NURS 6051 Discussion: Big Data Risks and Rewards
Sample Answer for NURS 6051 Discussion: Big Data Risks and Rewards Included After Question
NURS 6051 Discussion: Big Data Risks and Rewards
NURS 6051 Discussion: Big Data Risks and Rewards
As healthcare advances in technology, big data keeps on expanding. Big data are digital information that is high in volume and increasing continuously, and variation is remarkable (Campus, 2015). Big data in healthcare are ample information collected from various resources such as healthcare devices, medical tools, electronic health records, and anything that captures patients’ health information and is stored in an extensive database (Lee & Yoon, 2017).
One benefit of big data in healthcare is providing more data to healthcare providers so they can diagnose, categorize, and differentiate diseases: an example of this is genetic findings. Geneticists can categorize specific genes and diseases associated with them, and the more data they have, the more accurate the findings will be. A couple whose family has Alport Syndrome, a hereditary kidney disease diagnosed through genetic testing, will have answers from their doctor and geneticist on what specific gene will cause the disease. They can choose a path where they can be confident that the specific gene will not pass on to their children, such as In-vitro fertilization with the genetic testing method. Various information from different patients who had kidney disease with no comorbidities was tested genetically, and the information was collected, analyzed, and interpreted.
One potential risks of big data are miscommunication gap. Different hospitals, clinics, laboratories, urgent care, and other healthcare facilities have separate data from the same patient requiring care at a specific time. A specific example is patient A lives in Texas, the primary doctor with Kelsey-Seybold. All his regular annual exams and lab test are with Kelsey-Seybold. He was home complaining of severe abdominal pain that started a few days ago. He got admitted to Memorial Hermann Hospital. He was diagnosed with appendicitis and went through an appendectomy. He went on vacation in Florida the next month. He has a severe allergy, and he got admitted to one of the hospitals. All the patient’s admission and health data are with a specific facility. Most patients remember specific histories regarding their hospitalizations, operations, medication, and even their provider’s name. Providers can diagnose and create a better treatment plan if they have complete information about the patient. This will also save time and different tests and procedures the patient must go through. A one-stop shop for all healthcare records should help solve the issue (Fatt & Ramadas, 2018).
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In the clinic where I work, our Information technology department and Informatics have been working hard to get us access to different hospitals within 100 miles of our clinic. They are the hospitals where e our regular patients go or get admitted. We have access to at least three hospitals. Whenever the patients tell us they were hospitalized for a few days, we ask which hospital, and we can log in and see their health records, helping fill the miscommunication gap.
Introduction
Big data has emerged as a powerful tool in healthcare, offering immense potential to change clinical systems. However, it also brings challenges and risks. This discussion explores one benefit of using big data in clinical systems and a corresponding challenge or risk. A strategy to mitigate the identified challenge or risk will also be discussed.
Benefit
One Significant benefit of utilizing big data in clinical systems is the potential for enhanced clinical decision-making. By analyzing large amounts of structured and unstructured data, including electronic health records (EHRs), patient-generated data, and medical literature, healthcare professionals can gain valuable information that supports evidence-based decision-making. Big data analytics can help identify patterns, trends, and correlations that may not be easily detectable using traditional methods. These insights can aid in diagnosing diseases, personalizing treatments, predicting outcomes, and improving patient safety (McGonigle & Mastrian, 2022).
Challenge/Risk
One major challenge of utilizing big data in clinical systems is ensuring data privacy and security. The increasing volume and complexity of healthcare data pose risks to patient information. Health data contains personally identifiable information (PII), making it attractive to cybercriminals. Combining data from multiple sources raises the risk of unintended data breaches and unauthorized access, potentially compromising patient privacy and confidentiality (Glassman, 2017). Another challenge of data analytics in healthcare is effectively managing and analyzing unstructured data, which requires advanced techniques for text mining, natural language processing, and image recognition to gain meaningful information from sources such as clinical notes, research articles, and medical images (Krylov, 2023).
Mitigation Strategy
Healthcare organizations can adopt a comprehensive approach to secure data governance and encryption to address data privacy and security challenges. This includes implementing strict access controls to limit data access to authorized personnel, using vital encryption techniques to protect data during transmission and storage, removing patient data to minimize the risk of re-identification, and conducting regular audits and monitoring of data access and usage to detect any security incidents (Wang, Kung, & Byrd, 2018). By implementing these measures, healthcare organizations can significantly reduce the risk of data breaches and ensure patient data remains secure and private.
Conclusion
Leveraging big data in clinical systems has the potential to enhance clinical decision-making and improve patient outcomes. However, data privacy and security challenges must be effectively mitigated to realize the full benefits of big data analytics. Implementing secure data governance practices, encryption measures, and regular monitoring can help safeguard patient data and maintain the trust of individuals in the healthcare system.
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
Krylov, A. (2023, March 23). Healthcare data analytics: Major challenges & solutions. Kodjin – Turn-key FHIR Server for Healthcare Data. Retrieved June 27, 2023, from https://kodjin.com/blog/data-analytics-in-healthcare-challenges-and-solutions/#:~:text=One%20of%20the%20challenges%20of,research%20articles%2C%20and%20medical%20images.
McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.
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.
SAMPLE 2
In this week’s discussion, the topic is BIG DATA. According to a heath leaders’ article, big data is “a large complex data set that yields substantially more information when analyzed as a fully integrated data set compared to the outputs achieved with smaller sets of the same data that are not integrated” (The, 2016). In simpler terms, they are inundated with much information such as lab values, imagining reports, point-of-care results, patient assessments, subjective patient symptoms, etcetera. All this information must be analyzed to find the root cause of patients being in the hospital. The whole picture is not just one aspect of a patient, and with big data, you cannot see the forest through the trees.
A Sample Answer for The Assignment: NURS 6051 Discussion: Big Data Risks and Rewards
Title: NURS 6051 Discussion: Big Data Risks and Rewards
As I just mentioned, big data can be helpful in healthcare as it allows you to see the larger picture. You can treat the patient as a whole instead of just one concern. In most cases, this is more achievable in outpatient doctor offices as the patient can be seen on several different occasions, and the big data of the patient can be analyzed over time and developed a comprehensive health plan for the patient. “Good preventative services, for example, could help to avoid many of the common reasons why older—particularly frail—people come into contact with accident and emergency services” (Hughes, 2013, p.618). Primary care providers can formulate complete health care for the patient that they collectively can work on over time.
However, in my field, there is the challenge of treating patients in the emergency room who come in with one complaint, yet since everything in the body is connected to everything else, coming in with a small problem might be caused by a different problem. Utilizing big data is only sometimes efficient in the emergency room (ER). In the emergency room, I have observed the challenge of deciphering all the information about the patient in a short amount of time. In some cases, in that setting, we cannot fix or address the root of a patient’s problem and slap a band-aide on the issue and tell them to follow up with their primary care provider.
Unfortunately, having all the data about one patient cannot be permanently fixed. To address the problem, I asked the patient about the most significant issue that brought them to the ER that day. Then I have to focus on just that issue yet still analyze other lab results or imaging and point of care tests to see if a significant issue is causing their specific issue that day. Before the patient leaves, I educate them on their specific problem and advise if there are topics to discuss with their primary provider to address the more significant issue. This can be a challenge in itself, per an article from MEDSURG “Effective teaching helps patients apply health-related knowledge to their lives” (Flanders, 2018, p. 55). Nevertheless, education may only be effectively received if the patient is receptive and focused on the bigger picture.
When you wake in the morning, you may reach for your cell phone to reply to a few text or email messages that you missed overnight. On your drive to work, you may stop to refuel your car. Upon your arrival, you might swipe a key card at the door to gain entrance to the facility. And before finally reaching your workstation, you may stop by the cafeteria to purchase a coffee.
From the moment you wake, you are in fact a data-generation machine. Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.
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Big data is a large complex data set which provides more information when analyzed as a fully integrated data set as compared to the results of smaller sets of the same data which are not integrated (Big Data Means Big Potential, Challenges for Nurse Execs, 2016). In other words, its complicated. The data collected for healthcare comes from many varying sources which include the government, employers, insurance companies, personal electronic devices and public records to name a few. The data is extensive and complex.
One way that we could use big data could be data from wearable devices or cell phones. These devices have evolved to be able to provide some form of preliminary diagnostic testing to prevent health deterioration (Healthcare Big Data and the Promise of Value-Based Care, 2018). As an example, apple products are now capable of performing simple ECG and interpreting the data collected, they can also monitor pulse and even O2 concentrations. If you took a sample collection of this data, you could use it to predict possible outcomes based on the demographic and health data collected when a person first signs up for these services.
The problem with this type of data is that it is too vast to have any real value for evidence base. The data must be controlled or toned down some to have any real value. If we took all the data collected in a 24-hour period from just one person and integrated it into an EMR it would be exhaustive to interpret. But if we toned the data collection down to periods of exercise it would provide a much clearer picture of the persons cardiovascular status at that time.
One way to mitigate the challenges and risks of using big data is through parameters and regulation of its access. As an example, parameters can be set in place which only allow data to be collected from willing participants at set intervals. If the person starts a workout, they would be prompted to allow their data to be collected. Once collected, further parameters would allow specific data to be analyzed with the irrelevant data left behind. Without a data governance strategy and controls, much of the benefit of broader, deeper data access can be lost (lawton, 2022).
References
Big Data Means Big Potential, Challenges for Nurse Execs. (2016, April 19). healthleaders. Retrieved December 25, 2022, from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execsLinks to an external site.
Healthcare Big Data and the Promise of Value-Based Care. (2018, January 1). nejm catalyst. Retrieved December 26, 2022, from https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0290Links to an external site.
lawton, G. (2022, January 5). 10 big data challenges and how to address them. tech target. Retrieved December 26, 2022, from https://www.techtarget.com/searchdatamanagement/tip/10-big-data-challenges-and-how-to-address-themLinks to an external site.
As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare. In this Discussion, you will consider these risks and rewards.
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To Prepare:
Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.
By Day 3 of Week 4
- Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples.
Thank you for the informative dive into big data in clinical systems and how this technology can augment patient care quality. In the paper, you have highlighted the potential of the technology in consolidating holistic approaches to patient care by providing insights that will enable patients actively take part in their care and make better decisions from both healthcare providers and patients alike.
You have also highlighted the barriers to using big data, mainly in collecting and analyzing data for healthcare providers. Understandably, analyzing big data can be challenging for healthcare providers for several reasons, probably due to the sheer volume of data that needs to be analyzed (Pramanik et al., 2022). Healthcare providers need the right tools and expertise to make sense of all this data and extract useful insights. Analysis can be time-consuming, as it often requires complex algorithms and advanced statistical techniques.
In addition to data mining tools, healthcare centres can also use specialized software and tools. These can include data visualization software, machine learning algorithms, and statistical analysis tools (Ngiam & Khor, 2019). These tools can help to organize and process large amounts of data and provide insights and trends that may not be immediately obvious.
It is also important for healthcare providers to clearly understand their goals and objectives when analyzing big data and to work with a team of experts with experience in data analysis and healthcare (Pramanik et al., 2022). By using these strategies, healthcare providers can more effectively draw useful insights from their data and use these insights to improve patient care and outcomes.
References
Ngiam, K. Y., & Khor, I. W. (2019). Big data and machine learning algorithms for healthcare delivery. The Lancet Oncology, 20(5), e262–e273. https://doi.org/10.1016/s1470-2045(19)30149-4
Pramanik, P. K. D., Pal, S., & Mukhopadhyay, M. (2022). Healthcare Big Data. Research Anthology on Big Data Analytics, Architectures, and Applications, pp. 119–147. https://doi.org/10.4018/978-1-6684-3662-2.ch006
In this week’s discussion, the topic is BIG DATA. According to a heath leaders’ article, big data is “a large complex data set that yields substantially more information when analyzed as a fully integrated data set compared to the outputs achieved with smaller sets of the same data that are not integrated” (The, 2016). In simpler terms, they are inundated with much information such as lab values, imagining reports, point-of-care results, patient assessments, subjective patient symptoms, etcetera. All this information must be analyzed to find the root cause of patients being in the hospital. The whole picture is not just one aspect of a patient, and with big data, you cannot see the forest through the trees.
As I just mentioned, big data can be helpful in healthcare as it allows you to see the larger picture. You can treat the patient as a whole instead of just one concern. In most cases, this is more achievable in outpatient doctor offices as the patient can be seen on several different occasions, and the big data of the patient can be analyzed over time and developed a comprehensive health plan for the patient. “Good preventative services, for example, could help to avoid many of the common reasons why older—particularly frail—people come into contact with accident and emergency services” (Hughes, 2013, p.618). Primary care providers can formulate complete health care for the patient that they collectively can work on over time.
However, in my field, there is the challenge of treating patients in the emergency room who come in with one complaint, yet since everything in the body is connected to everything else, coming in with a small problem might be caused by a different problem. Utilizing big data is only sometimes efficient in the emergency room (ER). In the emergency room, I have observed the challenge of deciphering all the information about the patient in a short amount of time. In some cases, in that setting, we cannot fix or address the root of a patient’s problem and slap a band-aide on the issue and tell them to follow up with their primary care provider.
Unfortunately, having all the data about one patient cannot be permanently fixed. To address the problem, I asked the patient about the most significant issue that brought them to the ER that day. Then I have to focus on just that issue yet still analyze other lab results or imaging and point of care tests to see if a significant issue is causing their specific issue that day. Before the patient leaves, I educate them on their specific problem and advise if there are topics to discuss with their primary provider to address the more significant issue. This can be a challenge in itself, per an article from MEDSURG “Effective teaching helps patients apply health-related knowledge to their lives” (Flanders, 2018, p. 55). Nevertheless, education may only be effectively received if the patient is receptive and focused on the bigger picture.
References
Flanders, S. A. (2018). Nurses as educators. Effective patient education: Evidence and common sense. MEDSURG Nursing, 27(1), 55-58.
Hughes, R. (2013). It is time to reform urgent and emergency care. British Journal of Healthcare Assistants, 7(12), 618-619. https://doi.org/10.12968/bjha.2013.7.12.618
Thew, J. (2016, April 19). Big data means big potential, challenges for nurse Execs. HealthLeaders Media. https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs?page=0%2C1
By Day 6 of Week 4
Respond to at least two of your colleagues* on two different days, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.
*Note: Throughout this program, your fellow students are referred to as colleagues.
Big data refers to a large and complex set of data that, when examined as a wholly integrated data, yields essential information than a small unintegrated collection of data. In the contemporary world, big data is increasingly becoming more prevalent, impacting nursing in various ways. Big data offers a nursing system a considerable opportunity to advance the vision of promoting human health and well-being. Although big data analytics is riddled with challenges, it is useful in decision-making in clinical systems.
Big data is a promising breakthrough in health care decision making. Big data analytics in the context of nursing enable an organization to analyze large volumes and velocity of data from various nursing networks to aid in evidence-based decision making and action (Macieira et al., 2017). It allows the integration of clinical information that provides health care insights to help nurses meet patients’ needs and improve the quality of healthcare. Moreover, big data can be used to understand the impact of nursing care and to expand the responsibility to meet continuous emerging needs.
Big data allows clinical systems to realize informatics benefits, including improved quality and accuracy of clinical decision and instant access to vital health records and information. Additionally, big data is a potential source for managerial benefits which allow health care organization to monitor and monitor the firm’s resources and evaluate the operation and support strategic business decisions (Wang, Kung & Byrd, 2018). Furthermore, big data analytics gives the clinical system the capability to generate accurate data and make predictions based on new observations. Predictive analytics play a crucial role in the clinical order of reducing uncertainty and preventing readmissions
Lack of substantial experience in big data analytics is one of the most significant challenges impeding the realization of maximum big data benefits. Evidence indicates that only a few percentages of health care organizations have the capability to conduct rigorous big data analytics to aid in the decision-making process (Wang et al., 2018). The lack of understanding by clinical officers on the value of big data analytic in the clinical system compounds this challenge. Therefore, the clinical system can leverage big data analytics as a means of transforming nursing in the era of informatics.
References
Macieira, T. G., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2018). Evidence of progress in making nursing practice visible using standardized nursing data: A systematic review. AMIA Annual Symposium Proceedings Archive, 1205–1214. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977718/
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, 3-13. doi:10.1016/j.techfore.2015.12.019
Thank you for your articulate post. As you explained big data can be used to understand the impact of nursing care and expand the responsibility to meet continuous emerging needs. Big data can be used in a variety of industries. The top benefits of using big data in healthcare include advancing patient care, improving operational efficiency, finding cures for diseases (Business Wire, 2018). An example of improving operational efficiency would be running reports on readmissions of COPD exacerbations. With this data, a company can examine the admission rates while analyzing staff efficiency. Predictive analytics can be used on the COPD patient to understand key factors in readmissions.
As nurses, we understand that healthcare is continually changing. The same principals can be applied to big data. One of the biggest problems with big data is that “it grows constantly, and organizations often fail to capture the opportunities and extract actionable data” (Joshi, 2018). Because of this, we may miss opportunities to best serve our patients.
References
Business Wire. (2018). Top Benefits of Big Data in the Healthcare Industry. Retrieved from https://www.businesswire.com/news/home/20180207005640/en/Top-Benefits-Big-Data-Healthcare-Industry-Quantzig
Joshi, N. (2018). Problems with Big Data that we failed to notice. Retrieved from https://www.allerin.com/blog/problems-with-big-data-that-we-failed-to-notice
Cybercrime in healthcare indeed puts a patient’s health and privacy at risk. “It is one of the most targeted sectors globally; 81% of 223 organizations surveyed, and >110 million patients in the US had their data compromised in 2015 alone” . We often think about cybercrime in terms of a virus or spyware stealing information, or money, however, cybercriminals are always coming up with new schemes. For example, in 2016 The Hollywood Presbyterian Medical Center’s entire computer system was essentially hijacked for ransom, “shut down its network for ten days, preventing staff from accessing medical records or using medical equipment until the hospital paid the ransom” .
The increased use of nursing informaticists in the healthcare system is a step in the right direction for hospitals as they continue to fight cybercrime while expanding the use of big data. The article, Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations, finds that while hospital systems are investing a large number of finances into big data, much of its capabilities are still underutilized . As hospital systems increase the usage and find new applications for big data, opportunities for cybercrime will increase, and informaticists will have to be ever vigilant in their protection of patient privacy.
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NURS 6051 Discussion: Big Data Risks and Rewards
In this week’s discussion, the topic is BIG DATA. According to a heath leaders’ article, big data is “a large complex data set that yields substantially more information when analyzed as a fully integrated data set compared to the outputs achieved with smaller sets of the same data that are not integrated” (The, 2016). In simpler terms, they are inundated with much information such as lab values, imagining reports, point-of-care results, patient assessments, subjective patient symptoms, etcetera. All this information must be analyzed to find the root cause of patients being in the hospital. The whole picture is not just one aspect of a patient, and with big data, you cannot see the forest through the trees.
As I just mentioned, big data can be helpful in healthcare as it allows you to see the larger picture. You can treat the patient as a whole instead of just one concern. In most cases, this is more achievable in outpatient doctor offices as the patient can be seen on several different occasions, and the big data of the patient can be analyzed over time and developed a comprehensive health plan for the patient. “Good preventative services, for example, could help to avoid many of the common reasons why older—particularly frail—people come into contact with accident and emergency services” (Hughes, 2013, p.618). Primary care providers can formulate complete health care for the patient that they collectively can work on over time.
However, in my field, there is the challenge of treating patients in the emergency room who come in with one complaint, yet since everything in the body is connected to everything else, coming in with a small problem might be caused by a different problem. Utilizing big data is only sometimes efficient in the emergency room (ER). In the emergency room, I have observed the challenge of deciphering all the information about the patient in a short amount of time. In some cases, in that setting, we cannot fix or address the root of a patient’s problem and slap a band-aide on the issue and tell them to follow up with their primary care provider.
Unfortunately, having all the data about one patient cannot be permanently fixed. To address the problem, I asked the patient about the most significant issue that brought them to the ER that day. Then I have to focus on just that issue yet still analyze other lab results or imaging and point of care tests to see if a significant issue is causing their specific issue that day. Before the patient leaves, I educate them on their specific problem and advise if there are topics to discuss with their primary provider to address the more significant issue. This can be a challenge in itself, per an article from MEDSURG “Effective teaching helps patients apply health-related knowledge to their lives” (Flanders, 2018, p. 55). Nevertheless, education may only be effectively received if the patient is receptive and focused on the bigger picture.
References
Flanders, S. A. (2018). Nurses as educators. Effective patient education: Evidence and common sense. MEDSURG Nursing, 27(1), 55-58.
Hughes, R. (2013). It is time to reform urgent and emergency care. British Journal of Healthcare Assistants, 7(12), 618-619. https://doi.org/10.12968/bjha.2013.7.12.618
Thew, J. (2016, April 19). Big data means big potential, challenges for nurse Execs. HealthLeaders Media. https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs?page=0%2C1
Collapse Subdiscussion
Big data is getting to be more of an issue due to security. It is a good think because as you mentioned trends can be viewed that will improve patient outcomes.
RESPONSE 1,
Big data allow you to see the larger picture of your patient, especially if you are working in the emergency room [ER]. It is very challenging. You must have seen a lot of cases. When you get your patient history and assess them in totality, it will give you a more significant idea or picture of their problem.
Big data allow nurses and other healthcare professionals to deliver the best care possible. It can be used by various parties in hospitals or nursing homes, further reducing the expense of medical errors while promoting safe practice.
Data is a crucial driving force for organizational change and new developments. The more information a healthcare organization has, the more it can organize itself to deliver the best healthcare services to its clients. Therefore, big data in the healthcare sector refers to vast volumes of data generated from adopting digital technologies and interactions between healthcare stakeholders and healthcare systems in collecting, documenting, and retrieving healthcare data (Wang et al., 2018).
From research studies, government agencies, and laboratory results, healthcare personnel can collect big data; all these have proved to help manage organizational performance. one of the main advantages of using big data in the healthcare industry is the ability to anticipate future trends and events of specific parameters, which would then serve as actionable information that serves as the foundation for evidence-based interventions. (Wang et al., 2018). Big data, for instance, could assist a company in forecasting future trends in the prevalence of lifestyle diseases among a specific community. The company could use the prediction to develop evidence-based interventions targeting cost-cutting, value-based care, and higher service quality.
REFERENCE
Wang, Y. Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3–13. doi 10.1016/j.techfore.2015.12.019.
big data can offer many benefits to healthcare, but it also presents challenges that need to be addressed. By understanding these challenges and taking steps to address them, businesses can unlock the full potential of big data. New software and new inventions make the front end more user-friendly, I like EPIC better as the front end, is easily teachable and easily learnable and self-explanatory in many areas.
Challenges are there in any software that is cloud-based and internet-based, no doubt about it, the benefits out reaches the risks. Emergency preparedness and risk handling will help healthcare organizations to work efficiently and in a cost-effective way with big data, maybe the data should be analyzed and unnecessary data deleted over time, and encrypted after a certain time period, Patient charts and employee files have to be stored for 7 years in texas.
References:
National Institute of Standards and Technology. (2010). Contingency planning guide for information technology systems (NIST SP 800-34 Rev. https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-34r1.pdfLinks to an external site.
The definition of big data in the context of healthcare is “electronic health data sets that are so vast and complex that they are difficult (or impossible) to manage with traditional software and/or hardware; nor can they be easily managed with traditional or common data management tools and methods” (Keenan, p. 1, 2014). Big data in the clinical system has advantages like expanding diagnostic services, lowering hospital expenses, and keeping patients healthy (NEJM Catalyst, 2018). Big data keeps people healthy in ways that were previously impossible to track. Tech companies like Apple have collaborated with insurance providers and other companies to offer rewards to customers who reach specific fitness or weight reduction targets.
The patient and any providers with whom they may share the info may both benefit from its incorporation. Giving patients access to more treatment is a requirement for expanding diagnostic services with big data. Patients can contact a doctor or other healthcare provider by opening an app on their phone. Access to healthcare and services is made simple by this program. Additionally, big data lowers the price of hospital stays and numerous readmissions. In order to discharge patients more quickly and possibly prevent a readmission, hospitals and doctors can watch patients more closely and take action more quickly thanks to the data.
Potential Health Insurance Portability and Accountability Act (HIPAA) breaches are one issue that could arise with the use of big data in healthcare. Healthcare professionals should be concerned about confidential information being accessed due to the rapidly developing technology. The tools we use in the clinical setting have passwords and other security measures, but nothing is ever completely secure against information falling into the wrong hands.
De-identifying healthcare data before it is shared with researchers or other people is one potential countermeasure I have studied to the risk of HIPAA violations. The 18 steps that must be completed or checked off on the data are the best way to de-identify it. Removing names, zip codes, phone numbers, fax numbers, photos, email addresses, physical mailing addresses, and any insurance information is among the 18 things on this list (Kayaalp, 2018). Although it takes a lot of time and effort, I think it is crucial to keep up study while protecting patient privacy.
After the new Health Information Technology Act (HITECH) of 2009 went into effect, the electronic health record (EHR) became the largest application of big data in the healthcare industry. The HITECH act in the U.S. have been the reason why 80% of acute care hospitals that uses the EHR are certified (Ross, 2014, p. 97). The one benefit of the EHR program is the ability of the platform to absorb large amounts of data such as a patient demographics, medical history, allergies and laboratories values. This makes it easy for clinicians to improve patient quality of care by tracking progress and identifying potential problems early in their medical history.
If a clinician attempts to order norco for pain, the EHR will display a best practice advisory (BPA) alert and flag the patient codeine allergy; this will redirect the clinician to considering another pain medication that would not put the patient at risk for an adverse drug reaction. Adverse drug events are estimated to occur in 30% or more of hospital stays and cost billions of dollars (Ross, 2014, p. 98). This is a great benefit big data have provided in the development of the EHR program.
The major challenge with EHR is keeping patient health information safe by preventing access to unauthorized individuals. At my facility, the IT department have assigned employee education modules on cyber-attacks. For example, an employee received an email from an IT associate about a Hospital EHR update and upon request, gave their login name and password. Shortly after the information was given, the cyber attacker was able to login into the EHR, steal approximately 50 patient’s information and changed all the employee direct deposit bank accounts to an off shore financial institution in the Caribbean. Luckily, the IT department was notified immediately and was able to prevent the massive transfer of money, but was not successful in protecting the patient’s healthcare data. After this sentinel event, all employees are quarterly required to take education modules on the best practices to mitigate the challenges of a cyber-attack.
It is important to never give your password out and to always encrypt your emails. The new tiger connect system at my facility have features to encrypt your passwords and email before sending a message. In order to hold employees accountable, leadership have started to audit employees to ensure their following the safety protocols. Sometimes fake emails will be sent out to employees to ensure everybody understand there is never a right time to give their login information and the IT associates will never ask for this information.
At the hospital, there are policies and procedures that serve to maintain patient privacy and confidentiality; for example, employees must not share their ID with anyone, always log off when leaving a computer and only use their own ID to access patient digital records (Jamshed, 2015, p. 75). We use a new device called WaveID, where your password automatically log in after waving your employee ID over a keyboard magnet. It is a convenient and easy for an individual to steal your badge, which is why it is important to follow the policy in place and mitigate the risk of another cyber-attack.
Data leaking poses a severe risk to business activities, including those of firms, healthcare, and governmental organizations. According to Cheng et al. (2017), losing sensitive information can negatively impact an organization’s long-term stability and cause considerable reputational and financial losses. Data on employees or customers, as well as intellectual property and medical records, are common information that can be leaked.
In terms of healthcare, big data analysis has gradually permeated the medical industry due to the rapid development of Internet technology. Many electronic medical records, hospital information systems, medical imaging, and other data have emerged due to the digitalization of medical information. While the development of big data in the field of health care has brought enormous economic and social benefits to society, data privacy and security has become increasingly important. The privacy breach of health care data has had a negative impact, whereas, in the context of big data, the method of privacy disclosure is more secretive. As a result, correctly identifying privacy security risks can reduce the likelihood of risking medical data ( Lv & Qiao, 2020).
The day-to-day operations of the institution help generate millions of data that, over the course of time, will require proper channels of transmission, storage, processing, assimilation, and utilization. This is because of the rapidly accelerating pace of technological advancement in the health care sector. Following on from the vast amount of data that is generated, some of its benefits include but are not limited to functioning as a pattern discovery aid with relation to the amount of variance or similarity in the cases that are seen by a specific health care organization, the data bank develops a pattern memory that helps the facility better prepare based on the statistical evidence that is derived from their previous encounter with a surge of disease only relative to the hospitals geographical location, and the data bank also functions as a pattern memory that helps the facility better prepare “The act of just gathering data in order to respond to a question posed by an end user is only one aspect of pattern discovery. Data mining technologies search through datasets to discover patterns that were previously unknown. The information that is predictive and proactive that is produced as a consequence of data mining analytics is therefore helpful in the creation of business intelligence, particularly in regard to how we might become better. (McGonigle, 2017, p477)
Big data’s ability to improve continuity of care is another advantage of using it. From the moment a patient checks into the hospital until the moment they are discharged, the vast amount of data generated from laboratory testing, imaging, or other specialized tests ensures continuity of care. This is possible because every department and or axillary health care support system can access such data and proceed with their plan of care without having to redo the efforts of redoing labs and imaging s. Big data’s ability to improve continuity of care
In order to simplify and streamline the nursing workflow that occurs inside EHRs, eliminating unnecessary duplication of effort would go a long way. The electronic health record (EHR) may be connected to various patient care equipment, such as heart monitors, vital sign monitors, and I.V. infusion pumps. A great number of them are, in essence, little computers that store and transmit their discrete data to the electronic health record (Glassman, 2017). Given the rapid pace at which technical advances in health care are being made, there are a number of additional concerns that need quick consideration. These include the dangers and difficulties involved with its use. Because the usefulness and operability of the system are dependent on continuous power supply, unplanned power interruptions during severe weather conditions offer a significant risk factor to the utilization of the large data warehouse. Cooperation on the part of the patient to embrace new development and the changes made to enhance their care also presents with a risk factor to the overall functioning of the parameters that have been put into place, which is another one of the challenges that comes with the use of the big data system.
Another challenge that comes with the use of the big data system is how to keep the system running during moments of downtime or system upgrades. The fact that some of the characteristics that are most important to us as nurse leaders aren’t even included in the statistics is one of the things that causes the greatest aggravation for us as nurse leaders when we look at this data “Englebright adds. “For example, we do not have anything that can quantify the level of skill in nursing. We do not have anything that can assess the level of dedication shown by the nurses. We do not have anything that can determine whether or not the patient will really carry out the tasks that we have just spent a significant amount of time instructing them to carry out. (Thew, 2016)
Using the following solutions is one of the strategies that, in my experience and/or observation, has the potential to successfully minimize the issues that are linked with big data. However, this list is not exhaustive. The usage of backup generators has shown to be a more effective means of handling unanticipated disruptions in the power supply while maintaining our commitment to provide our customers with continuous service. When the hospital expects a low population, such over the holidays, when operations inside the hospital might be managed by a skeleton team, planning downtime and/or system improvements should be scheduled. On the event that the main system and data bank of the organization suffer a failure, the idea of having a backup data storage in the cloud will also serve the goal of making the data easily accessible and retrievable in an efficient manner.
Just to add on what you have put across on the challenges: Considering the millions of data put out by every individual daily, it might be complicated to develop an appropriate way to properly understand how this information can help the nurses (Thew,2016). Data analytics is crucial in understanding big data, but most nursing leaders do not have the skills to properly analyze the information and come up with conclusive results that can help the nurses. Additionally, the nursing leaders do not have information that reflects on their nurses and patients (Thew,2016). This means they cannot understand whether the nurses are committed to their work and whether the patients will follow the instructions given to them. For most nursing leaders, this is a time and labor-intensive process when manually analyzing the information on their patients and nurses.
Reference:
Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs, and Links to an external site. Links to an external site. Retrieved from https://www.
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