NURS 6051S Week 5 Discussion 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.

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.

Required Readings

  • McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.
    • Chapter 22, “Data Mining as a Research Tool” (pp. 537-558)
    • Chapter 24, “Bioinformatics, Biomedical Informatics, and Computational Biology” (pp. 581-588)
  • Glassman, K. S. (2017). Using data in nursing practice

Links to an external site.. Technological Forecasting and Social Change, 126(1), 3–13.

Expert Answer and Explanation

Big Data Risks and Rewards

Benefit of using Big Data in a Clinical System

Big data refers to massive data sets that are often analyzed using advanced computational techniques to give information about human behaviors, trends, and patterns. In healthcare, big data helps in adopting increased speed of data processing, where individuals can gather relevant insights that aid in patient care. A primary benefit of using big data in a clinical system is that care providers can constantly and efficiently monitor vital signs of patients (Groves et al., 2016).

Healthcare givers often try to improve the health of chronic disease patients by monitoring the vitals such as temperature and blood pressure. When a patient’s health condition is constantly changing, it becomes impossible for the healthcare provider to monitor this patient because of the large volume of data generated (Groves et al., 2016). Using big data helps in computing the vitals and relate them with the progress of the patient.

Challenge of Using Big Data as Part of a Clinical System

One of the biggest challenges that face big data in its implementation in a clinical system is the inability to capture all the data. In a recent study, an ophthalmology clinic found that only 23.5% of the patient data reported is captured by electronic health records (EHR), which are part of big data (Dyer et al., 2019). That is, a patient would report to have around three eye problems, but the EHR would only agree to one and report it. This is a primary challenge because it means that big data cannot participate in efficient diagnosis of patient conditions.

Also, since big data is used in making and monitoring prescriptions, it is possible that the technology systems could fail to have efficient ways of capturing patient symptoms and the reaction of patients to medications (Heires, 2016). This problem is not always noticed because most of the healthcare givers using big data only focus on the small percentage of the reported data, and are oblivious of the fact that there could be other information that big data missed.

Strategy to mitigate the stated Challenge of Using Big Data in a Clinical System

One of the strategies to mitigate the challenge of capturing information in a clinical system using big data is prioritizing data types. While big data entails a situation where computers adopt human functions, it is evident that it is humans who program them to capture and record the data in specific ways (McCue & McCoy, 2017). That is, despite the fact that it is a powerful technology tool to manage data, there are human brains behind its design.

The problem of big data can be solved when these programmers ensure that they prioritize the types of data that are open for documentation, and hence reduce the probabilities of the system missing out some of the patient information. Also, clinical documentation and improvement programs that help clinicians to ensure full capturing of the data by the systems is crucial (Heires, 2016).

Such kind of education could be geared to helping the healthcare givers to have the skills to perform constant improvement and impart in them the skills to modify the system in the formats that help in relaying much of the patient information. Also, constant monitoring of the changes and implementation can help in improving the system performance using big data.

References

Dyer, B., Rao, S., Rong, Y., Sherman, C., Cho, M., Buchholz, C., & Benedict, S. (2019). 12 Clinical and cultural challenges of big data in radiation oncology. Big Data in Radiation Oncology, 181.

Groves, P., Kayyali, B., Knott, D., & Kuiken, S. V. (2016). The’big data’revolution in healthcare: Accelerating value and innovation.

Heires, K. (2016). The risks and rewards of blockchain technology. Risk Management, 63(2), 4-7.

McCue, M. E., & McCoy, A. M. (2017). The scope of big data in one medicine: unprecedented opportunities and challenges. Frontiers in veterinary science, 4, 194.