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Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.

Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.

Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
Discussion: Big Data Risks and Rewards NURS 6051

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

Discussion Big Data Risks and Rewards NURS 6051
Discussion Big Data Risks and Rewards NURS 6051

entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.

As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare. In this Discussion, you will consider these risks and rewards.

To Prepare:https://nursingpaperslayers.com/discussion-big-data-risks-and-rewards-nurs-6051/

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.

Main Post: Discussion – Week 5
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IntroductionI believe one of the greatest benefits of using big data in healthcare is the ability to involve patients in their own care and care decisions. This is one of the stated goals of the HITECH Act of 2009 (Glassman, 2017). One of the challenges of this is creating a safe, HIPAA compliant yet user-friendly interface to capture information directly from patients.In my hospital organization, Epic software is utilized for the electronic health record (EHR). Our organization consists of an 844-bed level-1 trauma hospital including a dedicated children’s hospital, multiple outpatient acute care hospitals, several dedicated surgical centers, and an 89-bed inpatient psychiatric hospital that provides partial hospital, and intensive outpatient programs. Hundreds, perhaps thousands of patients receive care within these facilities daily. The resulting volume of data generated during these encounters is almost unfathomable. Capturing this data discretely, so that it can be analyzed in meaningful ways poses another major challenge.As Glassman (2017) suggests, nurses are the most important EHR documentation contributors in healthcare settings since they enter the most patient data into the EHR. I would argue that the patient’s direct EHR input should be equally as important. Involving patient in their own care and allowing them to make their own individual decisions about their healthcare is an ethical obligation, as patient preferences, culture, and values should be considered (McCormack & Elwyn, 2018). One way that big data can support this direct patient data input, and thus direct involvement in their healthcare, is by using an app-based interface that communicates directly with the EHR. My hospital organization utilizes MyChart for this.
MyChart

MyChart overcomes the challenge of creating a safe, HIPAA compliant interface that is user- friendly. MyChart is available an app designed by Epic Systems Corporation. MyChart is secure, convenient, and accessible from any mobile device or desktop computer (MyChart, 2020). With MyChart, patients can contribute data to their own EHR. The patient can receive prompts to contribute data through MyChart too. Things like consent forms, questionnaires, test results, appointment reminders, and more can be sent to the patient who can get a notification either in the app or pushed to their email address. The patient can set their own preference for this.

With MyChart the patient can perform other direct EHR data input too, including personal preferences such as primary language, prescription refill requests, appointment scheduling, and more. This involvement of the patient in their own healthcare can contribute to improved outcomes. For example, knowing that the patient’s primary language is not English can inform the healthcare team that translation services are required to ensure understanding of informed consent for a procedure.

The use of MyChart to communicate and coordinate healthcare with individual patients helps my organization to make meaningful use of the enormous amounts of data captured. Each patient can review their information and communicate directly with their provider(s) without having to make an in-person appointment. This efficiency adds to the benefits of this software interface.

Making Sense of So Much Data

One of the challenges my organization has continued to face is analyzing and reporting capabilities related to the enormous volume of data that is captured in the EHR. Just as a disruption of one body-system can make an individual sick, disruption of one system within a large, connected EHR can create ill effects in other parts of the system (Thew, 2016). This has been the case at my organization. Because the EHR interface is built primarily to capture medical data, the narrative information captured in the mental health areas is cumbersome and often ineffective. Providers must type a lot of narrative information into daily notes. Also, they must read through multiple narrative notes to find information, a process that is both problematic and inefficient. Our leadership has noted when evaluating incident reports that important information is sometimes lost from one day to the next when a provider leaves out pertinent previous days’ information from their note, which is then lost in the subsequent days’ notes. Essentially, information falls off the narrative.

Only recently has the organization identified that capturing data discretely, such as in selectable flowsheet rows, or in other selectable fields of the EHR, better enables reporting and data analysis. This is an ongoing challenge that we are actively working to address. RN daily shift note templates have been analyzed and deconstructed to create discrete data fields for which reports can be created. One example of this is group attendance.

Hourly therapeutic and educational groups are conducted in the psychiatric areas. These group types can be programmed into individual fields that are quick-click selected by the RN when the patient attends. By capturing this data discretely, administrators can run reports to analyze the data. As an example, a report of group attendance data can show which groups are most consistently attended. Then decisions can be made about which groups to discontinue, change, or reschedule. This reporting ability can also assist with productivity, as staffing can be adjusted based upon group attendance.

Conclusion

Efficiently and discretely capturing the enormous volumes of patient care data that are generated today will be pivotal in helping nurse leaders not only make sense of the data that is captured, but also to improve productivity, patient care, and 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

Grading Criteria

To access your rubric:

Week 5 Discussion Rubric

Post by Day 3 and Respond by Day 6 of Week 5

To participate in this Discussion:

Week 5 Discussion

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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.

Accessible player

Learning Objectives

Students will:

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.

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)

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.

Accessible player

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

Accessible player

Discussion Big Data Risks and Rewards NURS 6051 SAMPLE 1

RE: Discussion – Week 4
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Per Laureate Education (2012), “data is a discrete set of details related to a specific situation, patient, or population”. Laureate Education (2012) also explains data turns into information that becomes knowledge and sequentially becomes wisdom. Thew (2016) defines big data as “a large complex data set that 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”.

Organizing and making sense of big data is a daunting task; however, when done correctly, it can achieve great things. One way to utilize big data that I find most exciting is preventative medicine/applications (Vinay Shanthagiri, 2014). By using big data, medicine has the chance to be proactive instead of being reactive.

Knowledge achieved from big data promotes preventive health measures. For instance, big data trends derived from a population of people diagnosed with hypertension shows potential risk factors. If compared to those without hypertension, big data can lead to potential preventative factors. Information about potential risk and preventative factors is found by obtaining data on these patients. This information turns into knowledge, such as poor diet and exercise, which are risk factors for hypertension. Based off of that, healthcare providers have the knowledge to educate patients on wise preventative measures (like improving diet and exercise) to mitigate developing hypertension. Big data analysis is so exciting because this method can be applied to so many different illnesses, and healthcare can finally take a proactive approach rather than a reactive one.

The one thing that I find scary about big data is data security. No one wants personal information out for everyone to see. I know when I go to the doctor, I do not want anyone walking by to see or hear my weight. Imagine all of your personal data: age, date of birth, diagnosis, medications, surgeries, etc. being available for anyone and everyone.

The hospital I work for mandates dual verification. This is an added step of security. Whenever employees log in, they use their unique employee number and a complex password (that changes every month). The program also sends a PIN number to your phone or email. This PIN number must be entered before accessing any company or patient data.

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

Discussion Big Data Risks and Rewards NURS 6051 SAMPLE 2

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

Select Grid View or List View to change the rubric’s layout.
Content
Name: NURS_5051_Module03_Week05_Discussion_Rubric

Grid View
List View

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
Name: NURS_5051_Module03_Week05_Discussion_Rubric

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.

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