NURS 6051 Discussion: Big Data Risks and Rewards

“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 during this course, nursing informatics and big data are beneficial to our professions and patients, but all of these benefits come with a number of obstacles.

What are the chances that we’ll be talking about ‘Big Data’ and its benefits and challenges this week, just a week after Hackensack Meridian Health, New Jersey’s largest hospital system, was hit by a ransomware cyber-attack? Despite the fact that no patient medical records were reported stolen, personal and financial information, including healthcare insurance information, was stolen. Hackensack Meridian was compelled to pay an undisclosed amount of money in ransom to restore access of its systems (Eddy, 2019). I knew Hackensack had a huge hospital system because I had lived in New York City for years. Still, I had no idea it had 17 facilities, ranging from acute care centers to nursing homes and rehabilitation institutes. “As a result of the attack, hospitals were forced to reschedule non-emergency surgeries, and doctors and nurses were forced to provide treatment without access to electronic data” (The Associated Press, 2019a). These cyber-attacks on healthcare facilities are more widespread than we realize. A ransomware assault hit an Alabama hospital system on October 2, 2019. According to reports, the hospitals implicated stopped taking new patients during the cyber-attack. “According to Tuscaloosa News spokesman Brad Fisher, the medical system funded the attackers” (The Associated Press, 2019b). A short Google search turned up more than a dozen recent healthcare facility cyber-attacks in which patient personal, financial, healthcare insurance, and healthcare records were stolen.

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?

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.

Also Read: Assignment: NURS 6051 Knowledgeable Nurse

RE: Main Discussion Post- Week 5

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).

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

Great post-Nia. To add to the knowledge you shared in your post, I would like to add that nursing is the act of caring, and when we care for patients, we need to consider their safety, privacy, and overall well-being. Health information technology serves as a key to safety by reducing and preventing medical errors, impacting clinical outcomes positively, allowing for collaboration among treatment teams, and keeping track of all patient data (Alotaibi & Federico, 2017). Information technology is now being used across the country to improve patient care and safety (Alotaibi & Federico, 2017).

Information technology is defined by Alotaibi and Federico (2017) as using computer software and hardware to apply information through data storage, retrieval, and sharing of health and medical data to make clinical decisions. Electronic Health Records (EHR) are considered healthcare information technology. Today, EHR is used in many hospitals to document and collect patients medical and family history (HealthIT.gov, 2019). With EHR, all providers involved in particular patient care can access the patient information wherever they may be.

References

Alotaibi, Y., & Federico, F. (2017). The impact of health information technology on patient safety. Saudi Medical Journal, 38(12), 1173–1180. https://doi.org/10.15537/smj.2017.12.20631

HealthIT.gov. (2019, September 10). What is an electronic health record (EHR)? Healthit.gov. https://www.healthit.gov/faq/what-electronic-health-record-ehr

Big Data Risks and Rewards

Big data refers to a large and complex set of data that, when examined as a wholly integrated data, yields essential information than a small unintegrated collection of data. In the contemporary world, big data is increasingly becoming more prevalent, impacting nursing in various ways. Big data offers a nursing system a considerable opportunity to advance the vision of promoting human health and well-being. Although big data analytics is riddled with challenges, it is useful in decision-making in clinical systems.

Big data is a promising breakthrough in health care decision making. Big data analytics in the context of nursing enable an organization to analyze large volumes and velocity of data from various nursing networks to aid in evidence-based decision making and action (Macieira et al., 2017). It allows the integration of clinical information that provides health care insights to help nurses meet patients’ needs and improve the quality of healthcare. Moreover, big data can be used to understand the impact of nursing care and to expand the responsibility to meet continuous emerging needs.

Big data allows clinical systems to realize informatics benefits, including improved quality and accuracy of clinical decision and instant access to vital health records and information. Additionally, big data is a potential source for managerial benefits which allow health care organization to monitor and monitor the firm’s resources and evaluate the operation and support strategic business decisions (Wang, Kung & Byrd, 2018). Furthermore, big data analytics gives the clinical system the capability to generate accurate data and make predictions based on new observations. Predictive analytics play a crucial role in the clinical order of reducing uncertainty and preventing readmissions

Lack of substantial experience in big data analytics is one of the most significant challenges impeding the realization of maximum big data benefits. Evidence indicates that only a few percentages of health care organizations have the capability to conduct rigorous big data analytics to aid in the decision-making process (Wang et al., 2018). The lack of understanding by clinical officers on the value of big data analytic in the clinical system compounds this challenge. Therefore, the clinical system can leverage big data analytics as a means of transforming nursing in the era of informatics.

References

Macieira, T. G., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2018). Evidence of progress in making nursing practice visible using standardized nursing data: A systematic review. AMIA Annual Symposium Proceedings Archive, 1205–1214. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977718/

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, 3-13. doi:10.1016/j.techfore.2015.12.019

Thank you for your articulate post. As you explained big data can be used to understand the impact of nursing care and expand the responsibility to meet continuous emerging needs. Big data can be used in a variety of industries. The top benefits of using big data in healthcare include advancing patient care, improving operational efficiency, finding cures for diseases (Business Wire, 2018). An example of improving operational efficiency would be running reports on readmissions of COPD exacerbations. With this data, a company can examine the admission rates while analyzing staff efficiency. Predictive analytics can be used on the COPD patient to understand key factors in readmissions.

As nurses, we understand that healthcare is continually changing. The same principals can be applied to big data. One of the biggest problems with big data is that “it grows constantly, and organizations often fail to capture the opportunities and extract actionable data” (Joshi, 2018). Because of this, we may miss opportunities to best serve our patients.

References

Business Wire. (2018). Top Benefits of Big Data in the Healthcare Industry. Retrieved from https://www.businesswire.com/news/home/20180207005640/en/Top-Benefits-Big-Data-Healthcare-Industry-Quantzig

Joshi, N. (2018). Problems with Big Data that we failed to notice. Retrieved from https://www.allerin.com/blog/problems-with-big-data-that-we-failed-to-notice

Cybercrime in healthcare indeed puts a patient’s health and privacy at risk. “It is one of the most targeted sectors globally; 81% of 223 organizations surveyed, and >110 million patients in the US had their data compromised in 2015 alone” (Martin, Martin, Hankin, Darzi, & Kinross, 2017, para. 3). We often think about cybercrime in terms of a virus or spyware stealing information, or money, however, cybercriminals are always coming up with new schemes. For example, in 2016 The Hollywood Presbyterian Medical Center’s entire computer system was essentially hijacked for ransom, “shut down its network for ten days, preventing staff from accessing medical records or using medical equipment until the hospital paid the ransom” (Martin et al., 2017, para. 6).

The increased use of nursing informaticists in the healthcare system is a step in the right direction for hospitals as they continue to fight cybercrime while expanding the use of big data. The article, Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations, finds that while hospital systems are investing a large number of finances into big data, much of its capabilities are still underutilized (Wang, Kung, & Byrd, 2018). As hospital systems increase the usage and find new applications for big data, opportunities for cybercrime will increase, and informaticists will have to be ever vigilant in their protection of patient privacy.

Martin, G., Martin, P., Hankin, C., Darzi, A., & Kinross, J. (2017, July 6, 2017). Cybersecurity and healthcare: How safe are we? thebmj, 358. http://dx.doi.org/ 10.1136/bmj.j3179

Wang, Y., Kung, L., & Byrd, T. A. (2018, January 2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. ScienceDirect, 126(), 3-13. http://dx.doi.org/https://doi.org/10.1016/j.techfore.2015.12.019

Dr. Cheryl,

I do agree with your statement about the positives of using a common language within our charting process.  This idea is also true regarding electronic health records (EHRs) in general.  If health systems were able to use compatible EHRs in their hospitals and clinics, the amount of data that could be mined would be massive.  How nice it would be to have the ability to pull records nationally from multiple large health system data banks at once.

One issue with this approach would be the financial expense of either upgrading existing EHRs for interoperability or buying into a different EHR platform altogether.  According to Sines and Griffin (2017), many smaller facilities may not have the funds needed to acquire these “extra” functionalities and only operate base model software.  Any nurse that has worked at multiple hospitals has undoubtedly noticed differing layouts and models of the same EHR being utilized.  I understand the ability to customize the look and charting environment is a selling point, but I am curious as to the interoperability functions of a base model system versus one more luxurious. Ultimately, a national goal with using the EHR is complete connectivity and the linking of data collection and handling (Wilson & Khansa, 2018).

References

Sines, C. C., & Griffin, G. R. (2017). Potential Effects of the Electronic Health Record on the Small Physician Practice: A Delphi Study. Perspectives in health information management, 14(Spring), 1f.

Wilson, K., & Khansa, L. (2018). Migrating to electronic health record systems: A comparative study between the United States and the United Kingdom. Health Policy, 122(11), 1232-1239. https://doi.org/10.1016/j.healthpol.2018.08.013

RE: Discussion – Week 5

Big data affects many aspects of life within a clinical system and outside of it. There are several benefits to the use of big data, one being cost reduction. When speaking of cost reduction, one hospital in England uses analytics to predict admissions over a period, helping hospital systems assign staff based on the needs predicted. This ensures there are no overstaffing issues, increases efficiency, and decreases wait times. (Berke, 2020). Some other benefits are follow-up care, “current” care, and medication error prevention.

One challenge to the use of big data as a part of any clinical system is that it can be overwhelming. Nurse leaders are drowning in data given to them and it is hard to differentiate and analyze it all. Looking at one unit specifically is overwhelming let alone a whole hospital system. There are many different aspects to keep a unit running and having everything in sync all at the same time is rare. A lot of time when dealing with big data the chief nurse executives must look at the big picture for the business side of things and not everything that matters to them most is included in that, and they would then have to fight for it on the side. (HealthLeaders, n.d.). Throughout my research, another risk I saw was sharing patient data within HIPPA guidelines as well as data privacy and regulations issues. Like everything else, there are challenges that go along with any benefit.

One strategy to start with to help with the overwhelming amount of data from many different sources is to try and have a hospital system as a whole use one system. I know in my experience at my hospital the nurses use one system, the doctors use a different system for some things, radiology uses their own system, and dietary uses a different system. All these different systems do not communicate with each other so when attempting to collect data from many different places it is not always the most accurate. It is difficult to navigate and when there is not good, accurate data being analyzed it is problematic to implement the appropriate, productive changes that need to be made to a hospital system. A simpler, more realistic strategy would be data mining projects. Data mining uses a four-phase process to produce a solution: Problem identification, exploration of data, pattern discovery, and knowledge deployment. (McGonigle & Mastrian, 2022). This will help predict trends that can increase the productivity of changes made to accommodate future needs.

References:

HealthLeaders. (n.d.). Big data means big potential, challenges for nurse execs. HealthLeaders Media. Retrieved December 30, 2021, from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Lindsey Berke. Dimensional Insight. (n.d.). Retrieved December 30, 2021, from https://www.dimins.com/blog/2020/03/02/big-data-healthcare/#:~:text=Top%20Advantages%20of%20Big%20Data%20in%20The%20Healthcare,many%20times%20there%20are%20lives%20on%20the%20line.

McGonigle, D., & Mastrian, K. (2022). Nursing Informatics and the foundation of knowledge. Jones et Bartlett Learning.

RE: Discussion – Week 5

Big data can be defined as some combination of the following characteristics: volume, variety, veracity, value, and velocity (Morton, 2014). The success of an organization is related to what data they have and how they use the data. Data is readily available and closest to real time that it has ever been, and organizations do not have to wait until month end reports are generated to gain access to the data (McGonigle & Mastrian, 2017).  Organizations need to manage and utilize all of their resources efficiently to be cost effective and successful. Big data can be used within an organization to analyze current trends based upon data and make strategic business moves based upon the data and the direction the organization wants to go to further success. One way an organization can use big data to be successful is to prevent fraud within the organization which will decrease overall healthcare costs. The most common occurrences in fraud within the United States are up-coding for services, charging for a service that was not received, or preforming medically unnecessary services to receive insurance payment (Georgakopoulos et al., 2020). Raw data is analyzed for outliers and standard pricing for procedures to assist in detecting fraud within healthcare. Organizations should be creating a culture where they thrive on the use of big data, develop data competencies, and to create an operation infrastructure to support big data use (Morton, 2014).

Problematic issues using big data are issues creating the full patient narrative to gain access to the full healthcare picture. The data presented in flowsheets and documentation are little pieces of information that the nurse has to put together to create a patient story (Glassman, 2017). The provider and the nurse are responsible to analyze data and use what information the patient provides during assessment as knowledge to make healthcare decisions on the best course of treatment for the patient. According to Glassman, one solution to create a full patient story is to allow nurses more time to spend for proper assessment so details do not get missed, and to be more active in creating changes within the electronic health record to improve workflow and increase patient safety (2017).

Resources

Georgakopoulos, S. V., Gallos, P., & Plagianakos, V. P. (2020). Using Big Data Analytics to Detect Fraud in Healthcare Provision. 2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering (MECBME), Biomedical Engineering (MECBME), 2020 IEEE 5th Middle East and Africa Conference On, 1–3. https://doi.org/10.1109/MECBME47393.2020.9265118

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

McGonigle, D., & Mastrian, K. (2017). Nursing Informatics and the Foundation of Knowledge (4th ed.). Jones & Bartlett Learning.

Morton, J. (2014). Big data: opportunities and challenges. BCS, The Chartered Institute for IT.

Name: NURS_5051_Module03_Week05_Discussion_Rubric

Name: NURS_5051_Module03_Week05_Discussion_Rubric