NURS 6051 Big Data Risks And Rewards

The Big Data has significant benefits as well as challenges for the healthcare organizations. One of the benefits of the Big Data is that it provides health organizations and health care providers with real time data. Health organizations and healthcare providers benefit from real time data in a number of ways. Firstly, health organizations obtain up-to-data on any errors in their systems, enabling them to trouble shoot the errors before they can affect adversely the outcomes of care. Real time data from the Big Data therefore saves time, money, and resources that could have been used in responding to the threats of the errors (Kruse et al., 2016). The Big Data also provides real time information on the trends and needs of the consumers of healthcare services. Health organizations can use this information to develop products and services that match the prioritized and perceived needs of their consumers. Real time data also benefits healthcare providers in that they can use it to make informed decisions on the care that the patients need and ways of meeting them (Pramanik et al., 2017).

Despite the above benefits, Big Data is associated with some challenges. One of them is data privacy. The Big Data relies on the use of vast amount of data from different sources for organizational benefit. The data is however at a risk of being used for other purposes that are not meaningful. There is also the risk of data loss from an organization to third parties threatening data privacy and confidentiality. Several strategies have been developed to address the risks associated with the Big Data on healthcare. One of them is the development of data protection and use guidelines for health organizations to ensure meaningful use of health data. There is also the use of provider training to ensure that healthcare providers have adequate knowledge and skills on the promotion of data integrity (Snyder & Zhou, 2019). Therefore, a consistent use of these strategies promotes safe use of the Big Data in health.

References

Kruse, C. S., Goswamy, R., Raval, Y. J., & Marawi, S. (2016). Challenges and opportunities of big data in health care: A systematic review. JMIR Medical Informatics, 4(4), e38.

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Pramanik, M. I., Lau, R. Y., Demirkan, H., & Azad, M. A. K. (2017). Smart health: Big data enabled health paradigm within smart cities. Expert Systems with Applications, 87, 370–383.

Snyder, M., & Zhou, W. (2019). Big data and health. The Lancet Digital Health, 1(6), e252–e254.

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.

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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.

There are a few benefits to using big data as a clinical system. One of the reasons for using gross data concerning research is that it usually incorporates multiple areas or groups of people and different backgrounds, states, and even countries. By utilizing such extensive data, you can consider a larger body of information, often vague and lacking in detail, with little research. Using a large research format and extensive data collection helps to identify trends in the study that are similar from individual to individual. Identifying these trends allows healthcare providers to create new protocols and new patient care techniques to care for our patients better and efficiently.

Although big data is beneficial in the sense of healthcare, it also comes with its complications and risks. In this form of data collection, you often face a considerable body of information to organize. Much information may be about something other than the research or hypothesis you may be identifying, so it may take some time to identify individuals who may align with the research you are placing.

I have utilized data-collecting techniques in the past. I have observed in my clinical experience that when doing research, there are many ways in which data collection can be done. Individuals utilize interviews to gather information but doing interviews may only sometimes be efficient, especially if your sample or research sample size is large. This is because if the group is large, it would often take some medicine amount of time and effort, if not multiple individuals, to complete the overall interview aspect of the research. Developing a survey or a questionnaire for individuals to fill out to the best of their ability is often a quick, efficient, and straightforward way to collect data quickly.

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.

The Use of Big Data in Clinical System

Big data is increasingly being applied in clinical systems to help improve patient care. This is due to the benefits that big data has had on clinical systems. This paper will discuss the possible benefits, challenges, and a strategy that can be used to mitigate the risks posed by big data to clinical systems.

The Benefits of Using Big Data as Part of a Clinical System

A potential benefit of using big data in a clinical system is that it can help improve patient care. According to research, big data has been helpful in building holistic care strategies for patients in order to achieve patients’ well-being in an effective way (Dash et al., 2019). This is because big data empowers patients to take an active part in their own healthcare. Furthermore, big data can help identify patterns in patient behavior that may indicate a need for preventive care or early detection of diseases. For instance, if big data is used to monitor patient health over time, it may be possible to detect patterns in how patients respond to treatment and identify which patients are at risk for developing certain diseases. This information can then be used to make better decisions about how to treat those patients. In addition, big data can help healthcare providers identify potential risks and issues early on in a patient’s illness in order to prevent more serious complications (El Naqa et al., 2018). Finally, using big data in clinical systems can help make it easier for doctors and nurses to identify optimal care plans for patients based on their individual needs. By collecting and analyzing data from multiple sources, it is possible to create a comprehensive picture of a patient’s health that can be used to improve care.

Challenges of Using Big data as Part of a Clinical System

One potential challenge of using big data in clinical systems is that it can be difficult to collect and analyze the data in a way that is effective and efficient (El Naqa et al., 2018). For instance, it may be difficult to find a way to use big data to monitor a patient’s health over time. Additionally, it may be difficult to ensure that the data is accurate and reliable. If the data is not accurate or reliable, it may not be useful in making decisions about a patient’s care. Finally, big data may be too complex or time-consuming to use in some cases. If the data is too complex, it may be difficult for doctors and nurses to understand it (Mayo et al., 2017). If the data is too time-consuming, it may be difficult for doctors and nurses to use it in their decision-making.

Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using the big data you described.

One strategy that may mitigate the challenges or risks of using big data in clinical systems is to use a data mining tool. Data mining tools can help doctors and nurses to find patterns in the data that may be useful in making decisions about a patient’s care (Bartschat et al., 2019). It can also help to ensure that the data is accurate and reliable. Additionally, data mining tools can be used to simplify the data so that it is easier for doctors and nurses to understand it.

References

Bartschat, A., Reischl, M., & Mikut, R. (2019). Data mining tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1309.

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25.

El Naqa, I., Kosorok, M. R., Jin, J., Mierzwa, M., & Ten Haken, R. K. (2018). Prospects and challenges for clinical decision support in the era of big data. JCO clinical cancer informatics, 2, 1-12.

Mayo, C. S., Matuszak, M. M., Schipper, M. J., Jolly, S., Hayman, J. A., & Ten Haken, R. K. (2017). Big data in designing clinical trials: opportunities and challenges. Frontiers in Oncology, 7, 187.

When nurses enter data, they document essential information for the health team’s understanding of the patient. The importance of this information is critical to understanding and treatment methods. This data and the value of technology are meaningful use to a clinical system (Glassman, 2018). Technologies can be beneficial to gathering and analyzing patient data in a clinical setting. According to Glassman (2018), nurses must engage themselves in their feedback on big data processes and technology support. Nurses having a voice and being at the table to use good data for improved outcomes is key to making effective positive changes (Thew, 2016). Nurse leaders are interested in how to use big data for advocacy in best practice management.

The lack of data standardization and understanding of what to do with all the big data is an obvious barrier. As overwhelming as this subject is, I empathize with the nurse leader’s role in how to make changes from a large influx of data. To untap the value of big data, big data analytics and data mining may offer some solutions for healthcare organizations. Data management systems will help compartmentalize data suitable for big data that comes with healthcare data entry. A potential benefit for leaders is nurse managers using data analytics to view consolidated daily reports concerning patient safety concerns Wang et al. (2018). Data mining are tools to convert data into valuable knowledge. McGonigle & Mastrian, 2022 find that “Data mining includes tools for visualizing relations in the data and mechanizes the process of discovering predictive information in massive databases” (p.537). Nurse managers would be interested in how data mining technology could benefit the interests of their departments and patient outcomes. A nurse manager in a medical unit may be interested in fall prevention methods. Lee et al. (2011) further describe not all falls can be unavoidable, but reducing injuries and avoiding future falls align with desirable goals that healthcare providers and organizations could use from incident reporting system data. Incident data documentation reported by nurses would serve as data to establish fall prevention measures, guidelines, policies, and interventions. Big data could benefit this clinical area of interest through its abilities in data collection measures, methods, and analysis.

Some challenges of using big data are the need for data standardization and the failure of how data can interact (Thew, 2016). In the example of incidents of falls, accurate fall prediction models may use data from the incident reporting systems. Data that is hard to code or document may use the free text option, which can be a challenging variable. Misinterpretation can also add to the risks and challenges of big data.

A researched mitigation strategy to combat resistance to using big data is accepting and assuming the risk. Since fall prevention is a hot topic, I believe big data is a risk worth investing in. The link between evidence-based nursing knowledge and big data can intertwine in the improvement efforts in fall prevention programs. Stevens et al. (2017) described how “improving case management and implementation strategies that promote patient adherence to evidence-based strategies is crucial to successfully reducing falls” (p.77). The argument of why a nurse manager would advocate for specific methods concerning fall prevention measures would have supportive data rather than resorting to a person-to-person debate. In efforts to improve and understand healthcare to a greater degree, meaningful data is necessary (McGonigle & Mastrian, 2022). By using big data, there is an opportunity for improvements in several aspects of healthcare.

Glassman, K. S. (2017). Using data in nursing practice Links to an external site. Links to an external site. American Nurse Today, 12(11), 45–47. Retrieved from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdfLinks to an external site.

Lee, T., Liu, C., Kuo, Y., Mills, M. E., Fong, J., & Hung, C. (2011). Application of data mining to the identification of critical factors in patient falls using a web-based reporting system. International Journal of Medical Informatics, 80(2), 141–150. https://doi.org/10.1016/j.ijmedinf.2010.10.009Links to an external site.

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning

Stevens, J. A., Smith, M. L., Parker, E. M., Jiang, L., & Floyd, F. D. (2017). Implementing a Clinically Based Fall Prevention Program. American Journal of Lifestyle Medicine. https://doi.org/10.1177/1559827617716085

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs, and Links to an external site. Links to an external site. Retrieved from 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. Links to an external site. Links to an external site. Technological Forecasting and Social Change, 126(1), 3–13.

ReplyReply to Comment

There is much we can talk about concerning the positive part of big data. Big data in healthcare has great significance especially in the prediction of possible outcome of diseases prevention of co-morbidities, mortality and taking care of medical treatment expenses (Pastorino et al., 2019). In the era of technology, more people are in need of relevant information especially that touches on patients regarding their healthcare options or choices as well as how they will be part of their health decision-making process. In essence, the use of big data will help to equip patients with relevant and timely information to assist them be greatly involved in arriving at decisions that directly impact their care and treatment.

Reference

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 Health, 29(Supplement_3), 23-27. doi: 10.1093/eurpub/ckz168

Good job on your post, You addressed some valid points regarding data size. When data capacities are so large, it becomes difficult to determine which data points are valuable and insightful. It can create difficulty for nurse leaders who want to analyze, compute data and discover new knowledge to reveal patterns, trends, and associations, especially relating to human behavior and interactions that can improve the quality of care their staff provides. Another challenge is ensuring that the significant data insights are in the hands of the right people so that they can work honestly and critical information is not misused. Also, there can be challenges that may arise due to missing data or incomplete data.
Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision-makers. In several contexts, the use of Big Data in healthcare is already offering solutions for improving patient care and generating value in healthcare organizations. By increasing earlier diagnosis and the effectiveness of information on health and access to health services and quality of treatments through the discovery of early signals. Overall, Big Data and predictive analytics can contribute to disease intervention and reduce the probability of adverse reactions. The major challenge with big healthcare data is sorting and prioritizing information.

Reference

Wang, Y., Kung, L., & Byrd, T. (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.2015.12.019Links to an external site.

Over the past decade, there has been a greater emphasis on the involvement of registered nurses in the development and implementation of health information technology systems to maintain patient safety and improve the quality of care services. Today, electronic health records remain a great source of protected health information and clinical documentation during the provision of care services by registered nurses and other healthcare professionals (Reid et al., 2021). The rapid deployment of EHR by healthcare organizations has created room for registered nurses to create digital versions of patient medical records and transform them into valuable clinical knowledge for preventing adverse events like patient falls and nosocomial infections, among many others. One of the greatest risks of utilizing big data from the digital versions of patient medical records is to maintain the integrity and quality of information system output (McGonigle & Mastrian, 2022). For instance, the digital versions of patient medical records are prone to manipulation and misinterpretation due to weak information security measures and the lack of relevant knowledge and skills for maintaining data integrity and quality. Through regular education and training, registered nurses and other healthcare professionals develop the required nursing informatics competencies, like maintaining strong access credentials for clinical information systems and data encryption to prevent manipulation and unauthorized access.

References

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.

Reid, L., Maeder, A., Button, D., Breaden, K., & Brommeyer, M. (2021). Defining nursing informatics: A narrative review. Studies in Health Technology and Informatics, 284, 108–112. https://doi.org/10.3233/SHTI210680

Big Data 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

Big Data as Part of a Clinical System

According to Coursera (2023) big data refers to large data sets considering both structured and unstructured data that are analyzed to find insights, trends, and patterns. The article goes on to explain that big data is created and analyzed in the healthcare industry from a variety of sources to help with decision-making, enhance patient outcomes, and lower costs. Sources that Coursera (2023) mentions that are used in healthcare that are considered big data are electronic health records (EHR), electronic medical records (EMR), personal health records, and data generated by widely used digital health tools like wearable medical technology and mobile health apps. Big data can help the healthcare industry advance medical research and boost the effectiveness and quality of patient care. NEJM Catalyst (2018) states using big data analysis to deliver information that is evidence-based will over time, increase efficiencies and help sharpen understanding of the best practices associated with any disease, injury, or illness.

Potential Challenge/Risk of Using Big Data

Alkhaldi (2021) mentions that cyberattacks are a problem. The author continues by outlining how healthcare organizations are among the most frequently breached. Hospitals must select reliable big data technology vendors and educate workers on appropriate data usage practices if they wish to prevent system breaches. For instance, certain websites are restricted when using the computers to browse the web at the hospital where I work. This is put in place to protect the software from any potential threat.

Strategy to Mitigate the Risk

A strategy that I have experienced at my current hospital is encrypted data. This organization uses a secure messaging system for workers to communicate and it is all encrypted. Also, the e-mails are encrypted, this is to avoid data breaches and avoid violating HIPAA. Encryption is a way of encoding data so that only authorized parties can receive and understand the information (Puranik, 2020). Security and privacy can be achieved through encryption. According to Mekarni (2022), recovering encrypted data requires a secret key, which is a long random number, and anyone who possesses that number can decrypt the data. Because of this, data sharing across organizations is safer.

References

Alkhaldi, N. (2021). Big Data in healthcare: where does the value come from? ITRex. https://itrexgroup.com/blog/big-data-in-healthcare-examples-problems-benefits/

Coursera. (2023). Big Data in Health Care: What It Is, Benefits, and Jobs. Coursera. https://www.coursera.org/articles/big-data-in-healthcare

Mekarni, R. (2022, April 2). An introduction to encryption: how to protect data in healthcare. Medium. https://medium.com/doctolib/an-introduction-to-encryption-how-to-protect-data-in-healthcare-4d3677add6ec

NEJM Catalyst. (2018). Healthcare Big Data and the Promise of Value-Based Care. NEJM Catalyst.

Puranik, M. (2020). Why Encryption is Essential in Healthcare Cybersecurity Strategies. Health IT Answers. https://www.healthitanswers.net/why-encryption-is-essential-in-healthcare-cybersecurity-strategies/

By Day 6 of Week 5

Click on the Reply button below to reveal the textbox for entering your message. Then click on the Submit button to post your message.

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

SASHA

RE: Discussion – Week 5

COLLAPSE

Top of Form

Big data in healthcare is generated from a collaboration between healthcare players, systems, and other stakeholders (Dash et al., 2019). The data sources may include pharmaceutical research, imaging studies, medical devices, electronic health records, government agencies, and search engines. Big data is important in health. Data analysis tools can be used to determine trends in healthcare. This information can be used to reduce healthcare costs, enhance safety, and improve patient outcomes. The information from data analytics can be used to make informed medical and financial decisions to improve the efficiency of healthcare services (Agrawal & Prabakaran, 2020).

The use of big data in healthcare has some significant challenges. One challenge is the privacy of medical information. Healthcare information is protected, and patient information should not be without their authorization. This violates privacy and confidentiality regulations as per the HIPAA regulations (Cohen & Mello, 2018). The second challenge of big data in healthcare is data security. There is a likelihood of unauthorized individuals accessing the patient data (Pastorino et al., 2019). The third challenge is financial constraints. Analysis of big data requires financial resources and human resources. The fourth challenge is the differences in data type and format. This makes it hard to analyze the data to realize its potential.

These challenges can be overcome through proactive strategies and recognizing the existence of the challenges. One such strategy is ensuring that the big data is presented in the same format and type. In conclusion, big data has a huge potential in the healthcare delivery industry. It is thus important to analyze big data to ensure its potential in improving healthcare outcomes is realized.

References

Agrawal, R., & Prabakaran, S. (2020). Big data in digital healthcare: Lessons learnt and recommendations for general practice. Heredity, 124(4), 525-534.

Cohen, I. G., & Mello, M. M. (2018). HIPAA and protecting health information in the 21st century. JAMA, 320(3), 231.

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1).

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 Health, 29(Supplement_3), 23-27.

Response

Hello Sasha, Bottom of Form

This is insightful Sasha, with the increase in the use of technology, every healthcare institution has a way of capturing and storing data for future references and undertaking research processes. Every step undertaken in the healthcare institution is always recorded. All the information is captured and stored in the databases from the point of admission, diagnosis, treatment, and discharges (Bahri et al., 2018). Big data can also be generated from the collaboration between systems, healthcare players, as well as other stakeholders. There are different stools that can be used to capture and store big data in healthcare institutions. These tools include EMR systems, databases, and other computer applications. Big data is essential in different processes, including research and evidence-based practices (Dash et al., 2019). The research processes meant to reduce the complexity of healthcare delivery processes rely on the big data that have been collected and kept over time (Shilo et al., 2020). Further, healthcare institutions need to ensure data security to protect information that criminals can otherwise use to interfere with the overall management of healthcare systems.

References

Bahri, S., Zoghlami, N., Abed, M., & Tavares, J. M. R. (2018). Big data for healthcare: A survey. IEEE access, 7, 7397-7408. 10.1109/ACCESS.2018.2889180

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25. https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0

Shilo, S., Rossman, H., & Segal, E. (2020). Axes of a revolution: challenges and promises of big data in healthcare. Nature medicine, 26(1), 29-38. https://www.nature.com/articles/s41591-019-0727-5

TIMOTHY

RE: Discussion – Week 5

COLLAPSE

Top of Form

One potential benefit of using big data is to improve efficiency and quality resulting in better patient outcomes (McGonigle & Garver Mastrian, 2018, p. 478, Glassman, 2017). We already do this for things like Influenza. It is well known and understood when the flu season starts and ends and know the treatment for this illness. We of course have been doing this for COVID-19 with improved treatment, the use of PPEs, use of reverse isolation rooms, etc. that have improved patient outcomes while protecting healthcare workers (Smith, Devane, Nichol & Roche, 2020).

The potential challenge is “drowning in data”. Different systems may have different terminology, units, etc. The lack of data standardization can be challenging (Thew, 2016). A famous (and costly) example was the Mars Climate Orbitor crashed into Mars because one contractor for the software controlling the orbiter’s thrusters used pounds while a different contractor for a different software analyzing the data used the metric unit Newtons (Grossman, 2010). This simple mistake cost NASA $125 million.

To help mitigate this potential problem, the software has default settings, for example the patient’s vital signs. Some vital signs are in metric (weight) while others are in imperial (height). This should be standardized, especially with the expanded use of Traveler and Temporary Nurses. I have seen a patient’s weight recorded in the chart as being 250 kg when looking at the patient, it is obvious this is an error. When reweighing the patient, it turns out the patient weighs 250 lbs. This can be an issue, not only in demographics but also treatment options because it affects the patient’s ability to have a CT Scan or MRI. The software used should flag the user when data entered is outside of the expected norm.

References

Glassman, K.S. (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

Grossman, L. (2010, November 11). Nov. 10, 1999: Metric Math Mistake Muffed Mars Meteorology Mission. Retrieved from Nov. 10, 1999: Metric Math Mistake Muffed Mars Meteorology Mission | WIRED

McGonigle, D., & Garver Mastrian, K. (2018). Nursing Informatics and the Foundation of Knowledge. (4^th Ed.) Jones & Bartlett Learning.

Smith, V., Devane, D., Nichol, A., & Roche, D. (2020). Care Bundles for Improving Outcomes in Patients with COVID-19 or related conditions in Intensive Care – a Rapid Scoping Review. Cochrane Database Syst Rev. 2020 Dec. 21;12(12):CD013819. Doi:10.1002/14651858.CD013819. PMID:33348427; PMCID: PMC8078496. Retrieved from https://pubmed.ncbi.nlm.nih.gov/33348427

Thew, J. (2016, April 19). Big Data Means Big Potential, Challenges For Nurse Execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Bottom of Form

Response

Hello Timothy,

This is insightful; big data is mainly used to enhance efficiency in the healthcare delivery processes. Every activity undertaken in the healthcare delivery processes is always documented, leading to the generation of big data (Smith et al., 2020). These data can be used in the research processes to enhance the quality of healthcare delivery processes. Besides, big data can be utilized to enhance evidence-based practices. One advantage of big data is the ability of healthcare institutions to determine trends in the healthcare delivery processes and make the required changes to enhance efficiency (Bahri et al., 2018). One of the main disadvantages of using big data is its insecurity. If data is not well guided, there are threats from cybercriminal activities that can interfere with the validity of the entire data from the organizational database (Dash et al., 2019). Data collection and storage tools need to have a high level of security to reduce the cases of losing important information.

References

Bahri, S., Zoghlami, N., Abed, M., & Tavares, J. M. R. (2018). Big data for healthcare: A survey. IEEE access, 7, 7397-7408. 10.1109/ACCESS.2018.2889180

Excellent post-Serena; utilizing Epic in the hospital setting is a great form of big data. There are many benefits to using such data-collecting methods. One benefit that just came to mind is that in the hospital setting, especially in the ER, you often see frequent fliers or those requiring medical treatment due to chronic illnesses. These chronic illnesses include diabetes mellitus, heart failure, sickle cell crisis, and COPD. These individuals have a higher likelihood of undergoing a medical crisis. Due to big data collecting programs like Epic, you can retain necessary patient data such as previous lab values, vitals, and other pertinent information over a patient’s lifespan. Epic makes it easier for healthcare professionals to track and quickly identify patient trends and information. It also saves much time with patient care. More specifically, it is much easier and quicker to update information than to repeatedly collect the same information. Another benefit I found from Epic is that a patient can have a lot to record when recording information on patients, especially if they have multiple system health issues. A well-organized data collecting system such as Epic makes it easy for healthcare professions to document complex health information.

I appreciate your comments to this discussion topic. It is important that nurses remain involved in the learning process for all technology. Nurses should have a basic proficiency of computers and related technology as the field of healthcare continuously advances and changes. Defining the basic minimum requirements allows employers to test for basic proficiency in new employees. It is important to be able to find the correct information, manipulate it to the need at hand, and be able to provide it to the patient. There are different generations of nurses, many of whom have varying levels of computer capabilities and different needs for their learning. The ability to quantify the needs of each nurse would help to develop custom teaching plans to ensure that each nurse is adequately prepared to perform their job. Does your institution have a formalized training plan? Thanks, Dr. Howe

NURS 6051 Big Data Risks And Rewards