Discussion: Big Data Risks and Rewards NURS 6051

Big Data (Initial Discussion Response)

After the new Health Information Technology Act (HITECH) of 2009 went into effect, the electronic health record (EHR) became the largest application of big data in the healthcare industry. The HITECH act in the U.S. have been the reason why 80% of acute care hospitals that uses the EHR are certified (Ross, 2014, p. 97). The one benefit of the EHR program is the ability of the platform to absorb large amounts of data such as a patient demographics, medical history, allergies and laboratories values. This makes it easy for clinicians to improve patient quality of care by tracking progress and identifying potential problems early in their medical history. If a clinician attempts to order norco for pain, the EHR will display a best practice advisory (BPA) alert and flag the patient codeine allergy; this will redirect the clinician to considering another pain medication that would not put the patient at risk for an adverse drug reaction. Adverse drug events are estimated to occur in 30% or more of hospital stays and cost billions of dollars (Ross, 2014, p. 98). This is a great benefit big data have provided in the development of the EHR program.

The major challenge with EHR is keeping patient health information safe by preventing access to unauthorized individuals. At my facility, the IT department have assigned employee education modules on cyber-attacks. For example, an employee received an email from an IT associate about a Hospital EHR update and upon request, gave their login name and password. Shortly after the information was given, the cyber attacker was able to login into the EHR, steal approximately 50 patient’s information and changed all the employee direct deposit bank accounts to an off shore financial institution in the Caribbean. Luckily, the IT department was notified immediately and was able to prevent the massive transfer of money, but was not successful in protecting the patient’s healthcare data.

After this sentinel event, all employees are quarterly required to take education modules on the best practices to mitigate the challenges of a cyber-attack. It is important to never give your password out and to always encrypt your emails. The new tiger connect system at my facility have features to encrypt your passwords and email before sending a message. In order to hold employees accountable, leadership have started to audit employees to ensure their following the safety protocols. Sometimes fake emails will be sent out to employees to ensure everybody understand there is never a right time to give their login information and the IT associates will never ask for this information.

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At the hospital, there are policies and procedures that serve to maintain patient privacy and confidentiality; for example, employees must not share their ID with anyone, always log off when leaving a computer and only use their own ID to access patient digital records (Jamshed, 2015, p. 75). We use a new device called WaveID, where your password automatically log in after waving your employee ID over a keyboard magnet. It is a convenient and easy for an individual to steal your badge, which is why it is important to follow the policy in place and mitigate the risk of another cyber-attack.

References:

Jamshed, N., Ozair, F., Sharma, A., & Aggarwal, P. (2015). Ethical issues in electronic health records: A general overview. Perspectives in Clinical Research, 6(2), 73–76. https://doi.org/10.4103/2229-3485.153997Links to an external site.

Ross, M. K., Wei, W., & Ohno-Machado, L. (2014). “Big data” and the electronic health record. Yearbook of Medical Informatics, 23(01), 97–104. https://doi.org/10.15265/iy-2014-0003Links to an external site.

Kruse, C. S., Smith, B., Vanderlinden, H., & Nealand, A. (2017). Security techniques for the electronic health records. Journal of Medical Systems, 41(8), 1–10. https://doi.org/10.1007/s10916-017-0778-4Links to an external site.

https://onlinenursingessays.com/discussion-big-data-risks-and-rewards-nurs-6051/

Big data risks and rewards

Organizations often use data with new developments in organization. Organizations use information to organize and deliver quality health care services to clientele. Big data in the healthcare sector, therefore, refers to huge volumes of data generated from the adoption of digital technologies and interactions between healthcare stakeholders and healthcare systems in the collection, documentation, and retrieval of healthcare data (Wang et al., 2018). For example, government agencies use big data through research studies and laboratory results to manage and organize their performances. Big data can also help and organization to predict changes and trends in diseases with age groups to form evidence-based data, thus forming interventions to enhance the quality of life, value, and cost reduction in patient care services.

Electronic documentations are used a lot more in health care organizations. This is used by collecting and documenting large data in detailed entity of patient care; however, this can lead to flooding of information systems making data unmanageable. One of the greatest challenges of big data is the access to patient data regarding proprietary rights, privacy, and interoperability. According to Perlin (2016), interoperability is the ability of healthcare information systems to exchange vital health data within and across organizational boundaries and present it in a way that is understandable to the user.

The HIPAA (Health Insurance Portability and Accountability) often becomes unconsidered for access to patient data is repeatedly breeched, which interferes with healthcare professionals’ ability to share and document patient information effectively. Sharing data, within the parameters of the Health Insurance Portability and Accountability Act, supports the meaningful use of EMRs to distribute patient information in health care (McGonigle et al, 2017).

An essential strategy to solve the accessibility challenges in big data sharing is the implementation of frequent security evaluations and procedures. This action could be carried out by encrypting big data and ensuring that health care professionals are practicing professional integrity. Many health care systems should strive to have mature EMR systems to support meaningful use and honesty. upgrading pre-existing information systems within health facilities will enhance the ability to share health information between providers and between health facilities efficiently (Perlin, 2016). This strategy I believe would aid in eliminating patient privacy breaching and risks associated while sharing big data amongst providers. There is an urgent need to understand the managerial, economic, and strategic impact of big data analytics and explore its potential benefits driven by big data analytics, and this will enable healthcare practitioners to fully seize the power of big data (Wang et al, 2018).

References

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

Perlin, J. B. (2016). Health information technology interoperability and use for better care and evidence. Jama, 316(16), 1667-1668. doi:10.1001/JAMA.2016.12337

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. doi: 10.1016/j.techfore.2015.12.019.

When you wake up in the morning, you may reach for your cell phone to respond to a few missed text or email messages. You may need to stop on your way to work to refuel your car. You may be required to swipe a key card at the door upon your arrival to gain access to the facility. Finally, before proceeding to your workstation, you may wish to purchase a cup of coffee from the cafeteria.

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 Discussion: Big Data Risks and Rewards NURS 6051 cond 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.

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.

The use of big data technology is enabling care providers know more about their patients and offer more effective care. Yaqoob et al. (2016) defined big data as data that is difficult to manage using traditional data management methods due to its large size, complexity, and velocity. Collecting and processing such data to produce meaningful information requires specialized technologies. However, studies have shown that big data analysis provides more detailed and accurate information than other forms of data. Hence, big data is widely used in clinical systems to improve healthcare processes and patient care. In my facility, big data from wearables is used to deliver timely medical intervention despite the risk of information theft.

Wearable technology is a facet of health information technology (HIT) that has improved the quality and timeliness of care offered to outpatients especially people with cardiac or respiratory illness. Wearable devices transmit patient data such as heart rate, blood pressure, and respiration rate to their care providers remotely (Wu & Luo, 2019). In my facility, we use this technology to track the condition of certain patients with heart and respiratory diseases remotely. A benefit of this technology is that it enables clinicians to intervene in the shortest possible time. When the heart rate of a patient with hypertension spikes or goes above the recommended level, the system sends an alert so that a medical practitioner can intervene by reaching out to the patient to recommend a solution or delivering care if necessary. Another benefit to using wearable technology is that it reduces the cost of in-patient care (Wu & Luo, 2019).

Despite the obvious advantages of using wearable devices, the technology possesses some risks. A major risk with using wearable technology is patient data security. Cillers (2020) asserted that wearable devices are vulnerable to cyber attacks because the data is usually sent through unsafe public wifis and cell data. This means that the weakest security link is in the intermediary connection. Thus, while using these unsafe networks to transmit sensitive data, cyber criminals can intercept such data. This problem is much more prominent when the identity of the patient is sensitive. To mitigate this issue, Wang et al. (2016) recommend using additional layers of security to checkmate the weak link. This strategy involves connecting the wearable device to only trusted networks and regularly updating the software to stay up-to-date with the latest security.

References

Cilliers, L. (2020). Wearable devices in healthcare: Privacy and information security issues. Health information management journal, 49(2-3), 150-156. https://doi.org/10.1177/1833358319851684

Wang, S., Bie, R., Zhao, F., Zhang, N., Cheng, X., & Choi, H. A. (2016). Security in wearable communications. IEEE Network, 30(5), 61-67. http://doi.org/10.1109/MNET.2016.7579028

Wu, M., & Luo, J. (2019). Wearable technology applications in healthcare: a literature review. Online J. Nurs. Inform, 23(3). https://www.himss.org/resources/wearable-technology-applications-healthcare-literature-review

Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., & Vasilakos, A. V. (2016). Big data: From beginning to future. International Journal of Information Management, 36(6), 1231-1247. https://doi.org/10.1016/j.ijinfomgt.2016.07.009

Discussion: Big Data Risks and Rewards NURS 6051 By Day 6 of Week 5

Two days apart, provide at least two of your coworkers with further mitigation techniques or insight into their view of the opportunities and hazards associated with big data.

Please be aware that your classmates will be referred to as “colleagues” throughout this course.

Discussion – Collapse of Week 5

The use of big data in healthcare empowers patients to have a greater say in their treatment and care decisions. This is a stated goal in the HITECH Act of 2009. (Glassman, 2017). One of the issues is developing a HIPAA-compliant and user-friendly interface to collect information from patients. Epic software is used in the electronic health record system at my hospital (EHR). We have an 844-bed level-1 trauma hospital, a children’s hospital, and several outpatient acute care hospitals, as well as various surgical centers and an 89-bed inpatient psychiatric facility that offers partial hospitalization and intensive outpatient programs. These hospitals treat hundreds, if not thousands, of patients every day. The amount of information generated as a result of these meetings is almost incomprehensible. Another significant issue is gathering this data and analyzing it in useful ways.

According to Glassman (2017), nurses are the most important EHR documentation contributors in healthcare settings because they enter the most patient data into the EHR. In my opinion, direct EHR input from the patient should be just as important. When involving patients in their own care and allowing them to make their own healthcare decisions, it is an ethical requirement to consider patient preferences, culture, and values (McCormack & Elwyn, 2018). The EHR can be directly communicated with using big data and an app-based interface. In this situation, my hospital’s preferred tool is My Chart.

MyChart

In order to create a safe, HIPAA-compliant interface that is user-friendly, My Chart has surmounted the obstacle. Epic Systems Corporation has developed a mobile app called MyChart. A mobile or desktop computer can access MyChart, which is secure, handy, and accessible at any time (MyChart, 2020). Using MyChart, users can add data to their own electronic health record. MyChart can also be used to prompt patients to provide additional information. An email notice or an app notification can be delivered to the patient to notify them of things like test results, appointment reminders, and other important information. This is something that the patient has control over.

Discussion: Big Data Risks and Rewards NURS 6051

Direct EHR data input is also possible with MyChart, which includes personal preferences like native language, prescription refill requests and appointment scheduling. The patient’s involvement in their own healthcare can have a positive impact on the patient’s outcomes. As an example, if the patient’s first language is not English, then translation services may be necessary in order to ensure that the healthcare team is aware of the patient’s grasp of the informed consent process.

MyChart’s use in patient communication and care coordination allows my company to make better use of the massive amounts of data it collects. Without the need for a face-to-face appointment, patients can access their medical records and contact directly with their providers. This efficiency enhances the software interface’s advantages.

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Trying to Make Sense of All That Data

It has been a problem for my business to analyze and report on the vast amount of data that is stored in the EHR. An individual can become unwell if one of their body’s systems is disrupted, and an EHR with several interconnected systems can suffer the same fate (Thew, 2016). At my company, this is the case. When it comes to mental health information, the EHR interface is designed primarily to collect medical data, which makes it difficult and ineffective to collect narrative information. Daily notes require providers to type in a significant amount of narrative content. To make matters worse, they must sift through several narrative notes in order to discover relevant data, which is a time-consuming and ineffective process. It has come to our attention as a result of reviewing incident reports that critical information can be overlooked by providers, who then fail to include it in their notes for consecutive days. Essentially, the story loses information.

Discussion: Big Data Risks and Rewards NURS 6051

Recently, the business discovered that reporting and data analysis can be improved by capturing data discretely, such as by selecting flowsheet rows, or by selecting other selectable fields in the EHR. This is an on-going problem, and we’re doing everything we can to solve it. RN daily shift note templates have been studied and disassembled to provide separate data fields for which reports can be created. One example of this is group attendance.

In the psychiatric regions, there are weekly groups for both therapeutic and educational purposes. When a patient comes in, the RN can quickly select one of these group kinds by programming it into a quick-click field. Administrators can run reports based on this data if it is collected discretely. A report on group attendance, for example, can reveal which groups have the highest levels of attendance. After that, it will be possible to make decisions on which teams to keep, drop, or reschedule. An added benefit of this reporting capability is that it allows for staffing to be altered in accordance with group attendance.

Conclusion Efficient and discrete data capture will be critical in helping nursing leaders make sense of the massive volumes of patient care data generated today, as well as improving patient care, outcomes and staff satisfaction.

References

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

McCormack, J. & Elwyn, G. (2018). Shared decision is the only outcome that matters when it comes to evaluating evidence-based practice. BMJ Evidence-Based Medicine 23(4), 137-139. Retrieved from https://ebm.bmj.com/content/23/4/137.info

MyChart. (2020). Join over 100 million patients who manage their care with mychart. Retrieved from https://www.mychart.com/

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. Health Leaders. https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Submission and Grading Information

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Potential Benefit

There are a plethora of benefits that come with Big Data in healthcare technology. It has and continues to improve care efficiency, care decisions, diagnostics, and the like.

Gupta et al. (2023) noted that Big Data improves healthcare by analyzing public health, behavioral patterns, treatment monitoring, etc. Big data is actively infused via electronic medical records, which makes caring for the sick safer.

Potential Challenge

It is without doubt that the pace at which new information is being created has accelerated since the turn of the century, wherein big data has surged from 2 to 59 zettabytes (Gupta et al., 2023). The mining of these gigantic data sets comes with major risks and challenges. Security is a major risk in the processing, managing, and storing of big data. Gupta et al. (2023) cited privacy, security, and usability as “issues on which the successful implementation of big data technologies in the healthcare sector are dependent” (p. 3).

Proposed Strategy

It is important to note that this process is interdependent; those who mine and analyze the data must do it with absolute accuracy. This is important because the “data you get out is as good as the data you put in” (Marion, 2016, p. 2). There are many strategies that have been proposed to help mitigate the security issues surrounding Big Data in healthcare. Centralized organization of analytics specialists, individual analytics services, and infrastructure to deliver meaningful access are just but a few of the many solutions for the usability of Big Data in healthcare (Marion, 2016).

In my experience, I have seen firsthand the impact of patients’ medical records or patient information being breached. Two-factor authentication coupled with access code verification for each time the record is being accessed is a means by which this issue could be resolved. In addition, methods employed by government agencies like the IRS and other major corporations might be worth a try. And, of course, a robust security monitoring firm. Being a victim of identity theft can is a nightmare. I had lived in constant fear since 2005, when my identity was stolen. This life-changing issue needs the understanding and compliance of all stakeholders involved.

Reference

Gupta B. B., Gaurav A., Panigrahi K. p., (2023). Analysis of security and privacy issues of information management of Big Data in B2B based healthcare systems. Journal of Business Research. ScienceDirect. Vol. 162, https://doi.org/10.1016/j.jbusres.2023.113859

Links to an external site.

Marion, J. (2016, August 11). Big Data: Only as Good as Analytics Capability. Retrieve march 29, 2023, from https://www.hcinnovationgroup.com/analytics-ai/blog/13027293/big-data-only-as-good-as-analytics-capability

Next Module

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Module

Module 4: Technologies Supporting Applied Practice and Optimal Patient Outcomes (Weeks 6-8)

Laureate Education (Producer). (2018). Informatics Tools and Technologies [Video file]. Baltimore, MD: Author.

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Learning Objectives

Students will:

Discussion: Big Data Risks and Rewards NURS 6051

Evaluate healthcare technology trends for data and information in nursing practice and healthcare delivery
Analyze challenges and risks inherent in healthcare technology
Analyze healthcare technology benefits and risks for data safety, legislation, and patient care
Evaluate healthcare technology impact on patient outcomes, efficiencies, and data management
Analyze research on the application of clinical systems to improve outcomes and efficiencies
Due By
Assignment
Week 6, Days 1–2
Read/Watch/Listen to the Learning Resources.
Compose your initial Discussion post.
Week 6, Day 3
Post your initial Discussion post.
Begin to compose your Assignment.
Week 6, Days 4-5
Review peer Discussion posts.
Compose your peer Discussion responses.
Continue to compose your Assignment.
Week 6, Day 6
Post at least two peer Discussion responses on two different days (and not the same day as the initial post).
Week 6, Day 7
Wrap up Discussion.
Week 7, Days 1-7
Continue to compose your Assignment.
Week 8, Days 1-6
Continue to compose your Assignment.
Week 8, Day 7
Deadline to submit your Assignment.

Discussion: Big Data Risks and Rewards NURS 6051

Learning Resources

Required Readings

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

Chapter 14, “The Electronic Health Record and Clinical Informatics” (pp. 267–287)
Chapter 15, “Informatics Tools to Promote Patient Safety and Quality Outcomes” (pp. 293–317)
Chapter 16, “Patient Engagement and Connected Health” (pp. 323–338)
Chapter 17, “Using Informatics to Promote Community/Population Health” (pp. 341–355)
Chapter 18, “Telenursing and Remote Access Telehealth” (pp. 359–388)

Discussion: Big Data Risks and Rewards NURS 6051

Dykes, P. C., Rozenblum, R., Dalal, A., Massaro, A., Chang, F., Clements, M., Collins, S. …Bates, D. W. (2017). Prospective evaluation of a multifaceted intervention to improve outcomes in intensive care: The Promoting Respect and Ongoing Safety Through Patient Engagement Communication and Technology Study. Critical Care Medicine, 45(8), e806–e813. doi:10.1097/CCM.0000000000002449

HealthIT.gov. (2018c). What is an electronic health record (EHR)? Retrieved from

https://www.healthit.gov/faq/what-electronic-health-record-ehr

Rao-Gupta, S., Kruger, D. Leak, L. D., Tieman, L. A., & Manworren, R. C. B. (2018). Leveraging interactive patient care technology to Improve pain management engagement. Pain Management Nursing, 19(3), 212–221.

Skiba, D. (2017). Evaluation tools to appraise social media and mobile applications. Informatics, 4(3), 32–40.

Required Media

Laureate Education (Producer). (2018). Public Health Informatics [Video file]. Baltimore, MD: Author.

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Laureate Education (Producer). (2018). Electronic Records and Managing IT Change [Video file]. Baltimore, MD: Author.

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RE: Discussion – Week 4

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“Data is a discrete set of details related to a specific situation, patient, or population,” according to Laureate Education (2012). According to Laureate Education (2012), data becomes information, which becomes knowledge, which becomes wisdom, and so on. According to Thew (2016), big data is “a large complex data set that yields significantly more information when analyzed as a fully integrated data set when compared to the outputs achieved with smaller sets of the same data that are not integrated.”

Discussion: Big Data Risks and Rewards NURS 6051

Organizing and making sense of big data is a daunting task, but when done correctly, it has the potential to achieve great things. Preventative medicine/applications are one of the most exciting ways to use big data in my opinion (Vinay Shanthagiri, 2014). Medicine can be proactive rather than reactive by utilizing big data.

Big data knowledge encourages preventive health measures. Big data trends derived from a population of people diagnosed with hypertension, for example, reveal potential risk factors. Big data can lead to potential preventative factors when compared to those who do not have hypertension. Obtaining data on these patients yields information about potential risk and preventative factors. This information becomes knowledge, such as poor diet and lack of exercise, both of which are risk factors for hypertension. Based on this, healthcare providers can educate patients on wise preventative measures (such as improving diet and exercise) to help them avoid developing hypertension. Big data analysis is exciting because it can be applied to so many different illnesses, allowing healthcare to finally take a proactive rather than reactive approach.

The one aspect of big data that scares me is data security. Nobody wants their personal information to be public. I know I don’t want anyone passing by to see or hear my weight when I go to the doctor. Consider the possibility that all of your personal information, including your age, date of birth, diagnosis, medications, and surgeries, is available to anyone and everyone.

Dual verification is required at the hospital where I work. This is an extra layer of security. Employees log in using their unique employee number and a complex password (that changes every month). A PIN number is also sent to your phone or email by the program. Before accessing any company or patient data, this PIN number must be entered.

References

Laureate Education (Executive Producer). (2012). Data, Information, knowledge and wisdom continuum [Multimedia file]. Baltimore, MD: Author. Retrieved from http://mym.cdn.laureate-media.com/2dett4d/Walden/NURS/6051/03/mm/continuum/index.html

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

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

Vinay Shanthagiri. (2014). Big Data in Health Informatics [Video file]. Retrieved from https://www.youtube.com/watch?v=4W6zGmH_pOw

RE: Initial post – Week 4

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Evidence Based medicine is the core of the USA healthcare system. One cannot deny the fact that data is an integral part of evidence-based medicine. As mentioned at the description of discussion, we used, and we leave data from the time we wake up to the time we go to bed and during sleep as well. EMR has helped to reduce medical errors and work a bit easily. In a recent review exploring the definition of “big data,” Ward and Barker amalgamate concepts of size, complexity, and technology to define “big data” as “the storage and analysis of large and/or complex data sets using a series of techniques including…machine-learning” (Ward & barker, 2013)

As you mentioned, checking boxes during an assessment prevents an error. At the same time, it has become very tedious task and monotonous charting for the patient. since EMR’s widespread implementation into the healthcare system, electronic medical records have been met with resistance. Commonly cited barriers to usage are the time it takes to learn and use an EHR, workflow disruption, poor communication between users, lack of interoperability, and technical problems (Ajami and Bagheri-Tadi, 2013).

EMR is a technology advantage and like as you mentioned, it is not perfect either. There are multiple EMR options available in the market and each one has its pros and cons. An excellent example of innovative electronic data collection is the system used by participants in the Nightingale Tracker System pilot study, in which nursing students traveling to rural clinical sites submitted information via handheld devices while miles away from their preceptor- supervisor. (McGonigle & Mastrian, 2018, p.470).

As mentioned In the article of “Big Data Means Big Potential, Challenges for Nurse Exec” Failure to recognize how this data interacts throughout the system is a big challenge. (Thew,2016). In your scenario with the Integumentary system, the system doesn’t have all available options and hence staff has to enter details manually. We had same problem as well and hence my organization went for optimization 15 months after launching their new EMR system. It is a continuous learning process and data defiantly helps us to understand the progress.

References

Ajami, S., & Bagheri-Tadi, T., (2013). Barriers for adopting electronic health records (EHRs) by physicians. Acta Informatica Medica, 21(2), 129-134. doi: 10.5455/aim.2013.21.129-134

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

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

Ward JS, Barker A. Undefined By Data: A Survey of Big Data Definitions. arXiv:1309.5821; 2013.

Discussion Big Data Risks and Rewards NURS 6051 Rubric Detail

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RE: Discussion – Week 5

As technology progresses, so does the capacity to collect and analyze massive data sets from a variety of distinct sources. Big data is the product of such massive information. Big data refers to the enormous quantity of unstructured datasets that an organization must handle (McGonigle & Mastrian, 2018). Electronic health records (EHR) have enabled more efficient access to all aspects of current healthcare paperwork as well as previous data via backup and storage media.
Utilizing an EHR on a regular basis gives a continuous data set that can be easily queried and absorbed to provide information that can then be used to affect positive patient outcomes.

Trends in the documentation determined to be valuable during the monitoring and administration of patient care can be analyzed and used to guide future policy and practice modifications. Due to the universal language of the majority of charting modules inside a health system, it is simple to search and mine the data for a particular indicator. When a query attempts to retrieve charting details from an unstructured area, such as narrative charting entries, an issue develops.
So long as EHRs permit custom narrative entries, it will be time-consuming and labor-intensive to retrieve ordered system-wide search results. The unstructured data must then be inspected, read, and sorted manually. A clinical system’s lack of integration is a prime illustration of how large data mining may be burdensome and onerous (Wang et al., 2018).
Using a checkbox flowsheet approach of universal charting is a technique used to alleviate the difficulty of huge data. This technique’s standardized style gives the informaticist with ordered, easily accessible, and easily interpreted results (Glassman, 2017). Although integrating many evaluation categories in a single area can make the narrative approach more efficient at times, the information may be invisible and so inaccessible for the intended project.

References

Glassman, K. S. (2017). Utilization of data in nursing practice. 12(11), pages 45–47 in American Nurse Today. Obtainable at http://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf.
D. McGonigle and K. Mastrian (2018). Nursing Informatics and the Knowledge Foundation (4th ed.). The company Jones and Bartlett learning.
Wang, Y., Kung, L., & Byrd, T. (2018). Understanding the capabilities and potential benefits of big data analytics for healthcare businesses. 126, 3-13 in Technological Forecasting and Social Change.
https://doi.org/10.1016/j.techfore.2015.12.019

Replies

I concur that technological progress improves the capacity to analyze a huge quantity of data from many sources. The adoption of electronic health records has simplified healthcare recordkeeping, hence boosting patient safety. (Jagadeeswari et al., 2018) Big data facilitates the storage of massive amounts of information that are difficult to manage with paper documentation. I agree with you that the usage of electronic health records facilitates the provision of a continuous data set that facilitates the development of information used to influence a favorable patient outcome.

Unstructured areas present difficulty in retrieving charting information, notwithstanding the advantages that big data bring to healthcare operations. However, the difficulty can be circumvented by adopting a checkbox flowsheet technique to facilitate universal charting (Brown et al., 2017). Your post has informed me that it is difficult to allow custom narrative entries due to the labor-intensive nature of the organized system. Which should be the most effective narrative method to enable the visibility of information for the current project?

References

Brown, R. T., K. D. Komaiko, Y. Shi, K. Z. Fung, W. J. Boscardin, A. Au-Yeung,… & M. A. Steinman (2017). Validation of national Veterans Affairs functional status data in a big data world. PloS one, 12(6), e0178726. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178726

Jagadeeswari, V., Subramaniyaswamy, V., Logesh, R., & Vijayakumar, V. (2018). An examination of the medical Internet of Things and Big Data inside the personalized healthcare system. Health Information Science and Systems, volume 6, number 1, pp. 1-20.
https://link.springer.com/article/10.1007/s13755-018-0049-x

I agree with you that organizations use big data for new developments. However, the nature of the data may vary depending on the type of the organization. For instance, healthcare organizations may have detailed data about issues related to the health status of their patients. Big data are generated from digital space (Mehta & Pandit, 2018). Besides, the interactions between the healthcare stakeholders and the patients provide huge volumes of data. Big data is crucial in generating information that can be used to explain trends in certain health complications and predict changes (Pastorino et al., 2019).

Unfortunately, working on the big data may be hectic and tedious to some people due to the huge volume. Besides, people handling the big data may fail to protect the data from malicious use. As a result, the data may be misused without the owners’ consent. In healthcare institutions electronic documentations are used to perform various obligations (Kaur et al., 2018). Therefore, healthcare providers gather confidential information from their clientele. Work ethics require healthcare professionals to protect patient health information from an unauthorized access.

References

Kaur, P., Sharma, M., & Mittal, M. (2018). Big data and machine learning based secure healthcare framework. Procedia computer science, 132, 1049-1059. https://doi.org/10.1016/j.procs.2018.05.020

Mehta, N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics, 114, 57-65. https://doi.org/10.1016/j.ijmedinf.2018.03.013

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. https://doi.org/10.1093/eurpub/ckz168

RE: Discussion – Week 5 Initial Post

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Healthcare settings are involved with a lot of technology that holds a very large amount of data. From patient names and demographics to

lab results and diagnoses, the patient’s information is very important to keep private. The implementation of patient portals has been a very

big help for patient’s and getting information to and from their physicians. The patient can check in prior to their appointments by filling out

their paperwork online. This allows for the front desk to lessen their workload and to be better able to help the patient. “The patient portal allows

for better patient communication, keeping front office work efficient, better patient-physician relationships due to 24hr access, allowing

improvement of clinical outcomes such as prescription refills” (DeMarco, 2017). Patient portals have been implemented in both hospitals and

clinics. They allow the patient to look at any results, ask for prescription refills, and set up appointments all from the comfort of their home and

not having to wait on the phone or leave a message for someone to get back with them.

Although the patient portal is a great tool for the physicians and staff of healthcare facilities, they also have some flaws. “Some risks

include: reliance as a sole method of communication, possible security breaches resulting in HIPAA violations, and posting of critical

diagnostic results prior to provider discussions” (mlmic.com, 2021). Patient information breach is the number one issue that could happen when

using the patient portal. There is no computer system that is perfect, but it is extremely important to protect the patient’s information.

One way to protect the patient’s information is to “make sure private health information is safe from unauthorized access, is hosted on secure

connections, and accessed via an encrypted password-protected logon. Also, remember to remind patients to protect their username and

password” (Heath, 2016). Patient portals are great when used correctly. It is important to educate both the patient and the healthcare staff about

the benefits and risks of their patient portal. Patients just need to understand that it is a wonderful tool, as long as they understand how to use

it.

References

DeMarco, A. (2017). Patient portal: what makes it so great for a provider? micromd.com.

Heath, S. (2016). What are the top pros and cons of adopting patient portals? patientengagementhit.com.

mlmic.com. (2021). Risk management tip: the proper use of patient portals.

Hello Maria, I concur with you that the development of technology related to healthcare has led to the adoption of big data, which covers different segments of healthcare, including disease and wellness. In other words, the foundation of big data has necessitated access to a patient’s information with a click of a button (Thew, 016). In the healthcare sector, healthcare practitioners can derive patient medical records electronically with the foundation of big data. Similarly, a healthcare professional can be able to understand whether a patient is ill and the disease they are battling by applying big data machines. The use of big data has subjected healthcare practitioners to a position where they can make informed decisions. This is because they are supplied with the information, they need to develop decisions (Wang et al., 2018). Similarly, the service delivery to the clients have improved with the foundation of big data. This comes from the fact that; nurses can process electronic records faster, ensuring a patient proceeds to the next process. Similarly, big data ensures a patient’s health data is analyzed and predictions made or even create recommendations about the clinical action required.

On the other hand, as you have asserted, big data is characterized by risks. This comes from the fact that there are challenges to information unfolded by the big data machines that will be utilized to improve the quality of services to patients. This is because there is a variety of data being generated; hence it becomes difficult to analyze and apply the results. Similarly, big data makes healthcare professionals work under pressure as they are required to meet a particular value (Glassman, 2017). Without big data, the use of values to mark a performance was not possible as there was slow generation and integration of data. I agree with you that a system that patients can access can help mitigate the risks associated with big data. This is because there will be transparency; hence patients and other stakeholders could verify the misuse of patient’s data. Establishing ethical standards can also discourage misuse of a patient’s data hence enhancing confidentiality.

Name: NURS_5051_Module03_Week05_Discussion_Rubric

	Excellent	Good	Fair	Poor
Main Posting	Points Range: 45 (45%) – 50 (50%) Answers all parts of the discussion question(s) expectations with reflective critical analysis and synthesis of knowledge gained from the course readings for the module and current credible sources. Supported by at least three current, credible sources. Written clearly and concisely with no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.	Points Range: 40 (40%) – 44 (44%) Responds to the discussion question(s) and is reflective with critical analysis and synthesis of knowledge gained from the course readings for the module. At least 75% of post has exceptional depth and breadth. Supported by at least three credible sources. Written clearly and concisely with one or no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.	Points Range: 35 (35%) – 39 (39%) Responds to some of the discussion question(s). One or two criteria are not addressed or are superficially addressed. Is somewhat lacking reflection and critical analysis and synthesis. Somewhat represents knowledge gained from the course readings for the module. Post is cited with two credible sources. Written somewhat concisely; may contain more than two spelling or grammatical errors. Contains some APA formatting errors.	Points Range: 0 (0%) – 34 (34%) Does not respond to the discussion question(s) adequately. Lacks depth or superficially addresses criteria. Lacks reflection and critical analysis and synthesis. Does not represent knowledge gained from the course readings for the module. Contains only one or no credible sources. Not written clearly or concisely. Contains more than two spelling or grammatical errors. Does not adhere to current APA manual writing rules and style.
Main Post: Timeliness	Points Range: 10 (10%) – 10 (10%) Posts main post by day 3.	Points Range: 0 (0%) – 0 (0%)	Points Range: 0 (0%) – 0 (0%)	Points Range: 0 (0%) – 0 (0%) Does not post by day 3.
First Response	Points Range: 17 (17%) – 18 (18%) Response exhibits synthesis, critical thinking, and application to practice settings. Responds fully to questions posed by faculty. Provides clear, concise opinions and ideas that are supported by at least two scholarly sources. Demonstrates synthesis and understanding of learning objectives. Communication is professional and respectful to colleagues. Responses to faculty questions are fully answered, if posed. Response is effectively written in standard, edited English.	Points Range: 15 (15%) – 16 (16%) Response exhibits critical thinking and application to practice settings. Communication is professional and respectful to colleagues. Responses to faculty questions are answered, if posed. Provides clear, concise opinions and ideas that are supported by two or more credible sources. Response is effectively written in standard, edited English.	Points Range: 13 (13%) – 14 (14%) Response is on topic and may have some depth. Responses posted in the discussion may lack effective professional communication. Responses to faculty questions are somewhat answered, if posed. Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.	Points Range: 0 (0%) – 12 (12%) Response may not be on topic and lacks depth. Responses posted in the discussion lack effective professional communication. Responses to faculty questions are missing. No credible sources are cited.
Second Response	Points Range: 16 (16%) – 17 (17%) Response exhibits synthesis, critical thinking, and application to practice settings. Responds fully to questions posed by faculty. Provides clear, concise opinions and ideas that are supported by at least two scholarly sources. Demonstrates synthesis and understanding of learning objectives. Communication is professional and respectful to colleagues. Responses to faculty questions are fully answered, if posed. Response is effectively written in standard, edited English.	Points Range: 14 (14%) – 15 (15%) Response exhibits critical thinking and application to practice settings. Communication is professional and respectful to colleagues. Responses to faculty questions are answered, if posed. Provides clear, concise opinions and ideas that are supported by two or more credible sources. Response is effectively written in standard, edited English.	Points Range: 12 (12%) – 13 (13%) Response is on topic and may have some depth. Responses posted in the discussion may lack effective professional communication. Responses to faculty questions are somewhat answered, if posed. Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.	Points Range: 0 (0%) – 11 (11%) Response may not be on topic and lacks depth. Responses posted in the discussion lack effective professional communication. Responses to faculty questions are missing. No credible sources are cited.
Participation	Points Range: 5 (5%) – 5 (5%) Meets requirements for participation by posting on three different days.	Points Range: 0 (0%) – 0 (0%)	Points Range: 0 (0%) – 0 (0%)	Points Range: 0 (0%) – 0 (0%) Does not meet requirements for participation by posting on 3 different days.

Total Points: 100

Micah Childress Initial Post Week 5

As technology advances, so does the ability to obtain and analyze large sets of data from numerous differing sources. The result of this voluminous information is called big data. Big data is the name given to the vast amount of datasets within the organization that are difficult to manage due to their lack of structure (McGonigle & Mastrian, 2018). The universal move from paper charting to the electronic health record (EHR) has allowed more efficient access to all aspects of current healthcare documentation along with older data via backup and storage media.

The daily use of an EHR provides a continual data set that can easily be probed and assimilated to produce information that can then be used to influence positive patient outcomes. Trends in the documentation found to be useful during the monitoring and management of patient care can be examined and used to direct change in future policies and procedures. Due to the universal language of most charting modules within a health system, the data can easily be searched and mined for a specific metric. An issue arises when a query wishes to retrieve charting details from an unstructured area, such as narrative charting entries.

As long as EHRs allow custom narrative entries, the ability to pull organized system-wide search results will be time and labor-intensive. The unformatted information must then be manually viewed, read, and sorted. Lack of integration is a prime example of how big data mining can be overwhelming and cumbersome within a clinical system (Wang et al., 2018).

One strategy used to mitigate the challenge of big data is using a checkbox flowsheet method of universal charting. The structured format of this technique provides organized, easily accessible, and easily interpreted results to the informaticist (Glassman, 2017). Although using the narrative approach can be more efficient at times by grouping together multiple assessment categories in one location, the information could be invisible and, therefore, unavailable for the requested project at hand.

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

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

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.2015.12.019

RE: Discussion – Week 5

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Great point about fraud and how it applies to healthcare. It is truly disturbing what healthcare providers up-bill to get the maximum payment possible from the patient’s insurance company. In my EBP class, I am comparing different mechanical circulatory support (MCS) devices such as intra-aortic balloon pump (IABP) and Impella. Impellas are the most popular MCS device that my hospital uses. After reading ten studies, it shows how detrimental placing patients on Impella machines, the risk for fatal complications is significantly higher. Yet, they do not improve in-hospital mortality over IABP, and our Interventional Cardiologist still chooses to place patients on Impella.

After discussing my research findings with colleagues in the cardiology realm, they stated the hospital does not reimburse for putting a patient on IABP, but they do for Impella; therefore, they pick the device with worse complications because they get additional funding. This revelation makes me sick to my stomach and makes it hard for me to be a Cardiac ICU nurse. This is not insurance fraud, but healthcare fraud, Mackey et al. (2020) states that “healthcare fraud and abuse involve all sectors of the health care industry, including drug and device manufacturers, hospitals, pharmacies, physicians, wholesalers, distributors, laboratories, and payers.” Therefore, my scenario involves healthcare fraud; placing Impella devices is scientifically proven to be associated with more complications and risks with no additional benefit over IABP. My hospital is receiving reimbursement for doing so. I was not seeking out to find healthcare fraud in my search to find out which device is superior. I have not physically seen any paperwork regarding this “reimbursement” to prove this fraudulent act concretely, but just the thought is sickening.

Big data can certainly support and help with fraud detection by using auditing services. Georgakopoulos et al. (2020) state that future work will include multiple algorithms to achieve more accurate results and expand to detect fraud on the provider level. I find this information enlightening to know that the future will better protect our patients from fraudulent acts.

References

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.ezp.waldenulibrary.org/10.1109/MECBME47393.2020.9265118

Mackey, T. K., Miyachi, K., Fung, D., Qian, S., & Short, J. (2020). Combating health care fraud and

abuse: Conceptualization and prototyping study of a blockchain antifraud framework. Journal of

Medical Internet Research, 22(9). https://doi-org.ezp.waldenulibrary.org/10.2196/18623

Micah Childress Initial Post Week 5

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

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

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.

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.

SOLANGE

RE: Discussion – Week 5

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For years, I have always shied away from numbers. From whatever reason, they initially intimidated me. Sorting through a lot of numbers, data, and statistics was part of my job that I was least looking forward to, although extremely important. The ironic thing is without thinking about it, I use data every day. For the most minute, simple things we are always gathering and processing data. IT then became more interesting to inquire about how all these things work together.

Big data refers to large data sets that produce more information when analyzed and interpreted together, than information received in smaller amounts (Thew, 2016). Using big data analytics and data mining includes tools for correlating data and predictive information (McGonigle & Mastrian, 2018). The use of big data helps healthcare organizations analyze many sorts of data throughout their networks to support evidenced based decisions (Byrd et al, 2018). Using big data allows leaders of systems to identify patterns on their units.

Being open to big data and its benefits are necessary as the healthcare field continues to expand. Every day professionals are finding new patient care implementations and interventions to make care better. A few ways big data are benefiting healthcare are increasing earlier patient medical diagnosis and effectiveness of treatment, increasing disease prevention possibilities, and improving pharmacological drug safety (Binenbaum et al, 2019). Along with this reducing medication errors, cutting down on organization cost and patient wait times, and improving patient health data tracking are always a few benefits (Bibhuranjan, 2019).

So far one of the biggest challenges big data brings is a potential breech in patient confidentiality and privacy. “To be effective, and get the full comprehensive look at a patient, big data must have access to everything, including private records and social media posts” (Young, 2016 para. #5). We all know everything has their downfalls and technology sometimes can be hit or miss. I recently had an experience with Amazon and for them to be sure I was who I said was they needed my card number, recent purchases, membership timeline, and had to call me on the number I had on file. I was impressed and the realized my hospital system does not even use two-way authentication. This gives the impression that breech in confidentiality or hacking is much easier than we think. Another disadvantage thought of is that big data has potential to send patients towards technology for medical care answers rather than the expertise care of professionals (Young, 2016). I can see this as I am often telling patients to stay away from Doctor Google.

Potential mitigation strategies for these challenges are advancing software technology to ensure fewer hacking possibilities. Along with this ensuring that patients are efficiently educated on their care rather than turning to internet sources that may be unreliable for their healthcare answers.

References

Bibhuranjan. (2019). Big data analytics – How beneficial is it for healthcare?. Technofaq. https://technofaq.org/posts/2019/05/big-data-analytics-how-beneficial-is-it-for-healthcare/#:~:text=%20Big%20Data%20Analytics-%20Benefits%20in%20the%20Healthcare,is%20very%20important%20for%20any%20organization…%20More

Binenbaum, I., Boccia, S., De Vito, C., Glocker, K., Migliara, G., Pastorino, R., Ricciardi, W. (2019). Benefits and challenges of big data in healthcare: An overview of the European initiatives. European Journal of Public Health, 29(3), 23-27. Doi:10.1093/eurpub/ckz168

Byrd, T.A., Kung, L., & Wang, Y. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3-13. doi:10. 1016/j.techfore.2015.12.019.

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

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

Young, A. (2016). The pros and cons of big data in the healthcare industry. HealthCareZone. https://www.healthtechzone.com/topics/healthcare/articles/2016/11/18/427248-pros-cons-big-data-the-healthcare-industry.htm

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RESPOND TO SOLANGE

CONNOR

RE: Discussion – Week 5

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Big data usually refers to “a large complex data set that typically yields substantially more information when analyzed as a fully integrated data set as compared to the outputs achieved with smaller sets of the same data that are not integrated” (Threw, J., 2016). My interpretation of this statement is that “Big Data” is a large amount of information that, when integrated, can make stronger change versus smaller data sets. This becomes applied to many different fields, but with healthcare, it can allow for large growth and development that can benefit delivery of care.

Big Data, with healthcare, allows for massive improvement in many different ways. One huge benefit to having big data in healthcare is assessing the functionality of a unit or hospital. For example, our weekly resource written by Jennifer Thew, states that having big data available may allow insight into how “a particular unit is performing and make well-informed decisions about what to change.” (2016). Having information that reflects large results (such as how a unit is performing in this case) can show whether a particular intervention is improving or damaging the overall performance. This is just one method to the utilization of big data in the medical field.

An issue that may arise with big data in the medical field is also proposed in the previously mentioned article. A major issue with the healthcare field now is the lack of data standardization. As the article states, “failure to recognize how data interacts has been a limitation”( Thew, 2016) in regards to moving forward and progressing.

My suggestion to the resolve of this issue is to increase the use of electronic medical records, or EHR. Nurses use EHRs to document their care and HER aids in guiding care amongst a healthcare system. By utilizing EHRs, we as nurses tell parts of a patients story. By mainstreaming EHRs, the medical field may be able to fluently tell a patient’s story across multiple health systems. As outlined in Kimberly S. Glassman, “currently, a significant amount of complexity is involved with data transfers. In the future, we’ll expect (EHRs) to be seamless and done in real time.” (2017). The article continues to discuss technologies that are being developed that can increase the collection and distribution of data, such as IV pumps, cardiac monitors, and vital sign monitors that link up directly with the computer system in use (preventing hand written, possibly wrong or forged information from being examined). These devices also provide an instant, accessible collection to be examined (and used as big data is). I believe the increase in EHRs is the answer to more efficient nursing care and more diligent big data collection. “Big data not only improves IT effectiveness and efficiency, but also supports the optimization of clinical operations’ (Bird, T. A., LeeAnn, K., &Wang, Y., 2018).

Reference:

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

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

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.

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RE: Discussion – Week 5

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A benefit of big data use that is applicable today would be untangling what populations should be vaccinated first for Covid-19. Big data, with the help of data mining, can uncover relationships between differing aspects of the data and “mechanizes the process of discovering predictive information in massive databases” (McGonigle & Mastrian, 2017). With limited amounts of vaccines and a massive amount of people, big data could help researchers uncover who may benefit the most from the first vaccinations. For example, with a population of approximately 14 million, the province of Ontario (where I am from) to date has provided 18, 603 Covid-19 vaccines (Ontario, n.d. & Statistics Canada, n.d.). The vaccines will be administered in three phases, starting with the most vulnerable (Ontario, n.d.). I imagine big data would be of big assistance in determining where to start each phase to make the most impact with limited resources.

Like others have mentioned security, privacy, and safety of the collected data is a major concern with big data. Big data depends on the use of personal and sensitive information (McGonigle & Mastrian, 2017). We as healthcare providers protect that information and ensure that it stays confidential. But what happens to that data once its online? What happens if there is breech in data? My nursing regulatory body is the Ontario Nurses Association (ONA). ONA collects a large amount of personal data from its members including social insurance numbers, credit card numbers, addresses, and places of employment (ONA, n.d.).

They were subject to “cyber-security” incident, compromising the personal information of almost 200,000 nurses in Ontario (Dubinski, 2020). The website was infiltrated and ransom demands were placed on ONA to keep the data from being released. After this incident, ONA completed a forensic investigation and remediation to contain and uncover the issue (Soloman, 2020). ONA states their data is protected by extensive firewalls and password protection (ONA, n.d.). Firewalls are intended to prevent hacking, so having been breeched, the forensic investigation is important for uncovering where and how the hackers were able to infiltrate it. Once that is uncovered, the firewall can be strengthened to prevent attacks in the future. This shows the importance of keeping data truly safe and how vulnerable data can be once online.

References

Dubinski, K. (2020, September 17). Ontario nurses’ regulatory body investigating cybersecurity incident. https://www.cbc.ca/news/canada/london/college-of-nurses-ontario-cyber-security-incident-1.5727816

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

Ontario (n.d.). COVID-19 vaccines for Ontario. Retrieved on December 30, 2020. https://covid-19.ontario.ca/covid-19-vaccines-ontario?gclid=EAIaIQobChMI8ISnrr727QIVaE1yCh36gwrvEAAYASAAEgLqmPD_BwE#about-covid-19-vaccines

Ontario Nurses Association (n.d.). Privacy policy. Retrieved on December 30, 2020. https://www.ona.org/privacy-policy/

Soloman, H. (2020, September 18). Ontario nurses’ governing body hit by ransomware attack. https://www.itworldcanada.com/article/ontario-nurses-governing-body-hit-by-ransomware-attack/435884

Statistics Canada (n.d.). Population estimates quarterly. Retrieved on December 30, 2020. https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1710000901

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RE: Discussion – Week 5

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To state technology is ever-changing is an understatement. I can still remember what it is like to have paper charting. Over my almost 9 years of being a nurse, I have used multiple EHR systems such as Meditech, Allscripts, Cerner, and EPIC. I can say that having the EHR compile all the patient data into one place for me to access has made my job as a nurse easier. Nurses enter a large portion of the EHR documentation including: “plans of care, physiological parameters, assessments, interventions, and progress evaluations” (Glassman, 2017). The documentation nurses add to the EHR is essential to quality of care and patient safety (Glassman, 2017).

The largest benefit of using big data as part of a clinical system is the use of EHR systems. The EHR takes many different types of information, which is organized automatically into a user-friendly filing cabinet (Pastorino et al., 2019). “To date, we can collect data from electronic healthcare records, social media, patient summaries, genomic and pharmaceutical data, clinical trials, telemedicine, mobile apps, sensors, and information on well-being, behaviour and socio-economic indicator” (Pastorino et al., 2019).

One of the most significant challenges of using big data as part of a clinical system is security. Just recently “the FBI, HHS and Cybersecurity and Infrastructure Security Agency in the Department of Homeland Security” released a statement in October of this year for hospitals to increase their cybersecurity measures due to an imminent threat (Jercich, 2020). In general, Cybersecurity has become a major threat in the information technology workplace (Pastorino et al., 2019). This is extremely important as it applies to healthcare due to the privacy of information (Pastorino et al., 2019).

A strategy that may effectively mitigate the challenge of using big data is for users to become hypervigilant in their part of data security. When the news of this data breach was released all employees received notifications about not giving out information about passwords, and not to click on links in emails due to possible phishing. Until cybersecurity is 100% impenetrable, we must all do our part to help secure our patient’s data.

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

Jercich, K. (2020). HHS: More than 2M patients affected by breaches reported in October. Healthcare IT News. https://www.healthcareitnews.com/news/hhs-more-2m-patients-affected-breaches-reported-october

Pastorino, R., Vito, C. D., 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, 23–27. https://doi-org.ezp.waldenulibrary.org/10.1093/eurpub/ckz168

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Shelley, I enjoyed reading your post and agreed with how we can benefit from using big data in healthcare. Having a safe and efficient transferred of confidential health care data and using a high-quality electronic health record is important to minimize medical errors. Big data can work wonders; there are advantages of having big data. Combining and effectively using big data, healthcare organizations ranging from single-physician offices to multi-provider groups to large organization networks and accountable care stand to realize significant benefits. Having a big data have huge benefits including detecting diseases at it early stages and how to treat them (Raghupathi & Raghupathi, 2014).

Potential challenges or risk of using big data as part of a clinical system is security. Data security is the number one priority for healthcare organizations especially in the wake of a rapid fire series of high profile breaches and hackings. Phishing attacks to malware to electronic devices accidentally left in vehicles, healthcare data is a risk and very vulnerable. To mitigate the challenges, there are technical safeguards for organizations storing protected health information (PHI), including transmission security, authentication protocols, and controls over access, integrity, and auditing. These safeguards translate into procedures using up to date anti-virus software, setting up firewalls, encrypting sensitive data, and using multiple factor authentication. Staff members are frequently reminded of the critical nature of data security protocols and consistently review those with access to high value data assets to prevent malicious parties form causing damage (Bresnick, n.d.).

Bresnick, J. (2017). Top 10 challenges of big data analytics in healthcare. Healthcare IT Analytics. https://healthitanalytics.com/news/top-10-challenges-of-big-data-analytics-in-healthcare

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 3(2). https://doi.org/org/10.1186/2047-2501-2-3

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I agree with you ,there are several benefits on the use of major data as part of a clinical system. Big data is usually an extensive complex data set that yields substantially more information. Big data in the use of electronic medical records is one big advantage to health care providers. They can use big data for their data management, providers can also administer disease prevention, finding/diagnosis of comorbidities, and purpose interventions for pandemics and epidemics (Fatt & Ramadas, 2018) In this recent pandemic, government can support appropriate monies to help citizens and the small business.

Another advantage of big data is an upgraded access to all patients records for the providers. With the available information regarding current testing, provider in developingcare plans much more efferently. (Dash et al., 2019) Additional merit is about securing personal medical information, it would be a challenge using at times using big data. Therefore, IT staff must protect it whenever one has to deal with the use of its databases from hackers by using protectant firewalls and antivirus software since that type of information can provide a important sum of money to cyber thieves. It is vital that all programsmust be kept up to date to provide the best protection.

(Fatt & Ramadas, 2018) My current experience with big data includes the latest outbreak of COVID-19 we have had at the unit that I work for. Moreover, keeping track of positive patients and staff, we must schedule continued testing at specific dates per the Department of Public Health recommendations. We can fairly predict worse case scenarios when results are obtained by gathering this data, helping us decide staffing, total number of equipment needed for sick patients, and medications that is required to be delivered to us from the central pharmacy.

References

Dash, S., Shakyawar, S., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis, and future prospects. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0217-0

Fatt, Q., & Ramadas, A. (2018). The usefulness and challenges of big data in healthcare. Journal of Healthcare Communications, 03(02). https://doi.org/10.4172/2472-1654.100131

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

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Big data is a term that I was not familiar with prior to this module. After reading about it I now know how common it is throughout different businesses. One benefit of using big data as a part of a clinical system is that it can incorporate several different types of data, such as telemetry monitoring and the electronic health records, and incorporate them all into one report of how the patients are responding to different treatments. Big data can provide important and useful information to healthcare teams to make decisions based off of that data (Thew, 2016). Big data can also provide healthcare systems with predictive models (Wang et al., 2018), making patient care more focused and can aid in determining if treatments are successful and measure how successful they are.

One potential challenge of using big data as part of a clinical system is that it is costly to merge all of the systems and ensure that they are all reporting in the same ways. Healthcare is extremely complex with several different inputs of data and it can make it difficult for all of those inputs to create one output. It can be difficult to create data that is streamlined, concise, and reliable when you input several forms of data (Bresnick, 2017). One strategy that I would utilize when trying to ensure that the data that is being received when using big data I would make sure that I am very clear on what it is that I am looking for and have an idea of how I will use the information when I receive it. I have seen several reports that we, as managers, are responsible for knowing but it is confusing because some of the information is based on patient days and others are based on straight numbers.

References

Bresnick, J. (2017, June 12). Top 10 Challenges of Big Data Analytics in Healthcare. Health IT Analytics. Retrieved December 30, 2020, from https://healthitanalytics.com/news/top-10-challenges-of-big-data-analytics-in-healthcare

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

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.

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RE: Discussion – Week 5

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In healthcare, big data refer to a large collection of discrete sets of statistical details that relate to a specific patient population or situation that is taken from several sources (Laureate Education, 2012). This information can then be analyzed and used to benefit the healthcare organization. In 2017, The Center of `Disease Control (CDC) funded a study that pulled clinical data septic patients from electronic health records (EHR) from over 400 hospitals. This data helped with the early recognition and treatment of sepsis (CDC.gov, 2018).

One of the benefits of big data is the potential for healthcare professionals to harness helpful predictive information from the big data and use it to identify and treat potentially deadly illnesses (CDC.gov, 2018). The hospital where I currently work utilizes a sepsis bundle that was created by using big data. They were able to identify labs such as lactic acid, WBCs, that indicate a systemic response. This sepsis bundle has proven to save lives and improve patient outcome. Big data can make the process of refining information more efficient (Young, 2016),

Prior to the development of big data, information was stored in separate data silos. Big data involves such a vast complex collection of patient specific information and that makes it a security challenge (Thew, 2016). It also puts the organization at risk for data breech and theft of personal information. A few months ago, hackers tried to get into the database at work. As a result, the hospital fortified their firewall and implemented a rigorous password process. Prior to the attack, staff members were able to stay logged in for extended periods. Now, the computer automatically signs out any user who is inactive for more than six minutes. Although staff members are furious, this is an effort to safeguard organizational data and patient information.

References

Centers for Disease Control and Prevention, (2018). Sepsis. Retrieved from https://www.cdc.gov/sepsis/pdfs/Sepsis-Surveillance-Toolkit-Aug-2018_508.pdf

Laureate Education (Executive Producer). (2012). Data, information, knowledge and wisdom continuum [Multimedia file]. Baltimore, MD: Author. Retrieved from http://mym.cdn.laureate-media.com/2dett4d/Walden/NURS/6051/03/mm/continuum/index.html

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

Young, A. (2016). The pros and cons of big data in the healthcare industry. HealthCareZone. https://www.healthtechzone.com/topics/healthcare/articles/2016/11/18/427248-pros-cons-big-data-the-healthcare-industry.htm

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RE: Discussion – Week 5

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Nursing is an ever-evolving specialty with a lot of aspects to keep track of. Constantly assessing and analyzing patient satisfaction, patient outcomes, productivity, staffing, nursing initiatives, and other aspects is a lot. Having technology to keep track of everything is very helpful and efficient. It’s a way to keep information organized which makes it easier to notice trends to highlight successes and opportunities.

The use of big data definitely makes tracking and analyzing easier. Big data are large software datasets that have the ability to capture, store, manage, and analyze, (Sensmeier, 2016). The different programs and resources that are available are endless. When trying to propose a business case, the data makes telling the story much easier. Big data is impactful to nursing by improving documentation, helps ensure adequate staffing, determine best evidence-based practices, improve patient outcomes, and can provide new opportunities, (Bradley University, n.d.).

One of the risks associated with big data is that it does not tell the full story. When others analyze the data, they just see the data and do not consider the other factors that contributed to these results. Often times executives have to advocate by person-on-person debates about the missing pieces that the data does not capture but, are still important to consider, (Thew, 2016). Collaboration is vital to a successful organization. Throughput is an initiative that my organization is focused on for this fiscal year. Some of our targets that we are monitoring are admission and discharge times. We receive the data results daily and we have meetings weekly to meet with leadership from all the inpatient units, ED, and other areas that has an impact on these numbers. We discuss what the possible barriers are that are preventing us to meet our goals and we work together to try to figure out solutions.

With all the information available it can be overwhelming for executives. Each unit has different results, and it can be a lot to keep up with in addition to other organizational information. Having a strong and reliable leadership team is important so the executives have them as a resource when putting everything together.

Utilizing big data is beneficial however, it is still important to not rely just on the results and collaborate with others to understand the big picture.

References

Bradley University. n.d. Five ways big data is changing nursing. https://onlinedegrees.bradley.edu/blog/five-ways-big-data-is-changing-nursing/

Sensmeier, J. (2016). Understanding the impact of big data on nursing knowledge. Nursing2021 Critial Care, 11(2), pp 11-13.

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

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Discussion: Big Data Risks and Rewards NURS 6051

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