Challenges of Big Data Analytics in Healthcare

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RE: Discussion – Week 5

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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.

According to a report presented to the United States Congress in August 2012, big data is defined as mass quantities of supersonic speeds, difficult and complicated, and predictor data that necessitate enhanced techniques and technologies to empower the discovery, data transfer, transmission, maintenance, and interpretation of the findings (Raghupath & Raghupathi,2014).

Benefits of Big Data

Medical companies ranging from individual clinics and a number of co-partnerships to large medical complexes and accountable care corporations stand to gain tremendous benefits by digitizing, merging, and successfully utilizing big data (Raghupath & Raghupathi,2014). Identifying disorders at initial phases, when they may be addressed more readily and successfully; controlling specific groups and organizational health, and identifying financial crimes more rapidly and easily are all potential benefits. Big data analyses can be used to answer a variety of questions. Specific improvements or results, such as hospitalization; patients who might decide invasive procedures; patients who would also probably not gain from surgery; health problems; patients at increased danger for health complications; patients at increased danger for septic shock, MRSA, C. difficile, or even other healthcare disorder; disease extension, maybe anticipated and/or estimated based on immense quantities of available data. According to research, big data analytics can save the US healthcare system more than $300 billion per year, with two-thirds of it coming from cuts of about 8% in national healthcare spending (Raghupath & Raghupathi, 2014).

Risk of Big Data

Data integrity is one of the most significant difficulties connected with big data, particularly in light of the rapid rise in elevated hackings, breaches, and crypto locker incidents. Healthcare data is vulnerable to a wide range of threats, including phishing assaults, viruses, and laptops left in cabs. Patient privacy concerns, technological challenges such as compatibility, and data security uncertainty make access to patient records for scientific investigation and exchange of research analysis as digital objects for validating another complex topic. Patient concerns that are governed by relevant laws and regulations regarding privacy and confidentiality, such as the Patient Protection And Affordable Care Act of 1996 make access to healthcare data vulnerable (Pastorino et al., 2019).

Experienced Strategy

Nurses are regularly exposed to new technologies and advances. The regularity with which this new knowledge is used is often irregular, leaving nurses feeling nervous and apprehensive. Cardiac arrest is a scenario that all nurses, regardless of setting, will almost certainly face. When a person goes into cardiac arrest, they must act quickly and competently. Many nurses have expressed a lack of self-esteem in their knowledge of drugs, as well as combining and titrating medicines during a cardiac arrest (Dasher, 2015b). Nurses can run programmed care scenarios in a controlled situation and provide creative possibilities for educating and strengthening capacity for critical thought by using patient simulation initiatives.

In my five years as a nurse, I’ve seen a lot of code scenarios. Human patient simulation has previously been used to educate and fine-tune clinical abilities, improve existing knowledge, accurateness, and assess the origins and implications of health care choice (Shelestak, et al, 2015c). Lifesaving training can now be done anywhere from a simulation lab with mannequins hooked up to AED monitoring devices, monitoring systems that test the quality of cardiopulmonary resuscitation, and heart rhythms that can also be displayed on cardiac monitors to a class full of mannequins with a computer system urging out various situations and heart rhythms. The nursing staff is capable of comprehending the rationale behind change strategies if the simulation is followed by a review of the incidents and answers to the incidents. The risk of injuring an actual patient is removed, as is the obligation to execute speedily without making mistakes, with the ability to practice skills as many times as needed (Brewer, 2011). Engaging in the simulation lab has expanded my understanding, improved my evaluation skills, and improved my decision-making abilities.

References

Brewer, E. (2011). Successful techniques for using human patient simulation in nursing education. Journal of Nursing Scholarship 43(3). doi 10.1111/j.1547-5069.2011.01405.x

Dascher, K. (2015b). Teamwork during cardiopulmonary resuscitations at a rural Minnesota hospital (Doctoral Dissertation). Retrieved from http://web.b.ebscohost.com (Accession Number 109828396)

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.

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

Shelestak, D., Meyers, T., Jarzembak, J., Bradley, E. (2015c). A process to assess clinical decision-making during human patient simulation: A pilot study. Nursing Education Perspectives 36 (3). doi 10.5480/13-1107.1

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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

RE: Discussion – Week 5

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Thank you Tina. Simulation is a great was to gain knowledge and skill in a safe environment.

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5 months ago

Tae Kim

RE: Discussion – Week 5

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Tina, thank you for sharing informative post with us. Another challenge or risk of using big data, in my opinion, is that a healthcare provider may become so focused on the patient’s data that they miss the patient’s emotional needs. Not only would becoming more patient-centered improve the patient’s treatment, but it would also improve the healthcare professional’s ability to offer that care. Healthcare practitioners, according to Glassman (2017), can help establish stronger engagement with EHRs to gather critical data by focusing more on the patient. Although vital signs and other physiological measurements are crucial and may fit neatly into the patient’s flow sheet, there may be so much more that a provider may learn that does not fit into the flowsheet but still improves the patient’s care. As a result, nurses will need to collaborate with EHR developers to create a more comprehensive image of the patient. For example, it’s possible that there’s more to a patient’s health than whether or not they smoke. Why, if they do, are they doing so? Have they attempted to give up? Is there a reason why the patient won’t be able to quit? These are important details that nurses might include in their paperwork to help with patient care (p. 46).

Despite the focus on technology, the necessity for patient-centered care to be integrated into healthcare information technology is still recognized (HIT). There are attempts underway to use “user-centered design” (UCD) to make patient-facing medical technology safer, more usable, and more patient-centered. The use of rigorous and validated engineering tools to create technology that focuses on user goals is known as user-centered design (UCD). This comprises patient and clinician goals that reflect the device’s safety, effectiveness, efficiency, and user happiness. UCDs can help with the following six aspects of patient-centered care: “1) education and shared knowledge, 2) free flow of information, 3) patient engagement, 4) cooperation, 5) nonmedical components of care, and 6) respect for patient preferences” (Tippey & Weinger, 2017, p. 220).

References

Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45–47. Retrieved September 29, 2021, from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf

Tippey, K. G., & Weinger, M. B. (2017, May/June). User-centered design means better patient care. Biomedical Instrumentation & Technology, 51(3), 220-222.

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5 months ago

Dorothy Chudi-Agbaku

RE: Discussion – Week 5

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Hi Tina,

        Good post. I agree with you that hacking into data is on the increase. Security of big data at all times from the time of collection, transfer, analysis, storage, and processing is one of the biggest challenges. In addition to those who maliciously hack into systems to steal data, data including patient’s information could be lost or corrupted due to human errors or inadequate technology which would result to financial loss due to ransoms, fines and litigations. (Mallappallil et al., 2020).  To mitigate this challenge, and according to Mallappallil and his colleagues, big data should be encrypted at the point of input and decrypted at the point of output. Encrypting is the process of secretly coding plain text data (plaintext) into a meaningless word (ciphertext) for either storage or transmission while the process of converting ciphertext back to plaintext is known as decryption (Loshin, n.d.). This is done with a combination of variables known as key. The more complicated and longer the key, the harder it is for hacker to guess and the safer the data. Data can also be stored in the cloud in compartments limiting access with firewalls and with other filters in place. These would frustrate the efforts of hacker trying to steal information from big data.

Dorothy.

References

Loshin, P. (n.d.) Encryption. https://searchsecurity.techtarget.com/definition/encryption

Mallappallil, M., Sabu, J., Gruessner, A., & Salifu, M. (2020). A review of big data and medical research. SAGE open medicine, 8, 2050312120934839. https://doi.org/10.1177/2050312120934839

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5 months ago

Dorothy Chudi-Agbaku

RE: Discussion – Week 5

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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

RE: Discussion – Week 5

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Thank you Dorothy. Your post leads me to a question…

Class:

What is encryption?

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5 months ago

Dorothy Chudi-Agbaku

RE: Discussion – Week 5

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Hi Dr. Moyers,

Thank you for your question. Encryption is a valuable tool in the security of big data. Data could be stolen for malicious intent, the data could also be lost or corrupted due to human errors (Mallappallil et al., 2020). These could result to huge expenses form the organization involved for fines, ransoms or litigations. This makes the security of data through encryption very important. The process of secretly translating plain text data (plaintext) into coded and meaningless words(ciphertext) for storage or transmission is known as encryption, the process of converting ciphertext back to plaintext is known as decryption (Loshin, n.d.). To secure the data, a combination of variables to form key is used to encrypt and decrypt the data and the more complicated and longer the key the more difficult it is to guess, which makes encryption such a valuable security tool (Microsoft, 2018).

Dorothy.

References

Loshin, P. (n.d.) Encryption. https://searchsecurity.techtarget.com/definition/encryption

Mallappallil, M., Sabu, J., Gruessner, A., & Salifu, M. (2020). A review of big data and medical research. SAGE open medicine, 8, 2050312120934839. https://doi.org/10.1177/2050312120934839

Microsoft. (2018). Data Encryption and Decryption. https://docs.microsoft.com/en-us/windows/win32/seccrypto/data-encryption-and-decryption

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5 months ago

Tina Alino

RE: Discussion – Week 5

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Hello Dorothy,

I enjoyed reading your discussion post. Digital healthcare has paved the way for more convenient and effective healthcare, improving our lives much easier (She et al., 2020). To add to the importance of big data, wearable devices in healthcare are programmed electrical circuits with microchips that can be integrated into garments or worn as body accessories. They are unobtrusive, simple to operate, and include powerful capabilities such as wireless data transmission, real-time reporting, and warning devices. These monitors can provide critical information to healthcare specialists such as blood pressure, blood glucose levels, and breathing patterns, to name a few (Dwivediet al., 2019). Your explanation on how to avoid data loss was the part I loved reading the best. If data loss prevention is focused on preventing the most damaging leaks and establishing better ways for users to communicate information securely, it may be valuable, practical, and successful.

Dwivedi, A. D., Srivastava, G., Dhar, S., & Singh, R. (2019). A decentralized privacy-preserving healthcare blockchain for IoT. Sensors, 19(2), 326.

Seh, A. H., Zarour, M., Alenezi, M., Sarkar, A. K., Agrawal, A., Kumar, R., & Khan, R. A. (2020). Healthcare Data Breaches: Insights and Implications. Healthcare (Basel, Switzerland), 8(2), 133. https://doi.org/10.3390/healthcare8020133

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5 months ago

Tina Alino

RE: Discussion – Week 5

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Sorry I made a mistake on my citation, so I reposted my reply to Dorothy.

Thanks

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5 months ago

Tina Alino

RE: Discussion – Week 5

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Hello Dorothy,

I enjoyed reading your discussion post. Digital healthcare has paved the way for more convenient and effective healthcare, improving our lives much easier (Seh et al., 2020). To add to the importance of big data, wearable devices in healthcare are programmed electrical circuits with microchips that can be integrated into garments or worn as body accessories. They are unobtrusive, simple to operate, and include powerful capabilities such as wireless data transmission, real-time reporting, and warning devices. These monitors can provide critical information to healthcare specialists such as blood pressure, blood glucose levels, and breathing patterns, to name a few (Dwivediet al., 2019). Your explanation on how to avoid data loss was the part I loved reading the best. If data loss prevention is focused on preventing the most damaging leaks and establishing better ways for users to communicate information securely, it may be valuable, practical, and successful.

Dwivedi, A. D., Srivastava, G., Dhar, S., & Singh, R. (2019). A decentralized privacy-preserving healthcare blockchain for IoT. Sensors, 19(2), 326.

Seh, A. H., Zarour, M., Alenezi, M., Sarkar, A. K., Agrawal, A., Kumar, R., & Khan, R. A. (2020). Healthcare Data Breaches: Insights and Implications. Healthcare (Basel, Switzerland), 8(2), 133. https://doi.org/10.3390/healthcare8020133

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5 months ago

Adam Hundley

RE: Discussion – Week 5

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Main Post

The biggest benefit I see from using ‘big data’ is a more effective continuity of care. One person can typically pick up right where someone else left off if the data is accurate. There is so much data electronically charted on a patient during their stay in a hospital. Whether it is a nurse, doctor, or case manager, they should be able look at the previous data and be able to move forward with patient care. But that’s not always the case. This can be due to the vast amount of data even a bedside nurse can go through to find out what has taken place during a patient’s stay. Especially for a nurse executive, for whom it takes a lot of time and energy to sift through and analyze the data (Threw, 2016). It is true that this overwhelming amount of data can be nursing’s double-edged sword. There are simply so many things that can go wrong with all the data. A nurse can be charting on the wrong patient easily with the distractions of actually doing patient care at the bedside and other things. A nurse could chart the wrong number in the wrong column of a variable and completely change the doctor’s thought-process on treatment. Or, a nurse can even give the wrong medication to the wrong patient and potentially have the worst outcomes. So my suggestion would be to mitigate this over-charting is to streamline it. Have a nurse informaticist sit down with a bedside nurse, charge nurse, and others to truly look at what is being charted where and simply trim the fat. Kimberly S. Glassman who is senior vice-president of patient care services and chief nursing officer at the New York University Langone Medical Center says that a “eliminating duplication of effort will go a long way to simplifying and streamlining nursing workflow within EHRs” (Glassman, 2017). Also, as mentioned in the ‘Big data means big potential, challenges for nurse execs’ article, the units’ charting is not even uniform enough to compare apples to apples sometimes. Apparently big data analytics is a promising solution to these problems. These are programs that can analyze large volumes of diverse information quickly (Wang et al., 2018). I believe this could be a start to some sort of standard that would help to gain a clearer picture for the clinicians.

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

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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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

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Thank you Adam. Lack of standardization remains a problem.

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5 months ago

Jessica Ferrin

RE: Discussion – Week 5

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Adam,

       I enjoyed reading your post. My facility uses Meditech as our electronic health record (EHR). There is a lot of redundancy in the charting system, and as you stated, it was complicated for a leader to audit charts. Items are scattered around the system, and for example, skin integrity can be charted in multiple places, making it difficult to audit skin breakdown. Glassman (2017) states that to improve the workflow of EHRs, nurses must collaborate with the vendors of the system. In addition to your suggestion for the nurse informaticist to collaborate with bedside nurses and leadership, I suggest including representatives from the company who owns the system. Wang et al. (2018) discuss that healthcare organizations must foster a culture of sharing to implement big data analytics successfully. Imagine if a facility, led by the nursing informaticist, could improve an EHR by becoming advocates and engaging with others!

Resources

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

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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5 months ago

Paola Gaudioso

RE: Discussion – Week 5

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Adam,

           I agree that big data would be beneficial in health care for continuity of care. According to Lv & Qiao (2020), it was estimated in 2009 that by 2020 there would be 44 more times data in health care that would need to be stored. There is a lot of data that needs storage, and if centrally stored, it can be accessed by many providers. Being able to access records will help in the continuum of care. Cloud computing is a way to keep this data and can be accessed anywhere and accessed by many people at one time (Tawalbeh et al., 2016). The primary issue of storing data on a cloud is safety. According to Tawalbeh et al. (2016), integrity is also a concern because you do not want any information altered or changed. When the data is on a cloud, it is accessible by any device, even a mobile one. Accessing data is excellent for doctors if they need to access patients’ information any time of the day.

References

Lv, Z., & Qiao, L. (2020). Analysis of healthcare big data. Future Generation Computer Systems, 109, 103–110. https://doi-org.ezp.waldenulibrary.org/10.1016/j.future.2020.03.039

Tawalbeh, L. A., Mehmood, R., Benkhlifa, E., & Song, H. (2016). Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications. IEEE Access, Access, IEEE, 4, 6171–6180. https://doi-org.ezp.waldenulibrary.org/10.1109/ACCESS.2016.2613278

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5 months ago

Tanaka Ruzvidzo

RE: Discussion – Week 5 Initial Post

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Big Data

We live in a technology driven world and we take a lot of things for granted, not realizing that the modern conveniences we enjoy are a result of big data. According to Tauchman (2019) “Big data is defined as an extremely large data set that businesses must deal with on a day-to-day basis. A business may analyze its big data to track patterns and trends, and ultimately the data should be used to make stronger, more strategic business decisions. Big data is in fact called big data because it is a data set so large that standard analytical software cannot handle it” (Tauchman, 2019).

Potential benefit of using big data as part of a clinical system

One potential benefit of using big data as part of a clinical system according to Wang et al., (2018) is that “It not only improves IT effectiveness and efficiency, but also supports the optimization of clinical operations” (Wang et al., 2018). In the long run this means lower costs and expenses, thereby enhancing the business value. This is the direction that companies are moving towards and it helps their bottom line.

Potential challenge or risk of using big data as part of a clinical system

Access to data due to privacy concerns is a potential challenge of using big data as part of a clinical system. “Access to health care data is plagued by vulnerability due to patient privacy considerations which are protected by federal and local laws of protected health information such as Health Insurance Portability and Accountability Act of 1996 (HIPAA)” (Adibuzzaman et al., 2018). Even when the data has been de-identified, there is the fear of legal action and possible breach of privacy that discourages providers from sharing patient health data. Privacy and confidentiality of data are important in maintaining protection of health information. Managing electronic health information presents special challenges, and the larger the data, the more the challenge.

Strategy that may effectively mitigate the challenges or risks of using big data

One strategy that may effectively mitigate the challenges of using big data according to Tauchman (2021) is “to enhance your cybersecurity practices to cover your big data tools and initiatives. Grow your team’s knowledge on data security in particular and test your security parameters often to ensure they are protecting your information” (Tauchman, 2021). Using a variety of big data and analytics tools without putting proper cybersecurity measures in place first could make an organization vulnerable to cyberattacks. When a breach happens and several tools are used, it can be difficult to ascertain where the breach came from, or which tool has been compromised.

Conclusion

Now is the time to embrace big data. It is important for organizations to work around these problems because the fear of big data should not outweigh the benefits it can provide. The data should be leveraged to create better insights and it will enable an organization to rise above and have an advantage over its competitors.

References

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

Tauchman, E. R. (2021, August 5). 4 big data challenges and how to overcome them. Default. Retrieved September 27, 2021, from https://www.comptia.org/blog/4-big-data-challenges-and-how-to-overcome-them.

Tauchman, E. R. (2021, August 5). What is Big Data? get to know the three (or more) V’s. Default. Retrieved September 28, 2021, from https://www.comptia.org/blog/what-is-big-data-get-to-know-the-three-(or-more)-v-s.

Wang, Y., Kung, L. A., & Byrd, T. A. (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

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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

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Good insights Tanka. Thank you. Grow your teams knowledge. Good point.

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5 months ago

Tammy Rodgers

RE: Discussion – Week 5 Initial Post

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Response 2 to Tanaka Ruzvidzo

Tanaka,

During the era of the Internet of Things (IoT), or the creation of “networks of networks”, the risk of privacy invasion is paramount (Adams, 2017). Data is continuously generated on small technological devices like iPhones and FitBits, and uploaded to large data collectors such as clouds, for extraction by other software, in turn, this creates the inability for an individual to have control over personal information and who utilizes it (Adams, 2017).

A solution to aid during the paradigm of IoT and privacy security is to develop standards and protocols for IoT that ensure the use of encrypted algorithms for smaller devices that pair to larger data networks (Zeadally, et al., 2019). With most data breaches occurring in the health and medical industry (Zeadally, et al., 2019) it is imperative that consumers of handheld devices have top-notch cybersecurity and malware on their devices and that IoT is strictly governed with public privacy and confidentiality of information remaining a high priority.

Tammy

References

Adams, M. (2017). Big data and individual privacy in the age of the internet of things. Technology Innovation Management Review, 7(4), 12–24. https://doi.org/10.22215/timreview/1067

Zeadally, S., Siddiqui, F., Baig, Z., & Ibrahim, A. (2019). Smart healthcare. PSU Research Review, 4(2), 149–168. https://doi.org/10.1108/prr-08-2019-0027

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5 months ago

Mercy Ambe Mbu

RE: Discussion – Week 5 Initial Post

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Hi Tanaka,

I enjoyed reading your post. I agree with you that it is time for a turnaround; organizations should find ways to overcome the obstacles that have prevented them from fully embracing big data. According to Joshi, Vibhute, Ayachit, and Ayachit (2021), big data management requires sophisticated technology and skillful workers, and training is required. Change is not easy to come by. Like every other individual, nurses have to undergo the process change from Pre-contemplation, contemplation, preparation, action, maintenance, and possibly relapse.

Many nurses have not accepted the transition from paper charting to electronic documentation. In a survey, out of 80% of nurses who responded that they were comfortable using computers for personal use, only 5% said they were comfortable using electronic health records (Whalen et al., 2021). Training is crucial, I agree, but so many of us are still left behind despite all our training. What then is the way forward? When I look at the speed of technological advancement, I feel that our acceptance rate is no match. Nevertheless, continuous communication for behavior change through training and Nurses’ incentives remains our most valued option.

References

Joshi, S., Vibhute, G., Ayachit, A., & Ayachit, G. (2021). Big Data and Artificial Intelligence – Tools to be Future-Ready? Indian Journal of Ophthalmology, 69(7), 1652–1653. https://doi-org.ezp.waldenulibrary.org/10.4103/ijo.IJO_514_21

Whalen, K., Grella, P., Snydeman, C., Dwyer, A.-M., & Yager, P. (2021). Nursing Attitudes and Practices in Code Documentation Employing a New Electronic Health Record. Applied Clinical Informatics, 12(3), 589–596. https://pubmed-ncbi-nlm-nih-gov.ezp.waldenulibrary.org/34161987/

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5 months ago

Federica Clay

RE: Discussion – Week 5

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Potential Benefit of Big Data in Clinical Systems

        One potential benefit of using big data as part of a clinical system is being able to track progress throughout the clinical system. For example, if more and more nurses are clocking in at a later and later time to a certain unit, big data can be applied to track any improvement in the matter after teaching and possibly disciplinary action has been provided. This could help nurses become more prepared for their shift with an increased readiness in taking care of their patients. Performing quarterly or more frequent employee assessments help nurse managers to know their employees better and may be able to prevent certain situations from happening in an employee’s life or be able to provide resources to such employees. This is called using “predictive analytics” and was studied by Wang et al. (2018). It has “enabled managers to make better decisions faster and hence support preventive care” for employees as well as patients.

Potential Challenge of Big Data in Clinical System

        A potential challenge of using big data as part of a clinical system could be that there is too much data. In an article by Glassman (2017), the author defines “meaningful use of certified electronic health record technology” as it improves safety and efficiency as well as health disparities. It allows nurses to take care of themselves so they may take care of their patients. Meaningful use also provides privacy and security of health information for patients and nurse employees. Learning what data is important in what certain situations is significant to not be overloaded with unnecessary information and back up the clinical system or any progress to be made. For example, information regarding a nurse’s vaccination record not being up to date is most likely not needed to delve into the reasons that nurses are late clocking in to work. This information is valid and important, but not in this scenario when trying to find out what is making more and more nurses clocking in at later and later times. Shanthagiri (2014) gives a solution to the question of how to choose the right data. The solution provided was to use machine learning techniques to analyze data and choose which data is of importance in which area of the patient’s chart. Using the right documentation program can help organize this data.

One Strategy for Mitigating Challenge

        One strategy I have experienced for mitigating this type of challenge would be to create and provide the nurses with a questionnaire regarding their home life or any circumstances that may be causing their tardiness. The questionnaire would be given privately and would be kept confidential at all times. If needed, discussion would occur between the nurse and nurse manger regarding said circumstances. Plausible solutions and resources may be provided to help this employee. This questionnaire would create more data but would also narrow down the reasons of nurses being tardy.

Resources

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

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

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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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

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Good response Federica. Your post leads me to a question…

Class:

Can big data play a role in Meaningful Use? Can you provide an example?

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5 months ago

Marisa Buffa

RE: Discussion – Week 5

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Hi Federica,

     Utilizing predictive analytics in healthcare is one way to forecast future health outcomes for individuals or populations based on algorithms derived from historical patient data (Parikh, 2016). One example of this is seen within the health service organization Kaiser Permanente Northern California (KPNC). KPNC used maternal health data from more than 600,000 live births to determine the probability of early-onset neonatal sepsis in non-premature infants prior to birth. These infants are then categorized at birth to determine the probability of sepsis as low, medium, and high. This algorithm is translated into a score that obstetricians and neonatologist use to determine the need for antibiotics. After implementation of this algorithm, neonate antibiotic usage was down 33% to 60%, sparing 250,000 newborns from unnecessary antibiotics.

      Another example of predictive analytics in healthcare is seen with preventing 30-day hospital readmissions. Hospital facilities are able to assess risk factors based on clinical, social, and behavioral factors and implement evidenced-based interventions such as 48-hour follow up phone calls to ensure medication adherence, scheduling outpatient primary care and specialist appointments, and detailed inpatient education by a multidisciplinary team (HealthITAnalytics, 2019).  As organizations continue to incorporate predictive analytics into their facilities, patients are able to take advantage of improved processes allowing better care and health to flourish.



                                                                                                                               References

HealthITAnalytics. (2019, August 9). 10 high-value use cases for predictive analytics in healthcare. HealthITAnalytics. Retrieved September 29, 2021, from https://healthitanalytics.com/news/10-high-value-use-cases-for-predictive-analytics-in-healthcare.

Parikh, R. (2016, February 16). Predictive analytics and precision delivery of health care. JAMA. Retrieved September 29, 2021, from https://jamanetwork.com/journals/jama/article-abstract/2491644.

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5 months ago

Kene Fall

RE: Discussion – Week 5

COLLAPSE

  Big Data is an essential component of healthcare. However, it can also expose sensitive data and create challenges for healthcare professionals. Big data refers to a large data set that contains several sets of data that can be combined to produce a more complete and accurate analysis (Thew, J. 2016). It is often used to inform the development of strategies and improve the efficiency of the health care industry. Through software and analytical tools, data scientists can collect and analyze massive amounts of data to identify patterns and provide actionable information on various factors that affect the quality of care (Glassman, K. S.2017). Nurses play a critical role in the quality and safety of healthcare



  Extensive data systems can help improve healthcare by reducing costs and discovering new treatments. They can also help improve the efficiency of healthcare delivery (Adibuzzaman et al., 2018). Therefore, Nurses need to know how to interpret the data collected about their patients to make informed decisions. Additionally, Understanding and correctly analyzing Data can help healthcare professionals improve their clinical findings and enhance patient care. 



  The Health Information Technology for Economic and Clinical Health (HITECH)

The act promotes health information technology for better and more efficient care (Glassman, K. S.2017). According to the article of Glassman (2017), the goal of HITECH is to enable providers to collect and use electronic health record data (EHRs) to improve the care of patients. However, some challenges may arise utilizing EHRs. One of the significant challenges is to ensure the protection of health information. Since most medical Data is protected, its use in analytics could violate existing privacy regulations.

  The various approaches that can be used to address these issues can be acknowledged and addressed. One of these is by implementing the Health Insurance Portability and Accountability Act (HIPPA). This legislation aims to protect the privacy and security of sensitive information (O’Herrin J.K. et al., 2004). As healthcare professionals, we must protect the sensitive data that we use. 

References:

Adibuzzaman, M., Delaurentis, P., Hill, J., & Benneyworth, B. (2018, April 16). Big data in healthcare– the promises, challenges, and https://scholarworks.iupui.edu/handle/1805/17886.

Glassman, K. S. (2017, November). Using data in nursing practice. https://www.myamericannurse.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.

O’Herrin, J. K., Fost, N., & Kudsk, K. A. (2004). Health Insurance Portability Accountability Act (HIPAA) regulations: effect on medical record research. Annals of surgery, 239(6), 772–778. https://doi.org/10.1097/01.sla.0000128307.98274.dc

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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

RE: Discussion – Week 5

COLLAPSE

Good response Kene. Your post leads me to a question…

Class:

Consider your practice area. What is the relationship between big data and nurse as knowledge worker?

Dr. Moyers

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5 months ago

Federica Clay

RE: Discussion – Week 5

COLLAPSE

Kene,

        Another way to help mitigate the issue of making sure all of the patient’s information stays confidential is to use a documentation and charting system that is adapted to needing multiple identifiers before being able to access information. Shanthagiri (2014) provides a solution to use machine learning techniques to analyze data and choose which data is of importance in which area of the patient’s chart. This helps minimize exposure and provides relevant information when needed.

        Using the above-mentioned strategy can be even more useful when paired with a term called “predictive analytics.” This term was studied by Wang et al. (2018) and it has “enabled managers to make better decisions faster and hence support preventive care” for employees as well as patients. The electronic health record (HER) can be organized and made more relevant in placing big data where it needs to be in a patient’s chart.

Resources

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

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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5 months ago

jennifer girgis

RE: Discussion – Week 5

COLLAPSE

Response 2.

Hi Kene, your post was very informative. Thank you.

With the technology around, nurses can function efficiently. Nurses equipped with knowledge and skills in informatics can shape, refine, apply technology in new and different ways to build and gain wisdom. (McGonigle & Mastrian, 2018a) They use big data to make informed decisions that impact their care.

In your 5th sentence, the student believed you described data mining—a software process to sort through data to discover patterns and relationships, focusing on application. (McGonigle & Mastrian, 2018b) It is also a valuable research tool that scans unstructured data on the databases.

Health Insurance Portability and Accountability Act (HIPPA) full compliance only took effect in 2005, forcing some providers, suppliers out in the mix, making marketing opportunities harder or easier for other businesses. (Glenn, 2008) As a result, healthcare professionals and other vendors maintain the safety and privacy of patient’s information.

References

Glenn, D. (2008). New Regulations Can Create Market Opportunities. Marketing Health Services, 28 (2), 40.
McGonigle, D., & Mastrian, K. G. (2018). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.

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5 months ago

Marisa Buffa

RE: Discussion – Week 5

COLLAPSE

Potential Benefits of Using Big Data

Big data, or large, fast, and complex volumes of information whose sizes are beyond the ability of typical database software tools to capture, store, manage, and analyze information, continues to expand in complexity in healthcare (Sensmeier, 2015). As the main contributors to data, nurses use data to tell the patient’s story. One potential benefit of using big data as part of a clinical system is the ability to access patient information in real time. One example of this benefit is using the continuous cardiac monitor for chest pain patients in the Emergency Department. This allows the healthcare team to observe if a patient is experiencing a potentially fatal cardiac rhythm and provide life-saving interventions. If this were to happen, real-time electronic recording would be entered into the Electronic Health Record (EHR) and available for future use by healthcare providers. Allowing the healthcare team to securely access previously gathered data from an EHR contributes to providing high-quality care and life-saving treatments (Medanets, 2020).

Potential Challenges of Using Big Data

      One challenge to real-time data monitoring and entry involves interoperability. Lack of interoperability, or the ability of EHRs to communicate with each other, is detrimental to providing safe and effective care. If a patient has a detailed record at one hospital facility but is then brought by ambulance to a completely different hospital with a different EHR, all of the previous data would be unavailable to the new facility. Although improved with the move from paper charting to electronic, lack of interoperability has yet to be effectively resolved with electronic record keeping (Graffa, 2021).

Strategies to Mitigate Challenges of Big Data

      One strategy to mitigate the challenge of not having access to previous handwritten paper medical charts is through the use of optical character recognition (OCR). OCR is a software that recognizes handwriting and digitizes patient histories from pre-electronic record keeping (Dash et al., 2019). OCR can increase interoperability by converting handwritten critical patient history to an electronic medical record for future healthcare providers, allowing all information to exist on one secured platform. Although overcoming challenges like this would require investments in time, money, and energy, implementing an efficient healthcare system based on big data provides us with trustworthy, timely, and meaningful information (Dash et al., 2019).

References

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019, June 19). Big Data in Healthcare: Management, analysis and future prospects. Journal of Big Data. Retrieved September 29, 2021, from https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0.

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019, June 19). Big Data in Healthcare: Management, analysis and future prospects. Journal of Big Data. Retrieved September 29, 2021, from https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0.

Graffa, R. (2021, January 12). The benefits and challenges of Electronic Health Records. SE Healthcare Data Analytics and Solutions. Retrieved September 29, 2021, from https://www.sehealthcarequalityconsulting.com/2018/09/18/the-benefits-and-challenges-of-electronic-health-records/.

Medanets. (2020, April 28). Real-time data is vital to patient safety. Medanets. Retrieved September 28, 2021, from https://medanets.com/blog/real-time-data-is-vital-to-patient-safety/.

Sensmeier, J. (n.d.). Big Data and the future of Nursing Knowledge : Nursing Management. LWW. Retrieved September 28, 2021, from https://journals.lww.com/nursingmanagement/FullText/2015/04000/Big_data_and_the_future_of_nursing_knowledge.5.aspx.

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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

RE: Discussion – Week 5

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Thank you Marisa. Your post leads me to a question..

Class:

How can interoperability serve to increase safety and promote positive patient outcomes? Can you provide an example?

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5 months ago

Federica Clay

RE: Discussion – Week 5

COLLAPSE

Marisa Buffa,

The solution you provided regarding the optical character recognition sounds a lot like an idea provided by Shanthagiri (2014). His idea gives a solution to the question of how to choose the right data and a system that organizes its data appropriately. The solution provided was to use machine learning techniques to analyze data and choose which data is of importance in which area of the patient’s chart. Using the right documentation program can help organize this data and could also allow for interoperability across health systems and hospitals.

        The example you gave regarding recording events and results for a cardiac emergency leads me to the term “predictive analytics.” This term was studied by Wang et al. (2018) and it has “enabled managers to make better decisions faster and hence support preventive care” for employees as well as patients by making evidenced-based decisions and conclusions regarding patient care.

Resources

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

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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5 months ago

Alexis Liggett

RE: Discussion – Week 5 Peer Response 2

COLLAPSE

Marisa,

Great post! I had never thought about the fact that nurses use data to tell the patient’s story. Big data can tell us so much about an individual’s healthcare journey. “There is the “prequel” moment, sometimes years before the person gets to the encounter, and the “sequel” that continues long afterward” (Fierce Healthcare, 2015). As healthcare professionals, we must remember that we are just one small part of our patient’s story, not their entire story. Big data allows us to see all aspects of their story.

        As you mentioned, using optical character recognition (OCR) will help convert all handwritten patient history into electronic medical records (Dash et al., 2019). The OCR can also help with continuity of care. “Big data will help us manage the upcoming transformation into value-based care” (Thew, 2016). Having a thorough and completed electronic medical record helps improve the continuity of patient care thus, improve value-based care of our patients.

References

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big Data in Healthcare: Management, analysis and future prospects. Journal of Big Data. Retrieved October 2, 2021, from https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0.

Fierce Healthcare. (2015). Big data and storytelling: The necessary duo in healthcare. Retrieved October 2, 2021, from https://www.fiercehealthcare.com/hospitals/big-data-and-storytelling-necessary-duo-healthcare

Thew, J. (2016). Big data means big potential, challenges for nurses execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs?page=0%2C1

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5 months ago

Alexis Liggett

RE: Discussion – Week 5 Peer Response 2

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My references did not paste over correctly.

Referenes:

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big Data in Healthcare: Management, analysis and future prospects. Journal of Big Data. Retrieved October 2, 2021, from https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0.

Fierce Healthcare. (2015). Big data and storytelling: The necessary duo in healthcare. Retrieved October 2, 2021, from https://www.fiercehealthcare.com/hospitals/big-data-and-storytelling-necessary-duo-healthcare

Thew, J. (2016). Big data means big potential, challenges for nurses execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs?page=0%2C1

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5 months ago

jennifer girgis

RE: Discussion – Week 5

COLLAPSE

According to Thew (2016), as cited in Online Journal of Nursing Informatics, ” big data refers to a large complex data set that yields substantially more information when analyzed …” To put simply, it’s the enormous amount of data information across and within an organization that can be accessed any time without delay or interruption using technological devices, models and software.

The potential benefits of using big data in clinical systems include accessing nursing workflow and patient information in the electronic health record (EHRs) in a timely manner. By doing so, nurses can use essential data, document properly, review patient history records, and make the informed decision necessary to support providers and patient care. Moreover, according to Glassman (2017), it is when public and private insurance companies rely on the quality of nursing care and documentation for their reimbursement procedure (i.e., OASIS) and to be able to request more information to support the process if needed.

Using big data can be challenging to any clinical system if not managed properly. Some of these challenges include (1) much of big data is unstructured, resides in text files, and represents 75% of an organization’s data. (McGonigle & Mastrian, 2018) It can be easily overlooked, thus making it difficult to discern patterns and trends in data. Data specialists are then needed to translate the language and scan data’s importance. (2) Data security. Healthcare organization pays a large amount of money for information privacy and safety due to fines and regulations mandated by state and federal law if breached. According to Drees (2021), as cited in International Business Machines (IBM), an average cost of a healthcare data breach is $9.2 million as being top in the industry. As the demand for quality information grows, the need for its safety and privacy becomes the priority.

The student observed and researched some strategies to mitigate the risk of using big data. (1) Using data mining tools (e.g., Weka, RapidMiner, Spyder, Orange, R tool, KNIME) and the support of data mining analytics. McGonigle & Mastrian (2018, p. 477) It scan databases to identify previous and hidden patterns, providing meaningful insights and improvements. (2) The student observed that when data security is at risk in home care nursing, IT support will contact managers to inform subordinates of the malicious threats through email, meetings, or conferences. Refraining from opening any unsecured email message, links that are not supported should not be opened, and untrusted websites should be reported to the IT support specialist.

References

Drees, J. (2021, July 28). $9.2M is the average cost of a healthcare data breach, IBM says. Retrieved from https://www.beckershospitalreview.com/cybersecurity/9-2m-is-average-cost-of-a-healthcare-data-breach-ibm-says.html
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. G. (2018). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.

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

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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

RE: Discussion – Week 5

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Thank you Jennifer. Good “simple” explanation of big data!

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5 months ago

Ivo Ngosong

RE: Discussion – Week 5

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In the medical care environment, big information is an articulation utilized to depict massive amounts of information created by adopting sophisticated innovations and collaborations between partners that aid in gathering patient information and coping with emergency clinic exhibits (Ristevski and Chen,2018). Large data includes information collected from EHR, drug research, clinical devices, city hall leader records, and genetic sequencing. Other sources of big data used in the medical care framework include government organizations, computerized gadgets, for example, mobile phones, patient entryways, and publicly accessible reports (Raghupathi& Raghupathi, 2014). Using big information frameworks in centers has certain anticipated benefits on medical services since it contributes to creating modifications in the delivery of treatment and the disclosure of novel remedies. As a consequence of these improvements, medical service expenses have been reduced, re-hospitalization has been reduced, targeted contributions have been made to reduce visits to the crisis office, and work has been done on understanding security as well as persistent results (Kruse et al., 2016). Members of the medical care sector may make better clinical decisions on the type of care delivered with the use of examination insights obtained from vast amounts of data. The medical care sector may benefit greatly from massive amounts of information, but a few stumbling blocks prevent this information from being fully used in facilities. Data security is a challenge when dealing with huge amounts of data that are housed in centers. Information security is a concern since there is always the risk of unauthorized individuals gaining access to the data (Kruse et al., 2016). Another check is to see whether the patient is safe. It is a violation of patient-specialist confidentiality when huge information, such as patient medical records, is divided among medical care specialists and associations without the consent of the patient. Medical care offices that may leap at the opportunity to revamp their massive information systems will be put to the test if they don’t have enough staff members with the knowledge and preparation (Kruse et al., 2016). Cleaning, stockpiling, and information capture are additional ways to test massive amounts of information. Information gathering is a common medical care office exam due of inadequate information management practices. Cleaning is an essential component of large amounts of data, yet many medical organizations ignore this point of view, which leads to inaccurate, troublesome, and irrelevant information. An further major test is overseeing information storage, which entails substantial costs in terms of execution and security that must be addressed in the IT office (Kruse et al., 2016). The above-mentioned challenges may be addressed by implementing methods that address these problems, such as distributed computing. Due to its flexible storage arrangement, this is an important method for dealing with medical care organizations’ information problems. Distributed storage also makes it possible to share confidential information while maintaining flexible security. This problem may be solved by using reformist encoding principles and pseudo-anonymization of individual data in big information inquiry setups (Ristevski and Chen,2018). This product arrangement should provide security at the organizational level and check for each complex administrator, as well as establish ethical administration principles and practices that provide security and safety.

References

Kruse, C. S., Goswamy, R., Raval, Y., & Marawi, S. (2016). Challenges and Opportunities of Big Data in Health Care: A Systematic Review. JMIR medical informatics, 4(4), e38. https://doi.org/10.2196/medinform.5359

Ristevski, B., & Chen, M. (2018). Big Data Analytics in Medicine and Healthcare. Journal of integrative bioinformatics, 15(3), 20170030. https://doi.org/10.1515/jib-2017-0030

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

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5 months ago

Tae Kim

RE: Discussion – Week 5

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Ivo, thank you for sharing your post with us. You are right, when working with massive amounts of data, data security can be difficult. Large enterprises and organizations, not just in healthcare, are moving to the cloud as technology advances. Because cloud customers just pay for what they need when they need it, the cloud is efficient. They are relieved of the responsibility of purchasing and maintaining information technology hardware, software, and infrastructure. All of this is accessible via the cloud service provider (McConigle & Mastrian, 2018, p. 57). As national healthcare systems are faced with an increasing rate of medical care usage and the generation of huge amount of medical information, cloud computing is becoming the trend to manage this data. Through the cloud, healthcare organizations now have cost-effective and more secured means of storing and analyzing big data, filtering out meaningful information for analysis, and converting data in a format suitable for use in medical centers (Yang et al., 2017, p. 149).

Although storage of big data is becoming more prevalent, users and organizations who outsource their data to a cloud provider remain concerned about privacy. They can outsource the data to a cloud provider, but they cannot outsource their ownership and their responsibility for the data (Fox & Vaidyanathan, 2016, p. 5). One effective way of protecting data in the cloud is through encryption. There are several means of encryption and one of the more effective encryption mechanisms is called Triple Data Encryption Standard (TDES or 3 DES). Encrypted data can be securely accessed because only authorized data holders can acquire the keys to decrypt the data. If an unauthorized user attempts to access the data they are denied because they do not have the keys to decrypt the data (Shanugapriya & Kavitha, 2019, p. 265).

References

Fox, M., & Vaidyanathan, G. (2016). Impact of healthcare big data: A framework with legal and ethical insights. Issues in Information Systems, 17(3), 1 – 10. https:// iacis.org/iis/2016/3_iis_2016_1-10.pdf

McGonigle, D., & Mastrian, K. G. (2018). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, Ma: Jones & Bartlett Learning.

Shanmugapriya, E., & Kavitha, R. (2019). Efficient and secure privacy analysis for medical big data using TDES and MKSVM with access control in cloud. Journal of Medical Systems, 43(8), 1 – 12.

Yang, C., Liu, J., Chen, S., & Lu, H. (2017). Implementation of a big data accessing and processing platform for medical records in cloud. Journal of Medical Systems, 41(10), 1 -28.

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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

RE: Discussion – Week 5

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Thank you Ivo. Your post leads me to a question…

Class:

Is there any relationship between big data and Meaningful Use?

Dr. Moyers

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5 months ago

Ivo Ngosong

RE: Discussion – Week 5

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Thank you so much, Dr. Robin. I am certain that there is a link between big data and Meaningful Use. Meaningful Use is all about data collection, which entails putting in place electronic health record (EHR) systems to store and ultimately exchange protected health information (PHI). Despite the fact that the healthcare sector is still in the early phases of data collection, it must plan for what to do with this huge data. Big data systems have demonstrated the ability to make fundamental changes in care delivery and treatment discovery, such as lowering health care costs, lowering the number of hospital re-admissions, implementing targeted interventions for lowering emergency department (ED) visits, triaging patients in the ED, and preventing adverse drug reactions.

Yang, C., Liu, J., Chen, S., & Lu, H. (2017). Implementation of a big data accessing and processing platform for medical records in cloud. Journal of Medical Systems, 41(10), 1 -28.

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5 months ago

Tae Kim

RE: Discussion – Week 5

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Potential Benefit of Using Big Data as Part of a Clinical System

There are numerous advantages to utilizing big data in a clinical setting. Nurse informaticists assist nurses in using technology to reduce errors and aid decision-making while providing patient care. Documentation for electronic health records (EHRs) by care providers is critical for care integration and patient safety. When it comes to treating a patient, all data is crucial. Nurses review a patient’s data on a regular basis, making them vital communicators about the patient’s condition (Glassman, 2017, pp. 45 – 46). Data allows me to access every department, other hospitals in our network, labs, photos, procedures, and consent paperwork in my case. Before patients arrive on the unit, I occasionally use data to learn about their treatment history. The history of a patient’s care is provided by big data. Because there is so much data, I frequently use filters to find the information I require for a specific case.

Big data is transforming personal care, clinical care, public health, and related research in ways that go beyond the data used to treat individual patients. Big data technologies and analytical capabilities are being invested in by both the public and private health sectors. To promote in-depth examination of health services, governments can integrate national healthcare data sets. By creating observational evidence, institutions can expand their potential to develop new knowledge by analyzing EHRs. In clinical practice, big data is already crucial in developing disease progression models and offering individualized medication. It has also resulted in the evaluation of health policies and the improvement of clinical trial efficiency. Additional benefits of using big data include encouraging patients to participate in their own care, delivering personalized information, and integrating medicine with behavior, as well as the integration of EHRs with personal data from other sources such as medical devices and wearable devices (Vayena et al., 2018, p. 66).

Potential Challenge or Risk of Using Big Data as Part of a Clinical System

Not only is big data very valuable for healthcare organizations, it is also very valuable for cyber criminals. Stolen health information may be worth 20 to 50 times more than financial data on the black market. Stolen healthcare data can be devastating for individuals and result in billions of dollars lost for healthcare organizations. Medical identity theft occurs when an unauthorized individual uses another person’s stolen information to receive medical treatment, fill prescriptions, obtain health services, and/or submit fraudulent claims to insurance companies. The victim of this theft may be at risk for many problems including being billed for treatments they did not receive, delays in receiving care, stolen benefits, misdiagnosis or mistreatment, and even criminal charges for crimes they did not commit. The pandemic has only made cyber criminals more active. In the second half of 2020 alone, cyber criminals stole 21.3 million healthcare records. The estimated costs of data breaches in the healthcare sector in 2020 is $13 billion (“Medical Identity Theft in the New Age of Virtual Healthcare,” 2021). I personally had an incident two years ago when I received a bill for using an ambulance service which never happened. It took at least a month of hassles to clear that up.

Big data is extremely beneficial not only to healthcare organizations, but also to cyber thieves. On the underground market, stolen health information might be worth 20 to 50 times more than financial data. Stolen healthcare data may be harmful for individuals and cost healthcare companies billions of dollars. When an unauthorized person uses another person’s stolen information to acquire medical care, fill prescriptions, obtain health services, and/or make fraudulent claims to insurance companies, this is known as medical identity theft. The victim of this theft could face a variety of issues, including being charged for treatments they did not have, delays in receiving care, stolen benefits, misdiagnosis or mistreatment, and even criminal charges for crimes they did not commit. Cyber criminals have become much more active as a result of the pandemic. Cyber hackers stole 21.3 million healthcare records in the second half of 2020 alone and estimates that data breaches in the healthcare sector will cost $13 billion in 2020 (“Medical Identity Theft in the New Age of Virtual Healthcare,” 2021). I had a personal experience two years ago when I received a bill for an ambulance service that I never used. It took at least a month of wrangling to get that all out.

A Strategy to Mitigate the Challenges or Risks of Using Big Data

The following steps can be effective defenses against experienced cyber criminals:

Install network firewalls and keep networks that process Private Health Information (PHI) separate from networks that can reach the Internet.
Set policies preventing ordinary users from being able to install software.
Remove all unneeded servers from all servers and wrap remaining services to both restrict traffic and log incoming and outgoing traffic.
Write system logs to append only media that cannot be wiped by cyber criminals.
Perform regular checksums of all critical server files, store results on append only media, and cross check with the live system.
Run vulnerability scans and penetration tests on critical servers to identify vulnerabilities that can be exploited by cybercriminals (Langer, 2017, p. 122).
We have annual online computer security training as part of our obligatory education at our facility. For accessing some applications, we employ an RSA token and update passwords every 90 days. If there are security breaches, we receive emails with instructions on how to prevent repeat incidence.

Conclusion

Medical identity theft has no single remedy, and there is no feasible method to completely eradicate the issue. The risk will never go away altogether, no matter what an organization or individual does. The best one can hope for is to reduce the risk to a manageable level.

References

Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45–47. Retrieved September 27, 2021, from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf

Langer, S. G. (2017). Cyber-security issues in healthcare information technology. Journal of Digital Imaging, 30(1), 117 – 125.
Medical identity theft in the new age of virtual healthcare (2021, March 15). Retrieved September 27, 2021, from https://www.idx.us/knowledge-center/medical-identity-theft-in-the-new-age-of-virtual-healthcare

Vayena, E., Dzenowagis, J., Brownstein, J. S., & Sheikh, A. (2018). Policy implications of big data in the health sector. World Health Organization Bulletin of the World Health Organization, 96(1), 66 – 68.

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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

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Good information Tae. Big data can be a goldmine for clinical research purposes.

Dr. Moyers

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5 months ago

jennifer girgis

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Response 1.

Hi Tae Kim, your post was very informative. Thank you.

Challenges at various aspects of fighting cybercriminals include data availability, dynamics, forensics, topology modeling of malicious networks, and concealed malicious activity detection. (Yu et al., 2012) Moreover, due to society’s shallow knowledge and understanding of networks and the internet, cybercriminals are hardly ever caught. As you have mentioned, many organizations and the government spend millions trying to put them behind bars. Unfortunately, as big data increases in all levels of the industry, the more prone cybercriminals are to attack.

The United States Department of Homeland Security and Cybersecurity and Infrastructure Security Agency (CISA) collaborates with state and local governments, election officials, federal partners, and vendors; anyone can report incidents, phishing, malware, or vulnerabilities. (Cybersecurity, n.d.)

In addition to what you have mentioned on strategies to prevent risks, nurses can prevent suspicious network breaches by immediately reporting to the IT department and upline.

Data mining can also be a strategy to mitigate the challenges of using big data effectively. According to McGonigle & Mastrian (2018), it analyzes enormous databases to determine patterns, establishes new data applications, and is dependent on private health information (PHI). Clinicians must maintain the confidentiality of each patient. In addition, Healthcare organizations have Health Insurance Portability and Accountability Act (HIPPA) compliance policy that every healthcare employee must adhere to protect patient’s information and privacy.

References

Cybersecurity and Infrastructure Security Agency. (n.d.). usa.gov. Retrieved from https://www.usa.gov/federal-agencies/cybersecurity-and-infrastructure-security-agency
McGonigle, D., & Mastrian, K. G. (2018). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.

Yu, S., Zhou, W., Dou, W., & Makki, S. K. (2012). “Why is it Hard to Fight against Cyber Criminals?” 32nd International Conference on Distributed Computing Systems Workshops, 537-541. DOI: 10.1109/ICDCSW.2012.25
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5 months ago

Sophie Enjema Ndumbe

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Potential benefit of using big data

In every clinical setting we collect and use data everyday, this data comprises of many information depending on what we are looking for or what we intend to do with the data. Through the collection of data, we find solutions to problems. Big data includes information that are too broad which needs to be processed and analyzed well to get a solution to a problem. It involves working with people from different backgrounds who have knowledge in computing to analyze data. Through this we acquire more knowledge and can work in a clinical setting. According (Laureate Education, 2012) we collect clinical data, use facts to solve problems using evidence-based practice. By using big data, we analyze and find ways of solving complex problems which will impact the society positively. When people receive positive feedback from big data, it makes it easy for them to be more confident and they turn to trust the system. Reddy & Kumar (2021) states “big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications” (p.702).

Describe at least one potential challenge or risk of using big data

Big data are so complex in a way that people find it difficult to follow the system or do the right thing needed. Some big data are not secured, people turn to fraud the system, they do not use it the right way. When people turn to mess up the system then their system becomes weak everybody takes advantage of it and turn to scam the system, by collecting their information and doing things which are not necessary. When people start finding out that the system if faulty or data is not accurate, they lost confidence. For example, if it is a health care company, people or customers will not want to go there anymore, and the company will start losing customers which will start bringing their downfall. The use of large data sets makes it difficult for staff to concentrate due to excessive work load they turn to make serious errors which is detrimental for the company (Campbell et al., 2021).

Strategies to mitigate challenges of using big data

Educating the staff on how to use big data will help to reduce the problem. The company should also ensure that the workers abide to the rules and regulations, know the work ethics of the company. Xinzhi Zhang et al. (2017) “the power of big data cannot be achieved unless challenges such as secure storage, integration, harmonization, access, and sharing are addressed” (p.101). Teaching the staff on how to access information and how to keep the system secure can only be achieved if they follow the rules. This becomes very difficult for some workers, and they turn to lose their jobs. When companies hire people who are incompetent and cannot complete the tasks given to them, things start becoming worst and their turnover rate become extremely high which is detrimental for the company.

References

Campbell, A. A., Harlan, T., Campbell, M., Mulekar, M. S., & Wang, B. (2021). Nurse’s achilles heel: using big data to determine workload factors that impact near misses. Journal of Nursing Scholarship, 53(3), 333–342. https://doi-org/10.1111/jnu.12652

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

Xinzhi Zhang, Pérez-Stable, E. J., Bourne, E., Peprah, E., Duru, O. K., Breen, N., Berrigan, D., Wood, F., Jackson, J. S., Wong, D. W. S., Denny, J., Zhang, X., & Bourne, P. E. (2017). Big Data Science: Opportunities and challenges to address minority health and health disparities in the 21st Century. Ethnicity & Disease, 27(2), 95–106. https://doi-org /10.18865/ed.27.2.95

ReddyY, I. S. V., & Kumar, S. M. (2021). Dynamic Symptoms based disease detection with drug prediction using big data and machine learning. Turkish Journal of Physiotherapy Rehabilitation, 32(2), 702–707.

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April Ward

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Tiffany Turner

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Tammy Rodgers

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Mauricio De Regules

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5 months ago

ZULFIQAR ABBAS

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Hello colleagues,

Data information is very crucial for any field, it has significant importance in healthcare as well. Proper data collection and entry leading to a healthy outcome regardless of the specialty. Big data is the foundation in healthcare and provides great help in making appropriate clinical decisions to bring a positive patient outcome. The application of clinical data in healthcare is entirely dependent on the availability of correct data and analyzing the data to embrace in clinical practice. Government agencies spent a lot of revenue to obtained tremendous data before making a health policy to improve the outcome and decrease the cost (McGonigle & Mastrian, 2017). The data can be obtained from the facilities’ medical records, clinical trials, and pharmaceutical research. The current example is the COVID 19 vaccine from the pharmaceutical research data and has had approved for immediate use to deal with this pandemic.

                                                                                 Benefits of Big Data

The focus of big data is the delivery of high-quality care at a reasonable cost and eventually improve patient outcomes. Big data is collected from scholarly research and from observation and following up daily over a period of time to get the inference (Brennan & Bakken, 2015). Big data help in evaluation, diagnosis, and making a good assessment and plan for a medical problem. In clinical practice regardless of inpatient or outpatient, the Electronic Medical Record collects a substantial amount of data to be used with all the providers when needed 24/7. EMR stores a wide variety of patient information from demographic data to medically important information pertinent to the patient’s problems.

Besides the medical benefit of big data, it also helps in facilitating business policies and procedures and expenditure. Data also protect from legal implications in case of lawsuits or to fulfill the state or federal requirement. Big data must be safely stored and monitored that can retrieve it if needed. Big data helps facilities to find out the policy’s weakness and area need to be improved to run the operation smoothly and productively.

                                                                       Challenges and Risks of Using Big Data

Current nursing practice lacks the required skills and competencies for meaningful use of big data (Topaz, & Pruinelli, 2017). The biggest challenge for data is safety and security. There is a high risk of data stealing, hacking, phishing, or breach of PHI deliberately or accidentally. Because of remote and 24/7 access to EMR and related data by different providers and people there is a high risk of the data breach. Federal and State laws are in practice to maintain security, for example, federal HIPPA Act of 1996.

                                                        Strategies to deal the with challenges of Big Data 

As stated, earlier confidentiality and breach of PHI or data hacking is one of the big risks in Data usage. Federal Government had passed and required mandatory practice of the Health Insurance and Portability Act of 1996. All the facilities and healthcare team members involved in patient care, for example, physicians, nurses, the therapist must practice the HIPPA law and protect the patient data. IT department has significant responsibility to maintain the safeguard regarding the unauthorized access of the data by firewall protection, access is being monitored over the different platform, and access denial if doubt or suspicious of unauthorized access. In this regard healthcare staff need special training and protocols for the safety of data where, when, and who can access the data to keep the patient PHI safe and preventing phishing and hacking. All the healthcare team members and IT departments have the ultimate responsibility to protect and prevent the data breach or misuse.

                                                                                        Conclusion

Big data has a significant role in nursing practice. It has pros and cons, if uses properly it improve the overall outcome provides innovation in the healthcare sector. In conclusion, big data technology can be used to manage, analyze, and interpret the data and ensuring the meaningful use of that data when needed (Zhu, et al; 2019).

                                                                                          References 

Brennan, P. F., & Bakken, S. (2015). Nursing needs big data and big data needs nursing. Journal of Nursing Scholarship, 47(5), 477-484.

McGonigle, D. & Mastrian, K. (2018). Nursing informatics and the foundation of knowledge (4th ed.). Jones & Bartlett.

Topaz, M., & Pruinelli, L. (2017). Big data and nursing: implications for the future. Stud Health Technol Inform, 232, 165-171. PMID: 28106594.

Zhu, R., Han, S., Su, Y., Zhang, C., Yu, Q., & Duan, Z. (2019). The application of big data and the development of nursing science: A discussion paper. International journal of nursing sciences, 6(2), 229-234.

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Miguel Rodrigo Estrera

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Hello Zulfiqar, You have a solid point on the challenges and risks of using big data, particularly data breaches. Each year, data breaches in the healthcare industry in the United States cost the industry $6.2 billion. Approximately 90 percent of hospitals have reported a security breach in the last two years (Parwani, 2017). Implications can be extensive, ranging from a negative influence on the hospital’s finances and reputation to patient safety, the availability of information technology (IT) programs, and the compromise of patient and employee data (Parwani, 2017).

Education and awareness are essential in the fight against cybercrime and other security risks. Although not every danger can be avoided, adopting basic security hygiene procedures can help to lessen the likelihood of being attacked. Some of the interventions that can be used to combat cybercrime are as follows: Educating employees on phishing and conducting mock phishing exercises; educating employees on ransomware, denial-of-service attacks, and other cybercrime, including what to look for and how to report it; and developing an insider threat management program are some of the initiatives (Kim, 2020).

References
Kim, L. (2020, May ). Cybercrime, ransomware, and the informatics nurse. Nursing Management, 51(5), 10-12. doi:10.1097/01.NUMA.0000659448.63050.F1

Parwani, A. (2017). Healthcare industry steps up security as cyber attacks increase. Medical Laboratory Observer, 49(11), 56. Retrieved from https://web-a-ebscohost-com.ezp.waldenulibrary.org/ehost/pdfviewer/pdfviewer?vid=6&sid=c952050b-1257-4a4f-92b9-111f759c2bd0%40sdc-v-sessmgr03

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5 months ago

Adam Hundley

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5 months ago

Mauricio De Regules

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Potential challenges and risks associated with big data

The healthcare system is among the largest industries in the world, yet the most complex, with its consumers, patients continuously demanding better care management. It has made tremendous progress with specialists seeking more effective solutions and new technologies frequently emerging to help in meeting consumer needs. As such, big data coupled with the health care industry analytics have made a mark on healthcare, with a growing enthusiasm of potential usefulness in transforming personal care, clinical care, and public health. The industry faces several challenges ranging from the aging population and related disabilities to the increased use of technologies which has increased citizens’ expectations. Hence improving healthcare outcomes at the same time contain costs are the primary goal among several stakeholders. Big data offers a solution for healthcare providers to meet these goals in unprecedented ways. However, dealing with big data can be challenging for healthcare providers.

Arguably, big data has revolutionized healthcare organizations from single-physician offices and multiple-provider groups to large hospital networks and accountable care organizations that adequately intervene on high-risk high-cost patients. Conversely, the use of big data has posed both ethical and legal challenges due to the personal nature of the information it collects (Raghupathi and Raghupathi, 2014). It faces the risk of compromising privacy, personal autonomy, and the effects of public demand for transparency. Data analytics do not have regular access to data from various healthcare institutions due to imposed federal laws regulating the release of medical information.

Moreover, healthcare providers are drowning in data. As a certified nurse educator, I often encounter data analysis and synthesis, normally done manually. Data analytic tools differ from one system to another. What is applicable in hospitality cannot apply in healthcare. As such, figuring out ways of keeping up with individual agencies is challenging (Pastorino et al., 2019). I believe lack of standardization is another primary challenge that certified nurse educators face, especially when analyzing how a particular unit within an organization is performing. Also, those important variables that are critical in determining the success of patient care, such as measuring competence or commitment, are not in the data. As Thew (2016) observed, these frustrations make nurses leaders advocate for things they value most in personal debates with their subordinates as they cannot make a persuasive business decision based on the data.

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References:

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

Raghupathi, W., & Raghupathi, V. (2014). Big Data Analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1). https://doi.org/10.1186/2047-2501-2-3

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

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Adam Hundley

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Mauricio,

You have a great point! There is an absolute ton of information collected from a simple one-day stay at a hospital. I had taken some time off nursing and was surprised just how much HIPAA had taken over the unit. “To comply with the HIPAA Security Rule, all covered entities must do the following: Ensure the confidentiality, integrity, and availability of all electronic protected health information, detect and safeguard against anticipated threats to the security of the information, protect against anticipated impermissible uses or disclosures, and certify compliance by their workforce” (cdc.gov, n.d.). And HIPAA mandates all patient information be protected, but they do not offer much direction on acceptable practices for doing so (Adler, 2017). Every IVPB with a label is now disposed of carefully and cover sheets are over patient information sitting around the unit for quick access. This labeling system for medications streamlines the orders with the pharmacy, all the way to the administration via the barcode sytem. So there is no doubt it works to prevent errors, but what is the downside? Is it necessary? I am sure people way smarter than me thought through all the pros and cons before initiating the new system. But on the backside, the nurse is the one responsible for protecting the patient’s information after all those hands touched it. We do carry a lot of burdens of responsibility.

Also, I have a HIPAA joke. Knock, knock?

Who’s there?

HIPAA.

HIPPAA who?

“Can’t tell ya”. I’ll be here all night folks.

References

Adler, S. (2017, October 13). How to secure patient information (PHI). hipaajournal.com. Retrieved September 29, 2021, from https://www.hipaajournal.com/secure-patient-information-phi/

Health insurance portability and accountability act of 1996 (HIPAA). (n.d.). cdc.gov. Retrieved September 29, 2021, from https://www.cdc.gov/phlp/publications/topic/hipaa.html

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5 months ago

ZULFIQAR ABBAS

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Hi Mauricio,

Big data has brought innovation in healthcare for the past few decades. No doubt information technology made remarkable progress in the management of patients, administration, and businesses. Since healthcare became more digitalized and improved the care but unintended negative outcomes cannot be overlooked (Anderson & Agarwal, 2011). It had made a substantial improvement in the delivery of quality care, by the same token, it has many risks in maintaining the data safe and secure to be used in a meaningful manner. I agree that big data has associated with both legal and ethical issues. Federal regulation HIPPA ACT of 1996 imposed and ensures the meaningful use of personal information.

No doubt if standard practice and protocol are practiced there is less chance of occurrence of legal and ethical problems. Encrypted resources in healthcare technology, smart support systems, update related to technology, and organized clinical data sharing among the healthcare organizations and providers are useful to approach to prevent the legal and ethical issues (Gazzarata, et al; 2015).

                                                                                  References

Anderson, C. L., & Agarwal, R. (2011). The digitization of healthcare: boundary risks, emotion, and consumer willingness to disclose personal health information. Information Systems Research, 22(3), 469-490.

Gazzarata, G., Gazzarata, R., & Giacomini, M. (2015). A standardized SOA based solution to guarantee the secure access to EHR. Procedia Computer Science, 64, 1124-1129.

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5 months ago

Tammy Rodgers

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Big Data: Benefit, Challenge, and Mitigation Strategy

A significant contributor to gathering data in the healthcare system is utilizing electronic health records (EHR). For queries on a smaller scale, organizations with EHR can quickly access patient information and needed records, which can be vital when there is an emergency (Ramya, et al., 2018) and time is critical to life or death, such as in the ED. EHR can provide quick access to allergies, medication history, and any previous tests or treatments and their results.

On a larger scale, big data collection can be utilized with EHR for research purposes, monitoring the efficacy of trial drugs/vaccines and other therapeutic strategies as with COVID-19 and combating the pandemic (Dagliati, et al., 2021). Hospitals and organizations can enter medications, patient outcomes, and other vital information related to COVID-19. The data can then be extracted by larger organizations such as the World Health Organization (WHO) and the Center for Disease Control (CDC) to analyze the efficacy of treatment strategies that have been implemented.

Challenge

As with any information that is uploaded to a computer, privacy becomes an issue, especially when the information is patient health information and is shared amongst organizations. Studies report that there have already been many violations to patient health information (PHI) data by being accessed by unauthorized persons and third parties without consent. (Farhadi, et al., 2018).

Mitigating the Challenge

There are ways to mitigate the challenge of meeting HIPAA requirements when dealing with EHR and patient information. One study suggests that due to the high-volume of patient information shared in the healthcare system, each patient should be given a specific patient identifier number (Mello, et al., 2018). The indentifier could help prevent other parties from viewing data/information on patient’s with similar names or dates of birth while searching for records (Mello, et al., 2018).

Another way to tightly secure patient information is to ensure that the three security themes to HIPAA regulations are being met: administrative, physical, and technical (Kruse, et al., 2017). Administrative safeguards would entail establishing a security officer, performing security audits, and having a contingency plan (Kruse, et al., 2017). Physical safeguards include secure and private workstations and limiting access to only required files or areas that pertain to the job. (Kruse, et al., 2017). And safeguards for the technical theme include intense encryption/decryption software, strict firewalls, and routine monitoring for viruses (Kruse, et al., 2017).

Conclusion

Big data can be beneficial in hospital settings on a day-to-day basis to improve the delivery of care to patients and can also be beneficial to population health by the sharing of information and data to larger organizations to monitor the efficacy of treatments and therapeutic strategies during situations at a national or global scale. However, any data entered on a computer is at risk of unauthorized access to files and documents which could violate HIPAA laws. By ensuring that strict security measures are in place, as stated by the HIPAA security themes, and the establishment of patient identifier numbers, patient information and records can be kept more secure and safe during the continuously evolving era of the use of EHR.

References

Dagliati, A., Malovini, A., Tibollo, V., & Bellazzi, R. (2021). Health informatics and ehr to support clinical research in the covid-19 pandemic: An overview. Briefings in Bioinformatics, 22(2), 812–822. https://doi.org/10.1093/bib/bbaa418

Farhadi, M., Haddad, H. M., & Shahriar, H. (2018). Compliance of electronic health record applications with hipaa security and privacy requirements. In Censorship, surveillance, and privacy (pp. 1605–1618). IGI Global. https://doi.org/10.4018/978-1-5225-7113-1.ch079

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

Mello, M. M., Adler-Milstein, J., Ding, K. L., & Savage, L. (2018). Legal barriers to the growth of health information exchange-boulders or pebbles? The Milbank Quarterly, 96(1), 110–143. https://doi.org/10.1111/1468-0009.12313

Ramya, A., Khatheeja, S., Das, M., & Sanaboyina, A. (2018). Evaluation of benefits and barriers of electronic health records [ehr] with their solutions: A study in multispeciality hospitals. Annals of Health and Health Sciences, 5(2), 87. https://doi.org/10.5958/2322-0422.2018.00017.6

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ZULFIQAR ABBAS

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Hello Tammy,

I agree with you that Electronic Health Records (EHR) is an important tool in the healthcare industry. It made life very easy for all the healthcare providers and the people involved in the care, for example, physicians, NP, PA, Nurses, management, and insurance companies. EHR allows everyone to access the required data 24/7 to coordinate the care. The first and foremost benefit of EHR is the transfer of all healthcare data from paper to computer technology (Menachemi & Collum, 2011). It helps and improves the delivery of healthcare to patients and their families. The Health Information Technology for Economic and Clinical Health (HITECH) Act is a federally approved law to adopt and transfer healthcare into an EHR system. It is true that EHR provides quick access to all the patients’ health information anytime, anywhere, and helps avoiding a lot of errors that can be fatal.

There are some concerns regarding the EHR for example, privacy violation, and cost. At one point EHR provides a lot of benefits but it has the risk of a breach in privacy of patient’s information. Hospitals and other healthcare facilities have implemented policies to prevent privacy violations but there are still risks. According to one study, there are 215 data breaches reported affecting more than 500 individuals from 2009-2016 in large hospitals in the United States (Gabriel, et al; 2018). Hospitals should have special policies to monitor the breaches and multiple levels of authentication and checks should be in practice to prevent the data breach. HIPPA Act of 1996 provides significant help to control the violation of data breaches and secure the PHI.

                                                                                           References 

Gabriel, M. H., Noblin, A., Rutherford, A., Walden, A., & Cortelyou-Ward, K. (2018). Data breach locations, types, and associated characteristics among US hospitals. Am J Manag Care, 24(2), 78-84.

Menachemi, N., & Collum, T. H. (2011). Benefits and drawbacks of electronic health record systems. Risk management and healthcare policy, 4, 47.

.

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5 months ago

Marisa Buffa

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Hi Tammy,

Keeping patient health information (PHI) discreet is definitely a challenge healthcare providers face when utilizing big data. Big data has been shown to substantially improve healthcare by providing artificial intelligence (AI) algorithms. One example of AI use in healthcare is the ability of an algorithm to identify cancerous skin lesions from images as well as a trained dermatologist (Price & Cohen, 2019). AI can do this through previously collected sets of patient data. This raises an ethical concern: to what extent should a patient’s personal data be used in healthcare analytics without their consent or knowledge? This ethical dilemma lies within the healthcare organization collecting the data since sensitive patient information is at risk of discovery through data breaching. Ethics Review Committees (ERC) are now increasingly tasked with deciding whether big data violates the public’s values of transparency, trust, and fairness (Ienca et al., 2018). The expanding use of big data in healthcare may now require specialized review boards besides an ERC to assess and determine the validity and integrity of using PHI in future healthcare analytic projects.

References

Ienca, M., Ferretti, A., Hurst, S., Puhan, M., Lovis, C., & Vayena, E. (2018, October 11). Considerations for Ethics Review of Big Data Health Research: A scoping review. PloS one. Retrieved October 2, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6181558/.

Price, W. N., & Cohen, I. G. (2019, January). Privacy in the age of Medical Big Data. Nature medicine. Retrieved October 2, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6376961/.

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salome ugwu

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Week 5 main Post:

Potential benefit of using big data as part of a clinical system

Big data is defined as very large complex data sets which yield more data when analyzed, and involves structured, semi structured, and unstructured data (Thew, 2016). One benefit of using big data is the ability to transfer data quickly among IT systems. Data is analyzed for trends, patterns and anomalies, and hence improves the quality and accuracy of clinical decisions (Wang et al., 2018). Data such as lab values, vital signs, or medical consults notes need to be easily accessible and shareable among clinical decision makers, both within a health institution or across affiliated health systems. Health institutions should institute a culture of information sharing (Wang et al., 2018), especially when patients are transferred between multiple health care institutions. Shareability of data and information can help decrease waste in ordering similar tests, saves time in clinical interventions, and eliminates clinical task redundancy (Wang et al., 2018).

Potential challenge or risk of using big data as part of a clinical system

One challenge in using big data in healthcare is sharing clinical data especially if data has to be shared with institutions that are out of network from the sharing institution (Wang et al., 2018). Since most patient data is centralized, significant time is spent in locating information in a patient’s electronic health record (EHR), leading to time wastage, inefficiency in care, delayed decision making, and increased IT costs (Wang et al., 2018). In addition, data transfer and data sharing among health institutions is a complex process which presents risks such as patient data breach, and other possible transcription errors (Glassman, 2017).

Strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described.

Organizations storing Protected Health Information (PHI) need to take some drastic measures to secure PHI such as frequent software and firmware upgrades, using updated antivirus, using firewalls, providing transmission security, encryption, authentication protocols (Bresnick, 2017). To increase data security and decrease data breach of PHI, some institutions use a multifactor authentication process as compared to just name and password. The continued challenge for most healthcare institutions thereby continues to be the safety of their patients’ data through observing Health Insurance Portability and Accountability Act (HIPAA) compliance in technology.

References

Bresnick, J. (June 12, 2017). Top 10 Challenges of Big Data Analytics in Healthcare. Retrieved from:

https://healthitanalytics.com/news/top-10-challenges-of-big-data-analytics-in-healthcare

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

Tshew, J. (2016). 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 & Social Change, 126, 3–13. https://doi-org.ezp.waldenulibrary.org/10.1016/j.techfore.2015.12.019

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Mercy Ambe Mbu

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April Ward

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Week 5 Discussion

Initial Post:

                                                        Big Data

   So much data is created every day.  Even social media posts create data, including storing receipts, pictures from your cell phone or tracking a GPS signal.  There are more bytes of data generated than we can imagine, just in daily activities; this is called big data (McGonigle & Mastrian, 2018).

                                                  Big Data Benefits

   Where I work as a mental health nurse, every encounter with a client has to be entered into the computer in the client’s electronic health record. The date and time of the encounter, the amount of time spent, what medications were administered, and what was discussed with the client are just a few of the data recorded.  One benefit of using this big data is that it will accumulate and be counted as the amount of time spent performing my duties every day of every month, showing my productivity.  Counting the accumulation of big data every month to show productivity can be good in that my supervisors know that my time is well spent.

                                                      Big Data Challenges

   One challenge or risk of using big data as part of a clinical system is that any nurse may enter the information, but when looking closer, the data may not be fully accurate or may be lacking important information.  However, the nurse gets credit for the time spent, even though the thorough assessment had not been done or the pertinent information was left-out of the documentation.  There is no measure for nurse competence or the patient’s future cooperation with the treatment plan (Thew, 2016).

                                          Strategies to Mitigate Challenges

   Performance measures of big data should be put into place, as the first step in mitigating change, to ensure that the data entered is sufficient and also that the patient’s progress is followed.  Healthcare organizations should review the data that is entered and educate staff on proper entry of data for the most productive results.  A data governance committee may also be most prudent in measuring the integrity of big data (Wang, Kung, & Byrd, 2018).

                                                     Conclusion

   Big data is created and stored and used every day for our benefit.  Big data may also have its downfalls or challenges, as it is only useful, based on the knowledge and care of the person entering it.

References

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

Thew, K. (2016, April 19). Big data means big potential, challenges for nurse execs. 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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Alexis Liggett

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April,

        Great post! You made a very valid point with how much data is generated in a single day. Simple daily tasks that you wouldn’t think twice about generate data such as making a phone call or checking the weather. Big data is made up of extensive amounts of both semi structured and unstructured data that are cumbersome to manage. “Unstructured big data residing in text files represent more than 75% of an organization’s data” (McGonigle & Mastrian, 2017).

        Big data has many benefits such as continuity of care and, as you mentioned, showcasing staff productivity. “Big data will help us manage the upcoming transformation into value-based care” (Thew, 2016). For big data to help us improve value-based care as well as continuity of care, documentation, as you mentioned, needs to be complete and accurate. Inaccuracy and incompletion are significant challenges when it comes to big data.

References

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

Thew, J. (2016). Big data means big potential, challenges for nurses execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs?page=0%2C1

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5 months ago

Paola Gaudioso

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Big Data

In a technology-driven world, we use data and store data daily. According to Thew (2016), big data refers to a vast, complicated data set that, when studied as a fully integrated data set, gives additional information than smaller sets of the same data that are not integrated. Healthcare has tons of data that it needs to store and use regularly.

A benefit of Big Data

There are benefits to big data in healthcare since there is an increase in data storage. Since healthcare is using technology nowadays, they must find a way to store all the data safely. According to Wang et al. (2018), big data can help analyses text-based health documents and other data that is not structured. Big data can analyze written records and a number to find trends and the needed data.

A Negative of Big Data

Big data would not work in healthcare because healthcare is behind when it comes to IT. Big data uses clouds to save patient information. As we all know, clouds can be hacked. All personal technology uses clouds nowadays to store our data. There are many ways to store data safely, but people learn how to hack any program, as we all know. The hospital I work for recently had a breach, and we did not have a computer system for months while IT rebuilt it. According to Sudheep & Joseph (2019), there are encrypted documents and key fab access to all providers or workers accessing data in clouds which helps keep data safe.

Something to Implement

Big data is helpful in healthcare because it can help with all the large amounts of data with patient information. Using multi-model stacking can help find missing data which is a way that big data can be used in healthcare. There are many times that missing data is needed to figure things out for a patient. “It may be possible to predict the missing data using a pre-learned autoencoder (Kim & Chung, 2020).” Having people working with the healthcare workers and IT be able to work with the data and gather the data will help in multi-model stacking. Coders can make programs that detect what data is missing for a patient by parameters.

Conclusion

Being able to store all data in one area would be beneficial for patients and healthcare workers. Having a data cloud where pharmacies, physicians, and all healthcare workers had access to data would benefit the continuum of care. Big data could be the way to go in healthcare, but like all things, there are challenges. From the studies read, few companies are using big data in healthcare which gave many limited findings.

References

Kim, J., & Chung, K. (2020). Multi-Modal Stacked Denoising Autoencoder for Handling Missing Data in Healthcare Big Data. IEEE Access, Access, IEEE, 8, 104933–104943. https://doi-org.ezp.waldenulibrary.org/10.1109/ACCESS.2020.2997255

Sudheep, K., & Joseph, S. (2019). Review on Securing Medical Big Data in Healthcare Cloud. 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Advanced Computing & Communication Systems (ICACCS), 2019 5th International Conference On, 212–215. https://doi-org.ezp.waldenulibrary.org/10.1109/ICACCS.2019.8728351

Thew, J. (2016, April 19). Big data means immense potential: challenges for nurse execs. Retrieved from http://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nursing-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.ezp.waldenulibrary.org/10.1016/j.techfore.2015.12.019

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Alexis Liggett

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5 months ago

Robin Moyers WALDEN INSTRUCTOR MANAGER

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Mauricio De Regules

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5 months ago

Jessica Ferrin

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5 months ago

Chaquita Nichols

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What is Big Data

“Data is raw facts that lack meaning” (McGonigle & Mastrian, 2017). According to the Laureate video, Informatics takes technology and converts raw data into information, then into knowledge, then into wisdom. So, with big data, it just means there is more or an increasing amount of data. We are always learning something new and then over the years, it turns into wisdom.

Benefits of using Big Data

We use technology on a daily basis, especially at work. We are always charting patient information into a system that anyone who deals with patient care can view. We carry around phones, so we are able to be contacted. We have our computer on wheels so we can have access to patient information without having to run to a desktop computer, we can scan our medications and have ways to look up medication education if the patient needs it. Some of the big data is preprogrammed into the systems and let us know when an error is made. It also has questions set up to guide the healthcare provider in the right direction to provide the necessary care. Over the years as the nurses working in informatics learn new information and adds it to the system. They can do that by looking at different trends.

Potential Risk of using Big Data

Some risk of using big data is that if the wrong information is entered it can easily cause misdiagnosis or the wrong treatment. One bad habit that some nurses have is repetition, which means they are using to putting in certain information that they don’t really read the information, and they could miss new information that could have been added. Another risk is someone hacking into the system, but I’m sure the IT department try to find ways to prevent that. There is also misuse of information, such as looking at data on someone you are not supposed to.

Strategy to mitigate challenges of using Big Data

One way to mitigate the challenges of hacking, is to know the signs or what to look out for. I’m sure the IT team is on it when it comes to properly secure a system. Another solution to mitigate challenges is for healthcare providers not to get too comfortable in their charting and really pay attention to the information they are charting. You have to make sure it is the right patient and information because even the smallest typo can make a big difference. Also, for healthcare providers to be mindful of patients’ privacy. If you are not caring for the patient or providing a service for them, there should be no need for you to view their information.

Conclusion

“Nursing informatics is used in practice settings to organize and apply data, information, knowledge, and wisdom. Data is a discrete set of details related to a specific situation, patient, or population” (Laureate Education, 2018). So, as we continue to use big data with have to continue to gain the knowledge needed so that knowledge can turn into wisdom. Big data is only going to get bigger as they come up with new ways to communicate with patients and provide safe and easier care.

References

Laureate Education (Producer). (2018). Data-Information-Knowledge-Wisdom [Video file]. Baltimore, MD: Author.

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.

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Robin Moyers WALDEN INSTRUCTOR MANAGER

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Thank you Chaquita.

Class:

Can anyone provide an example of how big data can contribute to DIKW?

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Chaquita Nichols

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According to the Laureate video, Informatics takes technology to convert raw data into information, then into knowledge, then into wisdom. An example would be with the different questionnaires that address different issues. When we ask certain questions, depending on the patient’s answer there would be more ongoing questions, and with the right questions, a diagnosis can be made. Sometimes nurses get used to using certain data, they know what to expect just over the previous years and experience. For example, with sepsis protocols, when we enter the data, it tells who is becoming at risk for sepsis. Then over the years, the nurse recognizes certain signs of sepsis without having to enter the information. She has gained knowledge about sepsis because of the questionnaire that was created and now she can provide the quality care needed for the patient. She can anticipate what she has to do for the patient to try to prevent them from becoming septic. “Informatics competency helps nurses use information and technology to communicate, manage knowledge, mitigate error, and support decision-making at the point of care” (Glassman, K. S., 2017).

Laureate Education (Producer). (2018). Data-Information-Knowledge-Wisdom [Video file]. Baltimore, MD: Author.

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

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

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5 months ago

Mercy Ambe Mbu

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Nurses and other clinicians produce and use voluminous, varied, complex, veracious, valuable, and high-speed data daily. It is high-speed data arriving from different sources. Traditional relational databases cannot capture, manage, and process the complexity of these data sets (Joshi, Vibhute, Ayachit & Ayachit, 2021), especially healthcare data that need a high level of specialty due to the increased complexity. This complexity has given rise to a data phenomenon that has contributed to the development of new data analytics and sophisticated technologies, such as artificial intelligence, to combine, process, and analyze these data to make predictions and support optimal decision-making. According to McGonigle and Mastrian (2017), data mining uses software to sort through data to discover patterns and establish relationships. (Ho, Ali & Caals, 2020). Big data in itself has no value that can be made but is meaningful through data mining.

Health care is one of the areas in which tremendous changes are happening due to extensive data mining. Information obtained from data analysis improves efficiency and performance, decreases healthcare costs, increases profit, and initiates innovations. Big data supports and informs evidence-based practice (Ehrenstein et al., 2017). Before data mining, decision-making was not well informed, and even if it was, the quality of the data source was questionable. According to Laureate education (2018), technologies are not just meant for data collection but helps to turn information into actionable knowledge. Now, clinicians, researchers, and stakeholders make informed decisions about what areas of healthcare need improvement. For example, Data mining allows easy identification of the most common health conditions needing improvement; It informs research on what areas need studies and reveals which interventions are effective. Information from patient surveys can now be used because of the possibility of gathering and analyzing large and complex volumes of patient data. As a result, patients can make contributions to healthcare advancement.

Despite all the positive impacts of big data, just like every innovation comes with its challenges. The complexity of the processes involved in extensive data mining requires advanced technology and expertise. Limited awareness of analytics capabilities among health managers and health care professionals produces Inaccuracy and inconsistencies. (Borges do Nascimento et al., 2021). Many nurses are frustrated by their inability to manipulate new technology, which is fast becoming more complex. The lack of knowledge can increase the chances of human error due to poor quality data input. To resolve this issue, informaticists must collaborate with leaders within and outside the facilities to minimize manual data input. For example, when patients transition from hospitals to short-term rehabilitation, the patient’s information has to be input into a new system. Forgetting to add essential medications such as blood thinners can be life-threatening. Whereas, if the data were transferred from one electronic health record to another, these omissions would be eliminated or minimized. Nonetheless, all staff interfering with data need adequate training, refreshers, and encouragement to be effective in data management at individual levels and then as a collection.

References

Borges do Nascimento, I. J., Marcolino, M. S., Abdulazeem, H. M., Weerasekara, I., Azzopardi-Muscat, N., Gonçalves, M. A., & Novillo-Ortiz, D. (2021). Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies. Journal of Medical Internet Research, 23(4), e27275. https://doi-org.ezp.waldenulibrary.org/10.2196/27275

Ehrenstein, V., Nielsen, H., Pedersen, A. B., Johnsen, S., & Pedersen, L. (2017). Clinical Epidemiology in the Era of Big Data: New Opportunities, Familiar Challenges. Clinical Epidemiology, 9, 245-250. http://dx.doi.org.ezp.waldenulibrary.org/10.2147/CLEP.S129779

Ho, C. W. L., Ali, J., & Caals, K. (2020). Ensuring Trustworthy use of Artificial Intelligence and Big Data Analytics in Health Insurance. Bulletin of the World Health Organization, 98(4), 263–269. https://doi-org.ezp.waldenulibrary.org/10.2471/BLT.19.234732

Joshi, S., Vibhute, G., Ayachit, A., & Ayachit, G. (2021). Big Data and Artificial Intelligence – Tools to be Future-Ready? Indian Journal of Ophthalmology, 69(7), 1652–1653. https://doi-org.ezp.waldenulibrary.org/10.4103/ijo.IJO_514_21

Laureate Education (Producer). (2018). Data-Information-Knowledge-Wisdom [Video file]. Author.

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

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Kirsi Hoselton

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Mercy Ambe Mbu

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Annisha Mcgowan

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Applying big data and intelligence to the clinical nursing quality management system, constructing a nursing management and control platform that integrates nursing quality indicators, nursing event reporting, and nursing risk management, and realizes the dynamic and intelligent management and control of nursing quality throughout the process (Yang, Zan, Wang & Li, 2020).

Benefits of using big data

The benefit of having big data in the clinical setting is that it improves patient outcomes. Nursing is at the frontline, and they are more personable with patients. The data that nurses collect is based on experience dealing with the patient population. Within my facility we depend on data to improve workflow, introduce new practices, and to improve patient outcomes. Working at a surgery center new equipment are introduce often. There are representatives giving Inservice on new standard equipment often. Increasing better patient outcomes, eliminating patient injury, and creating a seamless workflow for nurses is always a benefit.

Risk of using big data

Although these cutting-edge scientists are advancing our engagement in Big Data–data science and empowering nursing’s contribution to the advancements in human health, many challenges exist. Some of these include accuracy of data, missing data, availability of standardized data, feature selection methods, and development of the state of the science analytics (Delaney & Westar, 2017). Accuracy of data is the most worrisome. Working in the emergency department and introducing new ideas to make our jobs easier and increase patient outcomes is always a pleasurable outcome. Working in trauma there was a new tourniquet that was introduced to tie off arterial bleeds. We used this device for about 6 months until it was recalled because some data was omitted which could have cause injury to the patient.

Strategies

Clinicians inputting HER data can mistype words and numbers or copy and paste information from a prior date into a current visit’s entry to save time without editing and updating it appropriately (Hoffman, 2018). With humans there is a possibility of error. Having and error can cause a misinterpretation of research. In relation can mislead clinicians into doing the wrong thing and decrease the outcome of patient safety.

References

Yang, X., Zan, T., Wang, L., & Li, D. (2020). Research on Nursing Management Based on Big Data. 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Measuring Technology and Mechatronics Automation (ICMTMA), 2020 12th International Conference On, 770–772. https://doi-org.ezp.waldenulibrary.org/10.1109/ICMTMA50254.2020.00168

Delaney, C. W., & Westar, B. (2017). Big Data: Data science in nursing. Western Journal of Nursing Research, 39(1), 3–4. https://doi-org.ezp.waldenulibrary.org/10.1177/0193945916671687

Hoffman, S. (2018). Big Data Analytics: What Can Go Wrong. Indiana Health Law Review, 15(2), 227.

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Mercy Ambe Mbu

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5 months ago

Bailey Schaal

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I agree that resources can sometimes be insufficient or dishonest resulting in negative outcomes for patients. Using data mining could help with this process. Data mining would work to find trends in big data and group similarities. If consistencies are found, it is more likely that the information is accurate and of high quality as more than one study backs up the results (Gordon & Shankaranarayanan, 2015). Along with finding quality data, data mining also decreases the workload of the nurse as she can use software to help narrow down searches rather than sifting through data by hand (IBM Cloud Education, 2021). Having a nurse go through data themselves can decrease the instance of technological errors or missed information using different terms, but it is a very onerous task and still has the chance of human error occurring.

References

Gordon, S., & Shankaranarayanan, G. (2021). Assessing and managing the quality of big data. Babson College. Retrieved from https://www.babson.edu/academics/executive-education/babson-insight/analytics-and-big-data/assessing-and-managing-the-quality-of-big-data/#

IBM Cloud Education. (2021). Data mining. IBM. Retrieved from https://www.ibm.com/cloud/learn/data-mining

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5 months ago

Bailey Schaal

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Big data has the potential to improve patient outcomes. Healthcare is constantly changing and evolving. Using big data and nursing informatics helps nurses keep up to date on best practice guidelines while improving communication, managing knowledge, mitigating error, and supporting decision making (Glassman, 2017).

The main challenge of using big data is sifting through the information to find what is useful to your care setting. Jennifer Thew explains that going through all of this information is time consuming and becomes more difficult as terminology used is not always consistent (2016). If studies and data do not use the same wording for the same topic, it is next to impossible to find what you are looking for.

Data mining is one way to navigate big data. Data mining reduces the nurse’s workload by using software to search for meaningful data. This will save the nurse a lot of time and stress as they will not have to sort through information by hand looking for similar terms and information pertaining to their practice. Data mining is also able to look at multiple databases as well as information that has not been added to databases (McGonigle & Mastrian, 2017)). This provides more comprehensive and detailed results as it combines many sources, likely more than a nurse could do on her own. I have not yet used data mining in my practice, but hope to learn more about it and how to use it.

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

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

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

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Annisha Mcgowan

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Hi Bailey, nice reading your post. Big data Voluminous amounts of datasets that are difficult to process using typical data processing; huge amounts of semi structured and unstructured data that are unwieldy to manage within relational databases (McGonigle & Mastrain, 2017). I agree with you a challenge of collecting big data is sorting out reliable information that can be useful. Determining the reliability of information is especially important. Security is important challenge that big data faces. The security of data has become an outstanding problem that needs to be solved urgently. To ensure the security of big data in e-government, judging the security of the data itself has become an important foundation (Fu, 2020). To maintain the security of big data is to have a proper security system in place updating it often.

References

McGonigle, Dee; McGonigle, Dee; Mastrian, Kathleen; Mastrian, Kathleen. Nursing Informatics and the Foundation of Knowledge (p. 558). Jones & Bartlett Learning. Kindle Edition.

Fu, Y. (2020). Evaluation Method of Big Data Reliability in Electronic Government. 2020 International Conference on E-Commerce and Internet Technology (ECIT), E-Commerce and Internet Technology (ECIT), 2020 International Conference on, ECIT, 142–144. https://doi-org.ezp.waldenulibrary.org/10.1109/ECIT50008.2020.00038

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Paola Gaudioso

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Bailey,

Great post. It seems that one of the issues with healthcare data is that there is tons of it. There is past medical history that needs to be uploaded on to the big data network. According to Zhang et al. (2020) most historical data contains valuable information that is needed to analyze treatments and diagnosis for other patients. It seems that data mining is a great way to sort through information. There many formulas that can be created to help sort through the data. The amount of data can seem overwhelming and will continue to grow. According to () the data is in real time so when it is sorted it is helpful to the medical teams. It will be interesting to see what healthcare does with all these IT aspects and how they can be beneficial.

He, W., Nazir, S., & Hussain, Z. (2021). Big Data Insights and Comprehensions in Industrial Healthcare: An Overview. Mobile Information Systems, 1–11. https://doi-org.ezp.waldenulibrary.org/10.1155/2021/6628739

Zhang, Q., Lian, B., Cao, P., Sang, Y., Huang, W., & Qi, L. (2020). Multi-Source Medical Data Integration and Mining for Healthcare Services. IEEE Access, Access, IEEE, 8, 165010–165017. https://doi-org.ezp.waldenulibrary.org/10.1109/ACCESS.2020.3023332

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5 months ago

Amy Birkenstamm

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Nurses, as the largest group of healthcare professionals, are key to quality and safety and to ensuring the best patient outcomes. To make informed practice decisions, nurses need access to aggregate data about their patients and the impact of their care, and they need to know how to interpret data (Glassman, 2017). Joining data between different information sources creates knowledge. When you apply experience, data, information, knowledge, and wisdom, the model forms the basis of informatics (Laureate Education, 2018).

Big data “typically refers to 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,” according to the Online Journal of Nursing Informatics (Thew, 2016). Benefits that can occur due to utilizing big data include streamlining processes and data so that content and pertinent information is cohesive and readily accessible. For both short and long term objectives, this should ultimately save time and standardize systems for a more streamlined approach and improvement with communication amongst the healthcare team.

Potential risks using big data within your organization can include breach of confidentiality since it contains patient records and information that needs to be protected for patient safety. An additional concern includes staff being too hyper focused on the big data system and not necessarily bedside care for the patients, this could lead to a decline in actual close practitioner-patient interactions and quality of care. “The frustration that we often have as nurse leaders in looking at this data, is that some of the variables we care about the most, aren’t even in the data. We don’t have something that measures nursing competence, for example. We don’t have something that measures how committed the nurses are. We don’t have something that measures if the patient really is going to do the stuff we just invested all this time in teaching them to do,” Englebright says (Thew, 2016).

An example of a way to mitigate challenges utilizing big data would be to have added securities for the systems in order to protect patient privacy. Using a technology data back up support system would also be helpful. As far as helping with efficiency as well as valuing and supporting continued quality patient care, proper staffing and ongoing IT support and training can be useful and beneficial for all parties involved and decrease risk for burnout. In conclusion, using big data has both positive and negative potentials when used in healthcare. Education and proper training is key in order to implement big data appropriately and safely.

References

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

Laureate Education (Director). (2018). Data Information-Knowledge-Wisdom [Film].

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

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Annisha Mcgowan

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Hi Amy, I enjoyed reading your post. I too agree with you that big data does need added security to prevent breach of patient privacy. Hospitals should have an adequate security in place to prevent patient data breach. Another challenge that big data can face is technology challenges. The increase in volume needs additional data storage system, storage mechanisms, new environment, and technologies that meet the demands of massive data. Due to the large volume of data used in the Big Data projects, storage is going to be an issue needs to be addressed in early planning stages of the project (Al-Sai, Abdullah, Husin, & Heikal, 2019). Storage of data is especially important, having the adequate space is essential while acquiring big data. Another challenge is training staff to on how to use big data. The key to utilize the outputs from big data analytics effectively is to equip managers and employees with relevant professional competencies, such as critical thinking and the skills of making an appropriate interpretation of the results (Wang, Kung, & Byrd, 2018). Staff need to be proficient in how to utilize and interpreted data to have proficient big data output.

References

Al-Sai, Z. A., Abdullah, R., & husin, M. heikal. (2019). Big Data Impacts and Challenges: A Review. 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Electrical Engineering and Information Technology (JEEIT), 2019 IEEE Jordan International Joint Conference On, 150–155. https://doi-org.ezp.waldenulibrary.org/10.1109/JEEIT.2019.8717484

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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