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Discussion Big Data Risks and Rewards NURS 6051

Discussion Big Data Risks and Rewards NURS 6051

 

RE: Discussion – Week 5

Initial Response

As technology progresses, the ability to collect data from a variety of sources increases as well. Access to such vast volumes of data can be beneficial, but only if it is structured in a logical manner, as with data mining, which identifies links between the data (McGonigle & Mastrian, 2018, p. 478). Our healthcare system, in general, relies on the collection, management, and dissemination of big data to enhance medical practices, cut costs, and forecast results, to mention a few. Hospital managers, such as chief nurse executives, rely on big data from all departments (for example, human resources, quality assurance, and unit leadership) and must devise a method for interpreting the data, establishing relationships, and “running the business” (Thew, 2016). Cerner is now the EHR system in use at the organization where I work. Vital signs, previous medical visits/procedures, and allergies are just a few examples of the huge data collected on each patient. When we discharge a patient, we verify that the patient comprehended the discharge instructions and assist them in scheduling follow-up care

. If they choose this option, we have a follow-up care coordinator who contacts the patient and assists them in scheduling visits or locating community resources as needed. In this situation, big data can be used to track patients who have chosen to get follow-up support and determine if there is a correlation between frequent readmissions and the use of follow-up assistance. (Hewner et al., 2018) examined the relationship between care transition and readmission in high-risk patients with pre-existing diseases who often visit the emergency department. The study investigates the collecting of large amounts of data and how to follow up with high-risk patients was connected with fewer emergency room visits. The usefulness of massive data collection in the clinical system is demonstrated by discharge documentation.

Discussion The Risks and Rewards of Big Data 6051 NURS Numerous concerns have been identified associated with the clinical system’s usage of big data, including security breaches. There is always a tight line between budgetary balancing and retaining safe patient files. (Wang et al., 2018) state that the majority of organizations use a “big data in the cloud” solution such as “software-as-a-service (SaaS) that provides a more cost-effective alternative.” Additionally, there are portals that allow patients to view their information within the restrictions of the “Health Insurance Portability and Accountability Act” (Glassman, 2017), which might be advantageous for patients who want to keep track of their diagnostic results. However, I can see how this could be a problem if there is a security breach, such as the patient checking in from an unsafe place, or if someone obtains access to the patient’s login credentials that was not disclosed. One of the most difficult challenges I faced this year was speaking with family members on the phone about their loved ones who were unable to enter the ER’s COVID-19 unit.

Every day, I was confronted with new dilemmas over how to proceed. In other instances, I was able to obtain verbal agreement from patients and deliver some rudimentary updates. In other instances, patients were unable to consent, and the caller lacked the patient’s identifying information. Due to patient confidentiality, I was unable to provide updates over the phone and was on the receiving end of

Discussion Big Data Risks and Rewards NURS 6051

Discussion Big Data Risks and Rewards NURS 6051

numerous frustrated family members. I understood their annoyances. However, I take patient confidentiality extremely seriously. I’m wondering if a remedy to this would be to have it written someplace that if the patient is unable to agree, the person assigned to access their patient portal may have access to that information. Similar to a will, but electronic? For instance, by displaying the option to include an emergency contact in the patient portal, the information or a link to the emergency contact’s email address where they may access the portal in an emergency? This way, the patient controls who has access to their portal, minimizing the possibility of an undesired family member or other individual gaining access who the patient does not like to grant access to. There may still be significant dangers of data breaches. And while there are mechanisms in place that allow us to communicate with next of kin, etc., many patients lacked emergency contacts and were unable to communicate with us while in the COVID-19 facility. Discussion The Risks and Rewards of Big Data 6051 NURS

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Perhaps there are better solutions, but it does inspire me to think about combining big data to keep patient information secure, but also respect the patient’s best interest in mind when handling these unique circumstances.

References

Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today. Retrieved December 28, 2020, from https://www.myamericannurse.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf

Hewner, S., Sullivan, S. S., & Yu, G. (2018). Reducing emergency room visits and in-hospitalizations by implementing best practices for transitional care using innovative technology and big-data. Worldviews on Evidence-Based Nursing, 15(3), 170–177. https://doi.org/10.1111/wvn.12286

McGonigle, D., & Mastrain, K. G. (2018). Nursing Informatics and the foundation of knowledge (4th ed.). Jones & Bartlett Learning.

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. HealthLeaders. Retrieved December 28, 2020, 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 & Social Change, 126, 3–13. https://doi.org/10.1016/j.techfore.2015.12.019

RE: Discussion – Week 5 1st response

Nataly, it was interesting reading your post. It is amazing to see how the tech world has taken over our lives, whether it’s big data or small data. All come with huge risks and benefits. As technology takes over certain tasks and frees up time for employees to focus on high-value tasks, roles and job descriptions may change.

According to the national coordinator for health information technology, some advantages of big data with electronic health records (EHR) are providing accurate, up-to-date, and complete information about patients at the point of care. EHR enables quick access to patient records for more coordinated and efficient care. EHR is the first step to transformed health care. Improved efficiencies and lower health care costs by preventative medicine and improved coordination of health care services, and reducing waste and redundant tests are some of the benefits of EHR (Health IT, n.d.).

Big data comes with lots of challenges including security breaches involving patients’ information. Elkhart Emergency physicians had 550,000 patient records breached when Central files tossed important documents. They were hired to destroy certain records and securely store patient files. Providers were notified of their documents in the dumpsite and mixed with other debris. These documents span over 8 years of collection of patients’ information (Davis, n.d.).

Technology can be great but can also consume us. Modern technology has made our lives easier, faster, better and more fun.

 

Davis, J. (n.d.). The 10 biggest healthcare data breaches of 2020, so far. Health IT security. https://healthitsecurity.com/news/the-10-biggest-healthcare-data-breaches-of-2020-so-far

Health Information Technology. (n.d.). What are the advantages of electronic health records? Health IT. https://www.healthit.gov/faq/what-are-advantages-electronic-health-records#:~:text=Electronic%20Health%20Records%20(%20EHR%20s,timeliness%2C%20efficiency%2C%20and%20equity.

RE: Discussion – Week 5

Data, data, and more data. One potential benefit of using big data as part of a clinical system is smart staffing and personnel management. Without interconnected, involved employees, patient care will decline, service will drop, and huge errors occur. Big data tools in healthcare can assist in rationalizing staffing activities in vital areas. Engaging with the appropriate analytics, time-stretched healthcare organizations can improve staffing while anticipating operation room demands and reshuffling patient care. Too often, there is a significant lack of flexibility in organizations with staff “flex” in the wrong areas at the wrong time. The disproportion of staffing could mean a department is either too overstaffed with employees or short-staffed when it matters most, leading to lower motivation for work and increasing the nonattendance rate. In this case, a resource dashboard may help because it presents employee data and is designed to assist and make the most of everyone’s time both within the human resources team and throughout the organization. It’s possible to predict when you might need staff in areas at peak times while distributing highly skilled employees to other areas within the organization during slower periods through data analytics (Datapine, 2020).

Dealing with big data can be challenging for executives within organizations, including frontline workers. A potential challenge or risk of using big data as part of the clinical system including how data is capture, cleanse of data collected, how data is stored, stewardship, querying, reporting, visualization, updating, sharing, and data security; it is the number one priority of organizations especially in the wake of breaches, hacking and ransomware. Healthcare data is subject to a nearly infinite array of vulnerabilities (Forman et al., 2018).

Big data can, at times, make nurse executives think they are statistician instead of nurses. A potential risk is using a vendor algorithm in the hiring process before actually hiring employees. This is a huge risk because vendors consider the algorithm to be proprietary and confidential. Unable to assess what is being done makes it difficult to determine potential bias, if any. For example, when a potential employee cannot use the technology, it leads to a barrier or a proper assessment; the tool may lead to discrimination claims. The law prevents an employer from obtaining information about potential employees’ medical history before hiring (Bresnick, 2017).

At my previous organization, we used Arena Scores (pre-employment assessments) to determine if candidates were more likely to stay once hire or leave within the first year. After using pre-employment assessments for about a year, we found out that employees’ personal information was vulnerable through my organization database. The vendor had access to our database as they monitor employees through their algorithm for the hiring process.

It was unfortunate, but our IT department caught the bridge, and the Chief Nursing Officer severed ties with the vendor. One strategy that may effectively reduce the risk is to secure our data, learn from mistakes, and ask simple questions, especially when using a vendor is….. who has access to it?

References

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

Durcevic, S. (2020). 18 examples of big data analytics In healthcare that can save people. The Datapine Blog News, Insights and Advice for Getting your Data in Shapehttps://www.datapine.com/blog/big-data-examples-in-healthcare/

Forman, A. S., Glasser, N. M., & Aibel, M. S. (2018). Minimize risks when using big data analytics in hiring. SHRM Better Work Places Better World. https://www.shrm.org/resourcesandtools/legal-and-compliance/employment-law/pages/big-data-analytics-in-hiring.aspx

Name:  Discussion Rubric

  Excellent

90–100

Good

80–89

Fair

70–79

Poor

0–69

Main Posting:

Response to the Discussion question is reflective with critical analysis and synthesis representative of knowledge gained from the course readings for the module and current credible sources.

40 (40%) – 44 (44%)

Thoroughly responds to the Discussion question(s).

Is reflective with critical analysis and synthesis representative of knowledge gained from the course readings for the module and current credible sources.

No less than 75% of post has exceptional depth and breadth.

Supported by at least three current credible sources.

35 (35%) – 39 (39%)

Responds to most of the Discussion question(s).

Is somewhat reflective with critical analysis and synthesis representative of knowledge gained from the course readings for the module.

50% of the post has exceptional depth and breadth.

Supported by at least three credible references.

31 (31%) – 34 (34%)

Responds to some of the Discussion question(s).

One to 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.

Cited with fewer than two credible references.

0 (0%) – 30 (30%)

Does not respond to the Discussion question(s).

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 references.

Main Posting:

Writing

6 (6%) – 6 (6%)

Written clearly and concisely.

Contains no grammatical or spelling errors.

Adheres to current APA manual writing rules and style.

5 (5%) – 5 (5%)

Written concisely.

May contain one to two grammatical or spelling errors.

Adheres to current APA manual writing rules and style.

4 (4%) – 4 (4%)

Written somewhat concisely.

May contain more than two spelling or grammatical errors.

Contains some APA formatting errors.

0 (0%) – 3 (3%)

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 Posting:

Timely and full participation

9 (9%) – 10 (10%)

Meets requirements for timely, full, and active participation.

Posts main Discussion by due date.

8 (8%) – 8 (8%)

Meets requirements for full participation.

Posts main Discussion by due date.

7 (7%) – 7 (7%)

Posts main Discussion by due date.

0 (0%) – 6 (6%)

Does not meet requirements for full participation.

Does not post main Discussion by due date.

First Response:

Post to colleague’s main post that is reflective and justified with credible sources.

9 (9%) – 9 (9%)

Response exhibits critical thinking and application to practice settings.

Responds to questions posed by faculty.

The use of scholarly sources to support ideas demonstrates synthesis and understanding of learning objectives.

8 (8%) – 8 (8%)

Response has some depth and may exhibit critical thinking or application to practice setting.

7 (7%) – 7 (7%)

Response is on topic and may have some depth.

0 (0%) – 6 (6%)

Response may not be on topic and lacks depth.

First Response:

Writing

6 (6%) – 6 (6%)

Communication is professional and respectful to colleagues.

Response to faculty questions are fully 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.

5 (5%) – 5 (5%)

Communication is mostly professional and respectful to colleagues.

Response to faculty questions are mostly answered, if posed.

Provides opinions and ideas that are supported by few credible sources.

Response is written in standard, edited English.

4 (4%) – 4 (4%)

Response posed in the Discussion may lack effective professional communication.

Response to faculty questions are somewhat answered, if posed.

Few or no credible sources are cited.

0 (0%) – 3 (3%)

Responses posted in the Discussion lack effective communication.

Response to faculty questions are missing.

No credible sources are cited.

First Response:

Timely and full participation

5 (5%) – 5 (5%)

Meets requirements for timely, full, and active participation.

Posts by due date.

4 (4%) – 4 (4%)

Meets requirements for full participation.

Posts by due date.

3 (3%) – 3 (3%)

Posts by due date.

0 (0%) – 2 (2%)

Does not meet requirements for full participation.

Does not post by due date.

Second Response:
Post to colleague’s main post that is reflective and justified with credible sources.
9 (9%) – 9 (9%)

Response exhibits critical thinking and application to practice settings.

Responds to questions posed by faculty.

The use of scholarly sources to support ideas demonstrates synthesis and understanding of learning objectives.

8 (8%) – 8 (8%)

Response has some depth and may exhibit critical thinking or application to practice setting.

7 (7%) – 7 (7%)

Response is on topic and may have some depth.

0 (0%) – 6 (6%)

Response may not be on topic and lacks depth.

Second Response:
Writing
6 (6%) – 6 (6%)

Communication is professional and respectful to colleagues.

Response to faculty questions are fully 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.

5 (5%) – 5 (5%)

Communication is mostly professional and respectful to colleagues.

Response to faculty questions are mostly answered, if posed.

Provides opinions and ideas that are supported by few credible sources.

Response is written in standard, edited English.

4 (4%) – 4 (4%)

Response posed in the Discussion may lack effective professional communication.

Response to faculty questions are somewhat answered, if posed.

Few or no credible sources are cited.

0 (0%) – 3 (3%)

Responses posted in the Discussion lack effective communication.

Response to faculty questions are missing.

No credible sources are cited.

Second Response:
Timely and full participation
5 (5%) – 5 (5%)

Meets requirements for timely, full, and active participation.

Posts by due date.

4 (4%) – 4 (4%)

Meets requirements for full participation.

Posts by due date.

3 (3%) – 3 (3%)

Posts by due date.

0 (0%) – 2 (2%)

Does not meet requirements for full participation.

Does not post by due date.

Total Points: 100

Name:  Discussion Rubric

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