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Discussion: Big Data Risks and Rewards

NURS 6051 Discussion: Big Data Risks and Rewards

Discussion: Big Data Risks and Rewards

The benefit of using big data is that it helps in promptly intervening and identifying high-risk and high-cost patients. For instance, big data enable precision medicine by allowing the recognition of heterogeneity in patient response. Additionally, big data acts in an effective way of managing by tailoring healthcare to individuals’ specific needs, thus reducing the inefficiency and leading to the improvements of cost containment in a healthcare setting (Pastorino et al., 2019). According to Wang (2018), big data helps healthcare organizations examine a huge volume of data crossways a wide range of networks, thus enabling effective action and supporting evidence-based decision-making.

The possible challenge or risk of using big data in a clinical system includes data fragmentation, thus leading to the lack of uniform digitization, impeding efficiency. For instance, a healthcare system may want to use big data, but the staff member lacks the necessary information or training, which is a challenge. According to Thew (2016), the lack of data standardization can lead to a challenge when using big data since it can hinder making informed decisions about healthcare settings. An effective strategy that can be used to overcome the challenges when using big data in healthcare includes finding people with the right skill for big data (Tauchman, 2019). For instance, a health care can outsource a data analyst to help set the organization with the data set, thus overcoming the challenges. Additionally, it is essential to secure the big data by enhancing cybersecurity practices which act as a big data initiative and tool (Tauchman, 2019).

References

Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European Journal of Public Health29(Supplement_3), 23-27. https://academic.oup.com/eurpub/article-abstract/29/Supplement_3/23/5628051

Tauchman.R. E, (2019). 4 Big Data Challenges and How to Overcome Them. https://www.comptia.org/blog/4-big-data-challenges-and-how-to-overcome-them

Thew. J, (2016). Big data means big potential, challenges for nurse execs. https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change126, 3-13. https://www.sciencedirect.com/science/article/pii/S0040162516000500?casa_token=qnJTe–LvqsAAAAA:i2Yr9P8Voi9cMZ8FdYUvsAWJgnxlFc_BHgKdnbyjoKyey1o-c9Gxy5fuM80y3uWXSP2QOmRV3A

https://class.waldenu.edu/webapps/discussionboard/do/message?action=list_messages&course_id=_16850392_1&forum_id=_9184048_1&discussion_board_entry&conf_id=_3992098_1&message_id=_118434594_1

     One potential benefit of using big data within our clinical system is the ability of systems to analyze patterns to build and assess a

Discussion Big Data Risks and Rewards
Discussion Big Data Risks and Rewards

model to generate predictions of new observations, otherwise known as predictive capability (Wang et al., 2018).  Within our healthcare organization, we utilize a function that uses predictive capability known as the Modified Early Warning Score, or MEWS, to predict a patient’s potential for decline.  The MEWS system analyzes a patient’s vital signs trends with algorithms for the regular heart rate, blood pressure, temperature, respiration rate, and level of consciousness.  Once documented, the algorithm assigns a score to the patient based on any deviations from the norm within the algorithm; if the patient receives a score of 6 or greater, a MEWS warning is generated. Our hospital Emergency Response Team is paged to assess the patient in person.  The MEWS system can benefit patient care because it allows nurses a second set of eyes and a fresh perspective, especially on units responsible for patient ratios upwards of 1:8. It allows early recognition of a patient’s potential decline and needs a higher level of care.

On the other hand, there is an increased risk with big data directly rated to patient care and the overall cost of healthcare.  There is evidence that the use of predictive capability in healthcare can cause patients to receive additional unnecessary testing and transfer to higher levels of care and cause further complications during their hospital stay.  When patients are negatively affected by algorithms with predictive capability, the cost of healthcare can also increase.  According to Househ et al. (2019), Big Data in healthcare is not all that is cracked up to be, and while the potential to be good is there, it often leads to misguided medical decisions by healthcare providers.  I have personally seen this with the Modified Early Warning Score in use in my institution.  The MEWS system realized a patient with abnormal vital signs and an altered level of consciousness but that was not abnormal for this particular patient due to their history of chronic renal failure and other comorbidities but what the system ended up doing was creating more work for the nurses and costs for the patient because the physician was alerted by our emergency response team that the patient was maybe declining.

A strategy I have after reading the article Big Data Means Big Potential, Challenges for Nurse Execs (Thew, 2016) is for nursing staff, especially chief nursing executives and administrators, to become involved in the process and recognize what types of big data are beneficial to their organization.  One specific big data capability the hospitals in the eastern region of my state saw as beneficial is a program to be able to share patient medical records even with different electronic health record systems.  Sharing patient’s medical record information and sharing information with our patients is a considerable benefit between different hospital systems. It goes along with the goals of HITECH, or the Health Information Technology for Economic and Clinical Health Act (Glassman, 2017).  It allows for staff to safely integrate patient records and engage them in their care by ensuring accuracy.  Being directly involved with the integration of big data that positively impacts patient care and staff, chief nursing executives can build a rapport as advocates within their health system.

References

Glassman, K. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45-47.  Retrieved from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf

Househ, M., Kushniruk, A. W., & Borycki, E. (2019). Big data, big challenges: a healthcare perspective: background, issues, solutions and research directions Version (238). In Informatics Empowers Healthcare Transformation (Vol. 238, pp. 36–39). Springer. https://ebooks.iospress.nl/publication/46821.

Thew, J. (2016, April 19). Big Data Means Big Potential, Challenges for Nurse Execs. HealthLeaders Media. https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs?page=0%2C1.

Wang, Y., Kung, L. A., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13. https://doi.org/10.1016/j.techfore.2015.12.019

RE: Discussion – Week 5

Big data in health care represent significant sources of information such as medical records, hospital records of patients, results of medical examinations. In addition, information is the critical factor in an organization leading to expansion and growth. Therefore, organizations must identify efficient storing, organizing, and optimal ways of using data (Dash et al., 2019).

Data mining or KDD are crucial to predict and deliver specific information in large databases. Data mining is a process used to collect, transmit, store, organize and relate data to produce new knowledge. For example, how many patients were diagnosed with a specific disease in a determined time? Additionally, KDD also identifies patterns useful in health care leading to desired patient outcomes (McGonigle & Mastrian, 2017).

A potential benefit of big data in a clinical system is cost reduction. For example, by implementing data analytics, a health care institution may attain better diagnosis and disease predictions through personalized medicine, decreasing hospital readmission rate and costs ( Dash et al., 2019). However, data capture, cleaning, and storage issues can increase healthcare costs due to inconsistent and irrelevant data. For data to be valuable, it needs to be cleaned,  accurate, and appropriately formatted. In addition, one of the significant challenges of using data is data security, such as malware and phishing attacks (HealthITAnalitics, 2019). For example, to protect our data at work, we report unusual emails by forwarding them to bad email. Then, the security department is in charge of tracing and protecting data from possible attacks by making the health care system aware. Additionally, the data security department conducts additional training by sharing information about recent security attacks to keep employees updated.

Dash, S., Shakyawar, S.K., Sharma, M. et al. Big data in healthcare: management, analysis, and future prospects. J Big Data 6, 54 (2019).    https://doi.org/10.1186/s40537-019-0217-0

McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.

HealthITAnalytics, J. B. (2019, June 19). Top 10 challenges of big data analytics in healthcare. HealthITAnalytics. Retrieved September 29, 2021, from https://healthitanalytics.com/news/top-10-challenges-of-big-data-analytics-in- healthcare.

RE: Discussion – Week 5

Benefits and Challenges of Big Data

Big data can be defined as the large and complex electronic health data sets that are difficult to handle with traditional software/hardware or common data management methods and tools. Big data includes clinical data from CPOE and clinical decision support system (physician’s written notes, prescriptions, laboratory, medical imaging, insurance, pharmacy, and other administrative data), patient data in electronic patient records, machine generated data (monitoring vital signs, social media posts, and web pages), and less patient-specific information (emergency care data, articles in medical journals, and news feeds) (Raghupathi & Raghupathi, 2014). Big data analytics in healthcare is evolving rapidly. As technology has advanced, healthcare organizations now can collect, analyze, and mange large amounts of data. There was a report saying that the data from United States healthcare system alone reached 150 exabytes in 2011. At this rate, it is soon going to reach the zettabyt and yottabyte scale. Kaiser Permanente, the California-based health network, has more than 9 million members, and they have approximately between 26.5 to 44 petabytes of data from EHRs, including annotations and images (Raghupathi & Raghupathi, 2014).

Nowadays, big data analytics in healthcare helps improve quality of care and patient outcomes and reduce healthcare costs by finding patterns, associations, and trends within the data. One of the most current and relevant big data analytic examples in healthcare is quick development of COVID-19 vaccines. Since researches could share information with each other, they were able to develop advanced medications very rapidly. Big data analytics in healthcare also helped us predicted and analyzed the spread of disease. It allowed us to manage and fight this pandemic more efficiently.

Although the potential of big data is enormous, there are also remaining challenges to overcome. For instance, healthcare cybersecurity and information privacy are one of the concerns that come with the big data. Data security is one of the priorities in the healthcare organizations, which are at high risk of rapid-fire series of high profile breaches, ransomware episodes, and hackings. In addition, many healthcare organizations are lack of adequate databases, systems, and the skilled staffs to mange big data (Touro College Illinois, 2021).

Strategies of Using Big Data

Many healthcare organizations and healthcare searchers are working to address and find solutions to these issues and to facilitate the use of big data analytics in healthcare field. Some strategies will include following (Kent, 2020).

  1. Provide comprehensive, quality training data.

  2. Eliminate bias in data and algorithms.

  3. Develop quality tools while preserving patient privacy.

  4. Ensure providers trust and support analytics tools.

References

Kent, J. (2020, October 2). 4 Emerging Strategies to Advance Big Data Analytics in Healthcare. Retrieved from https://healthitanalytics.com/news/4-emerging-strategies-to-advance-big-data-analytics-in-healthcare

Raghupathi, W. Raghupathi, V. (2014, February 7). Big Data Analytics in Healthcare: Promise and Potential. Health Inf Sci Syst. 2(3). doi: 10.1186/2047-2501-2-3

Touro College Illinois. (2021, March 11). Applications and Examples of Big Data in Healthcare. Retrieved from https://illinois.touro.edu/news/applications-and-examples-of-big-data-in-healthcare.php

Big Data refers to high volume and highly diverse biological, clinical, lifestyle, and environmental data on health and wellness status. Data collection ranges from one person to large cohorts. Big data in clinical systems obtain information from numerous sources such as patient summaries, EHRs, clinical trials, genomic data, telehealth, pharmaceutical data, and mobile apps (Pastorino et al., 2019). Employing big data in a clinical system comes with several benefits since it helps discover disease risk factors in an individual patient or a specific population (Glassman, 2017). Using big data increases disease prevention opportunities and informs health promotion activities for patients and the community.

Health professionals can analyze big data from a patient’s clinical data gathered from EHR and summaries in predicting the possible risk of a patient developing a lifestyle disease or a disease complication (Wang et al., 2019). From the risk prediction, the health can then plan appropriate health promotion interventions to lower the risk of having the disease or slow the progression of an existing condition.

However, using big data in a clinical system has its limitation, like the risk of a data breach.  Big data compromises the security of patient data from data breaches, system hacks, and ransomware (Pastorino et al., 2019). A data breach occurs due to ineffective administrative system safeguards to secure patient data. In addition, public cloud services increase the risk of system hacks causing a data breach.

The risk of a data breach in big data can be alleviated through staff training to enhance the organization’s data security measures. McGonigle and Mastrian (2017) assert that organizations can train staff on data security protocols and updates in the protocols. Furthermore, organizations can frequently evaluate the staff with access to sensitive patient data to avoid data breach or damage by hackers and malicious parties.

References

Glassman, K. (2017). Using data in nursing practice. American Nurse Today12(11), 45-47.

McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th Ed.). Burlington, MA: Jones & Bartlett Learning.

Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European journal of public health29(Supplement_3), 23-27 https://doi.org/10.1093/eurpub/ckz168

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change126, 3-13. https://doi.org/10.1016/j.techfore.2015.12.019

Big Data Risks and Rewards

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.

Resources

Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.

WEEKLY RESOURCES

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.

By Day 3 of Week 5

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.

By Day 6 of Week 5

Respond to at least two of your colleagues* on two different days, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.

*Note: Throughout this program, your fellow students are referred to as colleagues.