Feb 23, 2024 NURS 6051 Discussion: Big Data Risks and Rewards
NURS 6051 Discussion: Big Data Risks and Rewards
NURS 6051 Discussion: Big Data Risks and Rewards
As healthcare advances in technology, big data keeps on expanding. Big data are digital information that is high in volume and increasing continuously, and variation is remarkable (Campus, 2015). Big data in healthcare are ample information collected from various resources such as healthcare devices, medical tools, electronic health records, and anything that captures patients’ health information and is stored in an extensive database (Lee & Yoon, 2017).
One benefit of big data in healthcare is providing more data to healthcare providers so they can diagnose, categorize, and differentiate diseases: an example of this is genetic findings. Geneticists can categorize specific genes and diseases associated with them, and the more data they have, the more accurate the findings will be. A couple whose family has Alport Syndrome, a hereditary kidney disease diagnosed through genetic testing, will have answers from their doctor and geneticist on what specific gene will cause the disease. They can choose a path where they can be confident that the specific gene will not pass on to their children, such as In-vitro fertilization with the genetic testing method. Various information from different patients who had kidney disease with no comorbidities was tested genetically, and the information was collected, analyzed, and interpreted.
One potential risks of big data are miscommunication gap. Different hospitals, clinics, laboratories, urgent care, and other healthcare facilities have separate data from the same patient requiring care at a specific time. A specific example is patient A lives in Texas, the primary doctor with Kelsey-Seybold. All his regular annual exams and lab test are with Kelsey-Seybold. He was home complaining of severe abdominal pain that started a few days ago. He got admitted to Memorial Hermann Hospital. He was diagnosed with appendicitis and went through an appendectomy. He went on vacation in Florida the next month. He has a severe allergy, and he got admitted to one of the hospitals. All the patient’s admission and health data are with a specific facility. Most patients remember specific histories regarding their hospitalizations, operations, medication, and even their provider’s name. Providers can diagnose and create a better treatment plan if they have complete information about the patient. This will also save time and different tests and procedures the patient must go through. A one-stop shop for all healthcare records should help solve the issue (Fatt & Ramadas, 2018).
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In the clinic where I work, our Information technology department and Informatics have been working hard to get us access to different hospitals within 100 miles of our clinic. They are the hospitals where e our regular patients go or get admitted. We have access to at least three hospitals. Whenever the patients tell us they were hospitalized for a few days, we ask which hospital, and we can log in and see their health records, helping fill the miscommunication gap.
Introduction
Big data has emerged as a powerful tool in healthcare, offering immense potential to change clinical systems. However, it also brings challenges and risks. This discussion explores one benefit of using big data in clinical systems and a corresponding challenge or risk. A strategy to mitigate the identified challenge or risk will also be discussed.
Benefit
One Significant benefit of utilizing big data in clinical systems is the potential for enhanced clinical decision-making. By analyzing large amounts of structured and unstructured data, including electronic health records (EHRs), patient-generated data, and medical literature, healthcare professionals can gain valuable information that supports evidence-based decision-making. Big data analytics can help identify patterns, trends, and correlations that may not be easily detectable using traditional methods. These insights can aid in diagnosing diseases, personalizing treatments, predicting outcomes, and improving patient safety (McGonigle & Mastrian, 2022).
Challenge/Risk
One major challenge of utilizing big data in clinical systems is ensuring data privacy and security. The increasing volume and complexity of healthcare data pose risks to patient information. Health data contains personally identifiable information (PII), making it attractive to cybercriminals. Combining data from multiple sources raises the risk of unintended data breaches and unauthorized access, potentially compromising patient privacy and confidentiality (Glassman, 2017). Another challenge of data analytics in healthcare is effectively managing and analyzing unstructured data, which requires advanced techniques for text mining, natural language processing, and image recognition to gain meaningful information from sources such as clinical notes, research articles, and medical images (Krylov, 2023).
Mitigation Strategy
Healthcare organizations can adopt a comprehensive approach to secure data governance and encryption to address data privacy and security challenges. This includes implementing strict access controls to limit data access to authorized personnel, using vital encryption techniques to protect data during transmission and storage, removing patient data to minimize the risk of re-identification, and conducting regular audits and monitoring of data access and usage to detect any security incidents (Wang, Kung, & Byrd, 2018). By implementing these measures, healthcare organizations can significantly reduce the risk of data breaches and ensure patient data remains secure and private.
Conclusion
Leveraging big data in clinical systems has the potential to enhance clinical decision-making and improve patient outcomes. However, data privacy and security challenges must be effectively mitigated to realize the full benefits of big data analytics. Implementing secure data governance practices, encryption measures, and regular monitoring can help safeguard patient data and maintain the trust of individuals in the healthcare system.
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
Krylov, A. (2023, March 23). Healthcare data analytics: Major challenges & solutions. Kodjin – Turn-key FHIR Server for Healthcare Data. Retrieved June 27, 2023, from https://kodjin.com/blog/data-analytics-in-healthcare-challenges-and-solutions/#:~:text=One%20of%20the%20challenges%20of,research%20articles%2C%20and%20medical%20images.
McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3–13.
SAMPLE 2
In this week’s discussion, the topic is BIG DATA. According to a heath leaders’ article, big data is “a large complex data set that yields substantially more information when analyzed as a fully integrated data set compared to the outputs achieved with smaller sets of the same data that are not integrated” (The, 2016). In simpler terms, they are inundated with much information such as lab values, imagining reports, point-of-care results, patient assessments, subjective patient symptoms, etcetera. All this information must be analyzed to find the root cause of patients being in the hospital. The whole picture is not just one aspect of a patient, and with big data, you cannot see the forest through the trees.
As I just mentioned, big data can be helpful in healthcare as it allows you to see the larger picture. You can treat the patient as a whole instead of just one concern. In most cases, this is more achievable in outpatient doctor offices as the patient can be seen on several different occasions, and the big data of the patient can be analyzed over time and developed a comprehensive health plan for the patient. “Good preventative services, for example, could help to avoid many of the common reasons why older—particularly frail—people come into contact with accident and emergency services” (Hughes, 2013, p.618). Primary care providers can formulate complete health care for the patient that they collectively can work on over time.
However, in my field, there is the challenge of treating patients in the emergency room who come in with one complaint, yet since everything in the body is connected to everything else, coming in with a small problem might be caused by a different problem. Utilizing big data is only sometimes efficient in the emergency room (ER). In the emergency room, I have observed the challenge of deciphering all the information about the patient in a short amount of time. In some cases, in that setting, we cannot fix or address the root of a patient’s problem and slap a band-aide on the issue and tell them to follow up with their primary care provider.
Unfortunately, having all the data about one patient cannot be permanently fixed. To address the problem, I asked the patient about the most significant issue that brought them to the ER that day. Then I have to focus on just that issue yet still analyze other lab results or imaging and point of care tests to see if a significant issue is causing their specific issue that day. Before the patient leaves, I educate them on their specific problem and advise if there are topics to discuss with their primary provider to address the more significant issue. This can be a challenge in itself, per an article from MEDSURG “Effective teaching helps patients apply health-related knowledge to their lives” (Flanders, 2018, p. 55). Nevertheless, education may only be effectively received if the patient is receptive and focused on the bigger picture.
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.
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Big data is a large complex data set which provides more information when analyzed as a fully integrated data set as compared to the results of smaller sets of the same data which are not integrated (Big Data Means Big Potential, Challenges for Nurse Execs, 2016). In other words, its complicated. The data collected for healthcare comes from many varying sources which include the government, employers, insurance companies, personal electronic devices and public records to name a few. The data is extensive and complex.
One way that we could use big data could be data from wearable devices or cell phones. These devices have evolved to be able to provide some form of preliminary diagnostic testing to prevent health deterioration (Healthcare Big Data and the Promise of Value-Based Care, 2018). As an example, apple products are now capable of performing simple ECG and interpreting the data collected, they can also monitor pulse and even O2 concentrations. If you took a sample collection of this data, you could use it to predict possible outcomes based on the demographic and health data collected when a person first signs up for these services.
The problem with this type of data is that it is too vast to have any real value for evidence base. The data must be controlled or toned down some to have any real value. If we took all the data collected in a 24-hour period from just one person and integrated it into an EMR it would be exhaustive to interpret. But if we toned the data collection down to periods of exercise it would provide a much clearer picture of the persons cardiovascular status at that time.
One way to mitigate the challenges and risks of using big data is through parameters and regulation of its access. As an example, parameters can be set in place which only allow data to be collected from willing participants at set intervals. If the person starts a workout, they would be prompted to allow their data to be collected. Once collected, further parameters would allow specific data to be analyzed with the irrelevant data left behind. Without a data governance strategy and controls, much of the benefit of broader, deeper data access can be lost (lawton, 2022).
References
Big Data Means Big Potential, Challenges for Nurse Execs. (2016, April 19). healthleaders. Retrieved December 25, 2022, from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execsLinks to an external site.
Healthcare Big Data and the Promise of Value-Based Care. (2018, January 1). nejm catalyst. Retrieved December 26, 2022, from https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0290Links to an external site.
lawton, G. (2022, January 5). 10 big data challenges and how to address them. tech target. Retrieved December 26, 2022, from https://www.techtarget.com/searchdatamanagement/tip/10-big-data-challenges-and-how-to-address-themLinks to an external site.
RE: Main Discussion Post- Week 5
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Most of us live in a connected to the world through cellphones, social media, computers, game platforms, and more. That connection seems never to break, even when at work as we carry our phones with us and log to computers. We also help connect patients to database banks, even when they do it even realize it. We live in a world of big data and that data is priceless. It comes with positive outcomes and at times, it can also have adverse effects.
There are possibly countless benefits of big data in the healthcare system, and nurses are the ones responsible for entering most of that data. From the second we get to work and login to the electronic health record (EHR) to the moment we log off, we enter valuable information into computers. That data can be used to develop better protocols, enhance patient safety, better patient outcomes, even ease our nursing profession, and much more.
According to an article published by Health Information Science and System, some of the benefits of synthesizing and analyzing big data are:
The development of more thorough and insightful diagnoses and treatments which could result in higher quality care by analyzing patterns and trends; monitor adherence to drug and treatment regimens and detect trends that lead to individual and population wellness; detecting diseases at earlier stages; reducing readmissions by identifying environmental or lifestyle factors that increase risk or trigger adverse events; adjusting treatment plans accordingly; improving outcomes by examining vitals from at-home health monitors; managing population health by detecting vulnerabilities within patient populations during disease outbreaks or disasters; and bringing clinical, financial and operational data together to analyze resource utilization productively and in real-time. (W. Raghupathi & V. Raghupathi, 2014)
Some challenges have been found along the way, such as the inability to fully implement standardized nursing terminology (SNT), which, if addressed, can improve data analysis. “The use of standardized nursing terminologies (SNTs) to document nursing care enables the easy retrieval and analysis of nursing data while also representing the nurse’s clinical reasoning” (Macieira et al., 2017). SNTs would better communication among nurses and providers, increase the visibility of nursing interventions, improve patient care, and facilitate nursing assessment competency (Rutherford, 2008).
“The lack of data standardization can also make it challenging for a CNE to assess how the organization or a particular unit is performing and to make well-informed decisions about what to change” (Thew, 2018). “Englelbright says that by breaking down data silos, big data will also facilitate a balanced approach to assessing organizational and nursing performance” (Thew, 2018).
As we have discussed and learned throughout this course, nursing informatics and big data, helping our professions and patients, but all these benefits also come with many challenges as well.
What are the odds we get to talk during this week about ‘Big Data’ and its benefits and challenges a week after Hackensack Meridian Health, New Jersey’s largest hospital system experienced a ransomware cyber-attack? Although no patient medical record was reported stolen, personal and financial information, including healthcare insurance data, was stolen.
To regain control over its systems, Hackensack Meridian was forced to pay an undisclosed amount of money in ransom (Eddy, 2019). Having lived in New York City for years, I knew Hackensack was a large hospital system. Still, I was not aware it operated a total of 17 facilities, which includes acute care centers to nursing homes and rehabilitation centers.
“The attack forced hospitals to reschedule nonemergency surgeries and doctors and nurses to deliver care without access to electronic records” (The Associated Press, 2019a). These types of cyber-attacks targeting healthcare facilities are more common than we think. On October 2, 2019, an Alabama hospital system was a victim of a ransomware attack.
As reported, during the cyber-attack, the hospitals involved quit accepting new patients. “The Tuscaloosa News quoted spokesman Brad Fisher as saying the hospital system paid the attackers” (The Associated Press, 2019b). A quick Google search provided over a dozen healthcare facility cyber-attacks in recent years in which patient personal, financial, healthcare insurance, and healthcare records were stolen.
NURS 6051 Discussion: Big Data Risks and Rewards
During these cyber-attacks, the same computers and systems meant to assist our patients were locked and highjacked for ransom. Although the reports mentioned no patient health record was exposed, the investigation at Hackensack is still ongoing. These cyber-attacks have exposed the vulnerability of a system we usually do not associate with cyber-crimes. When think of data breach, banks, government offices, and credit bureaus such as Trans-Union and Equifax come to our minds, not Hackensack Meridian, Mount Sinai, or Swedish Health.
Big data threat is not limited to cyber-attacks, but also internal data mishandling. “One-quarter of all the cases [of healthcare data breaches] were caused by unauthorized access or disclosure – more than twice the amount that was caused by external hackers” (Brooks & Jiang, 2018). Sometimes the data is mistakenly shared with the wrong recipients by hospitals, doctors, pharmacies, and even health insurance companies as not all facilities have strict regulations.
When I think about privacy in healthcare, I initially think of patient privacy and the Health Insurance Portability and Accountability Act (HIPPA). We put all that data and not always know who has access to it. How do we know this data is truly kept private when so many agencies, organizations, and analytic companies have access to it? Who keeps track of what is shared, how it is used, and what is used for? We live ina society where personal data and our digital footprint is worth billions of dollars to companies that want to influence us. How do we ensure patient data does not fall in the hands of them?
References
Brooks, C. & Jiang, X. (2018, November 16). Health care providers – not hackers – leak more of your data. Retrieved from https://msutoday.msu.edu/news/2018/health-care-providers-not-hackers-leak-more-of-your-data/
Eddy, N. (2019, December 16). Hackensack Meridian Health pays up after ransomware attack. Retrieved from https://www.healthcareitnews.com/news/hackensack-meridian-health-pays-after-ransomware-attack
Macieira, T., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2017). Evidence of progress in making nursing practice visible using standardized nursing data: A systematic review. AMIA Annual Symposium Proceedings, 2017, 1205-1214. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977718/
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems,2(3). doi:10.1186/2047-2501-2-3
Rutherford, M. A. (2008). Standardized nursing language: What does it mean for nursing practice? Online Journal of Issues in Nursing, 13(1), 1–12. https://doi.org/10.3912/OJIN.Vol13No01PPT05
The Associated Press. (2019a, December 13). Large hospital system says it was hit by ransomware attack. ABC News. Retrieved from https://abcnews.go.com/Health/wireStory/large-hospital-system-hit-ransomware-attack-67724061
The Associated Press. (2019b, October 5). Report: Alabama hospitals pay hackers in ransomware attack. ABC News. Retrieved from https://abcnews.go.com/Technology/wireStory/report-alabama-hospitals-pay-hackers-ransomware-attack-66084508
Thew, J. (2016, April 19). Big data means big potential, challenges for nurses execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs
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.
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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 4
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.
Thank you for the informative dive into big data in clinical systems and how this technology can augment patient care quality. In the paper, you have highlighted the potential of the technology in consolidating holistic approaches to patient care by providing insights that will enable patients actively take part in their care and make better decisions from both healthcare providers and patients alike.
You have also highlighted the barriers to using big data, mainly in collecting and analyzing data for healthcare providers. Understandably, analyzing big data can be challenging for healthcare providers for several reasons, probably due to the sheer volume of data that needs to be analyzed (Pramanik et al., 2022). Healthcare providers need the right tools and expertise to make sense of all this data and extract useful insights. Analysis can be time-consuming, as it often requires complex algorithms and advanced statistical techniques.
In addition to data mining tools, healthcare centres can also use specialized software and tools. These can include data visualization software, machine learning algorithms, and statistical analysis tools (Ngiam & Khor, 2019). These tools can help to organize and process large amounts of data and provide insights and trends that may not be immediately obv
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