems utilizing nursing informatics rely on big data systems to collect, store, and disseminate medical care information and knowledge to relevant parties. However, these said big data structures are technologically-based, making them vulnerable to different problems despite displaying multiple benefits. Therefore, the assessment discusses the potential benefits and risks of using big data in a clinical setting, including strategies to mitigate identified risks.

            Data usage is part and parcel of standard nursing practice. According to Glassman (2017), nurses use data to make informed medical practice decisions; specifically, healthcare data helps nurses analyze, develop and assess patient care by determining the ideal or efficient approach to prevent and treat illnesses, resulting in improved health outcomes. The extensive use of data in daily nursing operations has contributed to big data incorporation in clinical systems. It comprises massive patient or population information volumes created by employing digital technologies to gather and store the data.

            In furtherance, big data use in nursing is associated with many benefits. Wang et al. (2018) describe how big data analytics in healthcare operations like nursing supports high-quality patient care through evidence-based practice promotion; the analytics allows for caregivers to discover associations from massive medical records, enabling a detailed identification of care patterns which ensures sufficient evidence is obtained to backup any medical intervention chosen to manage a specified health condition. Moreover, it contributes to interprofessional collaboration within a given healthcare organization by improving communication among varying healthcare practitioners and staff members. Other benefits constitute avoiding unnecessary medical costs incurred by the healthcare organization, such as information technology (IT) expenditure, quick transfer of information among exiting medical IT systems, shortening diagnosis periods, and reducing patient travel time.

            Nonetheless, there are certain challenges affiliated with big data use in nursing. One of the top challenges of using big data is showcased during evaluating and synthesizing patient or population data; the steps are usually conducted manually, resulting in high demand for time and labor power (Thew, 2016). In particular, the big data structures are built in silos, leading to a difference in data systems amongst existing units. Thus, any nursing practitioner intending to use the information may find the data standardization lack a great challenge, primarily when examining how a healthcare organization performs to facilitate informed decision-making, attributing to the need to analyze the information manually.

            The identified challenge can be mitigated by breaking down the traditional structured big data silos. According to Thew (2016), breaking down said silos will offer a balanced approach to evaluating nursing or organizational performances by eliminating the need for manual operations. Additionally, it ensures any individual analyzing the performances has access to real-time data, making the evaluations accurate and relevant. Hence, the analysis will be conducted by a few people within a short time.

             

Peer 2

    The collection of data by nurses never stops. Information is created by combining and analyzing individual data, which is then synthesized to give it meaning and create knowledge (McGonigle & Mastrian, 2017). Massive volumes of data, or “big data,” can be retrieved and continuously evaluated with the inclusion of EMRs and cloud storage (Benke & Benke, 2018). Technology-advanced computer processing systems must be used to examine huge data in order to retrieve, sort, analyze, and synthesize meaningful information. (2018) (Benke & Benke). As processing systems advance and artificial intelligence is added, the delivery of healthcare is changing (Benke & Benke, 2018). Through the use and distribution of the acquired information, nurses are placed at the forefront of this shift. 

Benefit of Using Big Data

     As processing systems advance and artificial intelligence is added, the delivery of healthcare is changing quickly (Benke & Benke, 2018). Clinical data can be continuously computed using AI models and other big data processing software (Benke & Benke, 2018). Real-time data analysis may be able to predict severe disease outbreaks, which enables the earlier implementation of preventative measures. To prevent outbreaks, save lives, and lower healthcare costs, accurate epidemiological surveillance is essential (Jovanovic Milenkovic, Vukmirovic, & Melenkovic, 2019). In order to help avoid future epidemics, identify the most efficient therapies, and follow the causative variables in their spread, the information gathered can also be employed in further research.

Challenge of Using Big Data

     Even with state-of-the-art technology, creating knowledge requires sorting through enormous amounts of data. To ensure that accurate, useful knowledge is produced, precise and thorough data collection is required (Jovanovic Milenkovic et al., 2019). The many approaches to recording, storing, gathering, and distributing this information create additional challenges (Jovanovic Milenkovic et al., 2019). In the healthcare industry, data are either standardized or uniform. Different healthcare systems and staff personnel use different terminology, terminologies, and abbreviations. AI is attempting to address this issue with learning algorithms; however results will be biased if all pertinent data is not fully captured. The safety and health of the patients may be in danger if this inaccurate information is used. The American Nurses Association has issued a policy statement in response to these worries that urges the use of standard terminologies to facilitate interoperability for the exchange of healthcare data (ANA, 2018). In order to simplify information interchange and address patient safety concerns, nurse leaders must make sure staff members understand and use approved terminology in the EHR.

 

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