Discussion: Big Data Risks and Rewards

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Discussion: Big Data Risks and Rewards

Benefits and Challenges of Big Data

Introduction

All medical treatments and interventions emanated from research results. Irrespective of controlled experiments, large volume of data is generated from day to day health facility encounters, medical investigations, and prescriptions. These generated data form part of what is used to determine interventions. According to Pastorino et al. 2019, the healthcare environment is driven by data. In clinical settings, big data has a lot of advantages. Regardless of its advantages, there are also risks involved. As efficiency in healthcare has been enhanced by big data, access to information is also a great challenge.

Benefit

The operational efficiency of healthcare systems is enhanced by use of big data which leads to positive patient outcomes. Data generated from the usage of equipment, the output from staff members, and patient outcome information are analyzed within healthcare institutions to identify gaps and opportunities for operational improvement (Adibuzzaman, DeLaurentis, Hill, & Benneyworth, 2018). Automation and utilization of resources available are evidence-based interventions. Data findings also helps to leverage possibilities that improves patient outcomes.

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Big data have also enabled the understanding of translation causal effects of clinical problems and their corresponding treatments (Pastorino et al., 2019). Electronic health records data can be useful to perform random clinical trials. These data offer large sample sizes that would provide new perspective to disease risk factors. In diabetes monitoring for example, individualized interaction using mobile health devices capable of collecting high data volumes are used with patients for real-time analysis, this enables quick responses to behavior changes for positive health results.

Challenge

The accessibility of patient data from devices to use for scientific research or reproduce has been a challenge due to the proprietary rights, share-ability, and privacy concerns involved, Thew (2016). The ownership of patient data uploaded in electronic health record systems has generated serious ongoing debate. It is presently considered that patients have rights to ownership of the data while the provider is only responsible for the data storage. In view of the above, it is difficult to access the information for the purpose of analysis unless authorized by the primary care provider. It is difficult to use the data even after it has been de-identified, there is also limitations in the ability to manipulate, use or share the data across various platforms. Data is collected in different formats by different systems, this makes the sharing process extremely complicating unless it is converted to formats which other systems can access. Patient privacy concerns guided by laws also affect accessibility of large data, providers are overly cautious of breach of patient’s privacy and this limits them from sharing patient information.

Mitigation Strategy

Through replicating the open-source technologies applied in other disciplines, the issue of data accessibility can be defeated. This will allow the protection of patient privacy and comply with regulations. Different systems can be mitigated through the use of hardware-software ecosystems that would facilitate distribution as exemplified in the Android or Apple app stores that enable access of de-identified data in compatible formats by authorized individuals (Pastorino et al., 2019).

Conclusion

The fact that the era of big data has opened opportunities in the clinical space cannot be over-emphasized, regardless that it comes with challenges for researchers. One of the enormous benefits include enhanced efficiency of healthcare while the major challenge is the accessibility of the data.

References

Adibuzzaman, M., DeLaurentis, P., Hill, J., & Benneyworth, B. D. (2018). Big data in healthcare – the promises, challenges and opportunities from a research perspective: A case study with a model database. AMIA … Annual Symposium proceedings. AMIA Symposium, 2017, 384–392.

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.

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

Question Description
Can you help me understand this Statistics question?

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.

From the moment you wake, you are in fact a data-generation machine. Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.

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:

Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.
BY DAY 3 OF WEEK 4
Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples.

BY DAY 6 OF WEEK 4
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.
SUBMISSION AND GRADING INFORMATION
Grading Criteria
To access your rubric:
Week 4 Discussion Rubric

You must proofread your paper. But do not strictly rely on your computer’s spell-checker and grammar-checker; failure to do so indicates a lack of effort on your part and you can expect your grade to suffer accordingly. Papers with numerous misspelled words and grammatical mistakes will be penalized. Read over your paper – in silence and then aloud – before handing it in and make corrections as necessary. Often it is advantageous to have a friend proofread your paper for obvious errors. Handwritten corrections are preferable to uncorrected mistakes.

Use a standard 10 to 12 point (10 to 12 characters per inch) typeface. Smaller or compressed type and papers with small margins or single-spacing are hard to read. It is better to let your essay run over the recommended number of pages than to try to compress it into fewer pages.

Likewise, large type, large margins, large indentations, triple-spacing, increased leading (space between lines), increased kerning (space between letters), and any other such attempts at “padding” to increase the length of a paper are unacceptable, wasteful of trees, and will not fool your professor.

The paper must be neatly formatted, double-spaced with a one-inch margin on the top, bottom, and sides of each page. When submitting hard copy, be sure to use white paper and print out using dark ink. If it is hard to read your essay, it will also be hard to follow your argument.

ADDITIONAL INSTRUCTIONS FOR THE CLASS

Discussion Questions (DQ)

Initial responses to the DQ should address all components of the questions asked, include a minimum of one scholarly source, and be at least 250 words.
Successful responses are substantive (i.e., add something new to the discussion, engage others in the discussion, well-developed idea) and include at least one scholarly source.
One or two sentence responses, simple statements of agreement or “good post,” and responses that are off-topic will not count as substantive. Substantive responses should be at least 150 words.
I encourage you to incorporate the readings from the week (as applicable) into your responses.
Weekly Participation

Your initial responses to the mandatory DQ do not count toward participation and are graded separately.
In addition to the DQ responses, you must post at least one reply to peers (or me) on three separate days, for a total of three replies.
Participation posts do not require a scholarly source/citation (unless you cite someone else’s work).
Part of your weekly participation includes viewing the weekly announcement and attesting to watching it in the comments. These announcements are made to ensure you understand everything that is due during the week.
APA Format and Writing Quality

Familiarize yourself with APA format and practice using it correctly. It is used for most writing assignments for your degree. Visit the Writing Center in the Student Success Center, under the Resources tab in LoudCloud for APA paper templates, citation examples, tips, etc. Points will be deducted for poor use of APA format or absence of APA format (if required).
Cite all sources of information! When in doubt, cite the source. Paraphrasing also requires a citation.
I highly recommend using the APA Publication Manual, 6th edition.
Use of Direct Quotes

I discourage overutilization of direct quotes in DQs and assignments at the Masters’ level and deduct points accordingly.
As Masters’ level students, it is important that you be able to critically analyze and interpret information from journal articles and other resources. Simply restating someone else’s words does not demonstrate an understanding of the content or critical analysis of the content.
It is best to paraphrase content and cite your source.
LopesWrite Policy

For assignments that need to be submitted to LopesWrite, please be sure you have received your report and Similarity Index (SI) percentage BEFORE you do a “final submit” to me.
Once you have received your report, please review it. This report will show you grammatical, punctuation, and spelling errors that can easily be fixed. Take the extra few minutes to review instead of getting counted off for these mistakes.
Review your similarities. Did you forget to cite something? Did you not paraphrase well enough? Is your paper made up of someone else’s thoughts more than your own?
Visit the Writing Center in the Student Success Center, under the Resources tab in LoudCloud for tips on improving your paper and SI score.
Late Policy

The university’s policy on late assignments is 10% penalty PER DAY LATE. This also applies to late DQ replies.
Please communicate with me if you anticipate having to submit an assignment late. I am happy to be flexible, with advance notice. We may be able to work out an extension based on extenuating circumstances.
If you do not communicate with me before submitting an assignment late, the GCU late policy will be in effect.
I do not accept assignments that are two or more weeks late unless we have worked out an extension.
As per policy, no assignments are accepted after the last day of class. Any assignment submitted after midnight on the last day of class will not be accepted for grading.
Communication

Communication is so very important. There are multiple ways to communicate with me:
Questions to Instructor Forum: This is a great place to ask course content or assignment questions. If you have a question, there is a good chance one of your peers does as well. This is a public forum for the class.
Individual Forum: This is a private forum to ask me questions or send me messages. This will be checked at least once every 24 hours.

Initial post – Week 4

Collapse

Big data is a big deal these days. In healthcare, it is expanding into a more detailed process. The advancements in big data analytics tools and medical imaging, as well as the increasing availability of real-time data, does help with clinical decision-making (Kent, 2018, para. 4).

Benefit

At my current workplace, we assess our patients on every shift. When we access the electronic medical record (EMR) and click under the assessment section, there are boxes to check in the areas. These areas are things like mental status, respiratory, skin, etc. This is a wonderful thing for nurses. By having these choices, it allows us not to miss any part of the assessment. As stated by Ferencsik (2016), “The EMR is a federal initiative to promote patient safety and quality of care and decrease medical error-related deaths in health care settings” (p. 1). It could be considered foolproof and ensures that the nurses have “hands-on” our patients to complete this section. It is in a head to toe fashion and gives information quickly to those looking at the chart. However, there is a way that one can bypass the questions section and write an assessment note instead of clicking on the boxes.

Challenge

This specific EMR system is not that great. Many areas of the chart contain the checkboxes. The choices offered do not always fit for the patients that we assess, yet, if we do not choose one, we cannot go on further in the assessment nor sign off on it. Nursing Administration, Directors, and Managers look down on bypassing the desired assessment area and opting for a personalized note. It is such a cookie-cutter that it, more times than not, does not fit for the patients and the drawbacks and challenges that frustrate clinicians and detract from what could be a more uniform improvement in care (Hospital Peer Review, 2019, p. 1).

Mitigate the Challenge

Here is a perfect situation to consult the Nurse Informaticist (NI). To fix this problem, nurses can go to the same screen where the questions with checkbox choices are for each patient assessment. Under each section, for example, the Integumentary system, if the patient does not fit into the conditions that are offered, a nurse may put a note directly under that section. This guarantee the nurses will complete each area, and it will remain in the correct order. If the leadership were still against free text, another option would be to consult the NI and ask that there be an expanded selection under each body region. Although having EMR has so many benefits, this is one big problem that should be addressed for excellent patient care and outcomes.

References

EMR effect on quality of care still a concern, can be addressed. (2019). Hospital Peer Review, 44(2) Retrieved from https://ezp.waldenulibrary.org/login?

qurl=https%3A%2F%2Fsearch.proquest.com%2Fdocview%2F2168832419%3Faccoun

Ferencsik, L. S. (2016). The lived experience of nurses transitioning to electronic medical records usage: A phenomenological inquiry (Order No. 10261435). Available from Nursing & Allied Health Database.

(1883607490). Retrieved from https://ezp.waldenulibrary.org/login?qurl=https%3A%2F%2Fsearch.proquest.com%2Fdocview%2F1883607490%3Faccou

Kent, J. (2018). Big data to see explosive growth, challenging healthcare organizations. Health IT Analytics. Retrieved March 16, 2020, from https://doi.org/https://healthitanalytics.com/news/big-data-to-see-

explosive-growth-challenging-healthcare-organizations

Good Afternoon Meghan

I really enjoyed reading your post and your discussion of Big Data and quality. In my view the use of Big Data and advanced data analytics will enable healthcare organizations to demonstrate value based care and receive maximum reimbursement for the delivery of quality care at lower costs. Much of this data will come from EHRs and related systems. In the NEJM Catalyst (2018) they discuss how Big Data can facilitate value-based care.

They noted, ” Despite these challenges, several new technological improvements are allowing healthcare big data to be converted to useful, actionable information. By leveraging appropriate software tools, big data is informing the movement toward value-based healthcare and is opening the door to remarkable advancements, even while reducing costs. With the wealth of information that healthcare data analytics provides, caregivers and administrators can now make better medical and financial decisions while still delivering an ever-increasing quality of patient care.

But adoption of big data analysis in healthcare has lagged behind other industries due to challenges such as privacy of health information, security, siloed data, and budget constraints. In the meantime, 80 percent of executives from financial services, insurance, media, entertainment, manufacturing, and logistics companies surveyed report their investments in big data processing as “successful,” and more than one in five declare their big data initiatives have been “transformational” for their firms.

There are at least two trends today that encourage the healthcare industry to embrace big data. The first is the aforementioned move from a pay-for-service model, which financially rewards caregivers for performing procedures, to a value-based care model, which rewards them based on the health of their patient populations. Healthcare data analytics will enable the measurement and tracking of population health, thereby enabling this switch. The second trend involves using big data analysis to deliver information that is evidence-based and will, over time, increase efficiencies and help sharpen our understanding of the best practices associated with any disease, injury or illness. Undoubtedly, adopting the use of healthcare big data can transform the industry, driving it away from a fee-for-service model toward value-based care. In short, it can deliver on the promise of lowering healthcare costs while revealing ways to deliver superior patient experiences, treatments, and outcomes”.

The Centers for Medicare and Medicaid Services (CMS) issued a Fact Sheet, “CMS Hospital Value-Based Purchasing Program Results for Fiscal Year 2019” in December of 2018 that I thought you may be interested in. It states, “The actual amount of incentive payments earned back by participating hospitals will depend on the following three values:

Each hospital’s Total Performance Score (TPS)

Each hospital’s value-based incentive payment percentage

Estimated FY 2019 amount available for value-based incentive payments

Hospitals may earn back an increase, receive a decrease, or have no change to their Medicare IPPS payments for the applicable fiscal year.

The calculations of hospital TPSs were subject to minimum case size and measure requirements. Also, hospitals must have a domain score for at least three of the four measurement domains in order to have a TPS calculated. Hospitals that do not meet the minimum domain requirements do not have their payments adjusted in the corresponding fiscal year. For every measure, each participating hospital receives an achievement score (based on how well it performed compared to other hospitals) and an improvement score (based on how much it improved over time); the higher of the two scores is awarded as the measure score.

For FY 2019, the average TPS across all participating hospitals increased to 38.1 from 37.4 in FY 2018, indicating improved quality of care and value. On average, rural hospitals performed better in the Safety, Person and Community Engagement, and Efficiency and Cost Reduction domains, while urban hospitals performed well in the Clinical Care domain. For FY 2019, the average TPS across all rural hospitals of 42.4 was greater than the national average TPS. Similarly, smaller hospitals (based on the number of inpatient beds) performed better in the Safety, Person and Community Engagement, and Efficiency and Cost Reduction domains, as well as overall TPS, while urban hospitals performed well in the Clinical Care domain.

Moving Forward

Value-based purchasing is an important step to revamping how care and services are paid for, moving increasingly toward rewarding better value, outcomes, and innovations. As we more closely link patient outcomes and costs to value-based hospital payment, the Hospital VBP Program not only aims for quality gains on paper, it also aims to promote a culture that prioritizes quality and value of care and better empowers patients and their healthcare providers through the public display of program results. Additional FY 2019 program results will be publicly reported on the next update of the Hospital Compare website” https://www.cms.gov/newsroom/fact-sheets/cms-hospital-value-based-purchasing-program-results-fiscal-year-2019

In my view, Big Data and advanced data analytics will facilitate the ability of providers and healthcare organizations to demonstrate they are providing value based care and high quality care. The use of Big Data will become more common in healthcare and will inform organizations and providers in their strategic planning as the aim to improve the value, efficiency, and quality of care. Thanks for sharing your thoughts, Dr. Reilly

NEMJ Catalyst. (2018, January). Healthcare Big Data and the promise of value-based care. NEJM Catalyst. https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0290

Rubric Detail

Select Grid View or List View to change the rubric’s layout.

Name: NURS_5051_Module03_Week04_Discussion_Rubric
Grid View
List View
Excellent Good Fair Poor
Main Posting
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.
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.
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.
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
10 (10%) – 10 (10%)
Posts main post by day 3.
0 (0%) – 0 (0%)
0 (0%) – 0 (0%)
0 (0%) – 0 (0%)
Does not post by day 3.
First Response
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.
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.
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.
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
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.
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.
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.
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
5 (5%) – 5 (5%)
Meets requirements for participation by posting on three different days.
0 (0%) – 0 (0%)
0 (0%) – 0 (0%)
0 (0%) – 0 (0%)
Does not meet requirements for participation by posting on 3 different days.
Total Points: 100
Name: NURS_5051_Module03_Week04_Discussion_Rubric

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