NURS 6051 BIG DATA RISKS AND REWARDS
Walden University NURS 6051 BIG DATA RISKS AND REWARDS – Step-By-Step Guide
This guide will demonstrate how to complete the Walden University NURS 6051 BIG DATA RISKS AND REWARDS assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.
How to Research and Prepare for NURS 6051 BIG DATA RISKS AND REWARDS
Whether one passes or fails an academic assignment such as the Walden University NURS 6051 BIG DATA RISKS AND REWARDS depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.
After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.
How to Write the Introduction for NURS 6051 BIG DATA RISKS AND REWARDS
The introduction for the Walden University NURS 6051 BIG DATA RISKS AND REWARDS is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.
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How to Write the Body for NURS 6051 BIG DATA RISKS AND REWARDS
After the introduction, move into the main part of the NURS 6051 BIG DATA RISKS AND REWARDS assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.
Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.
How to Write the Conclusion for NURS 6051 BIG DATA RISKS AND REWARDS
After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.
How to Format the References List for NURS 6051 BIG DATA RISKS AND REWARDS
The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.
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Sample Answer for NURS 6051 BIG DATA RISKS AND REWARDS
Benefits and Challenges of Using Big Data in Clinical Settings
Big data has the potential to revolutionize healthcare delivery by providing healthcare professionals with near real-time data and in-depth insights that can be used to inform decisions, improve patient outcomes, and drive value. The use of big data in clinical settings can provide numerous benefits, such as improved patient safety, enhanced decision-making, and increased efficiency (HealthLeaders, 2016). However, there are also potential challenges and risks associated with the use of big data, such as data privacy and security concerns, lack of proper data governance, and the need for more sophisticated data analytics tools.
One potential benefit of using big data as part of a clinical system is improved patient safety. Studies have proven that utilizing big data analytics can help identify potential risks to patient safety or uncover potential trends or patterns in patient data that could lead to better decisions and treatments (Wang et al., 2018). This can help minimize errors, improve the quality of care, reduce medical costs, and make sure that patients receive the best possible care. By collecting and analyzing data from various sources, healthcare providers can gain insights into patterns and trends that could identify potential safety risks. For example, a hospital could use big data to analyze its medical records to identify trends in patient outcomes in different units such as critical care (Sanchez-Pinto et al., 2018). This could include the types of treatments that are most successful and those associated with the highest mortality rates. With this information, the hospital then takes steps to improve safety by implementing better protocols or changing treatments to reduce the risk of adverse outcomes. Big data can further be applied to detect potential errors or inefficiencies in processes or practices. By analyzing the data, healthcare providers can identify areas where processes can be improved or streamlined to reduce the risk of errors and improve patient outcomes.
One potential challenge or risk of using big data as part of a clinical system is data privacy and security concerns. As healthcare providers increasingly rely on big data to store and analyze patient data, the risk of a data breach or unauthorized access to patient data increases. Data breaches can occur when hackers gain access to a healthcare provider’s network, or when an employee with access to sensitive data unwittingly shares it with an unauthorized third party (Khan et al., 2021). Data breaches can also occur when malicious software is installed on a healthcare provider’s system, allowing unauthorized access to patient data. To protect patient data and ensure privacy, healthcare providers must implement robust data security measures, such as encryption and access control systems. Healthcare organizations must also ensure that all staff members involved in handling patient data are properly trained in data privacy and security protocols.
One strategy to mitigate the challenges and risks of using big data is to develop a comprehensive data governance framework. A data governance framework should include policies and procedures that define how data is used, who has access to it, and how it is protected (Haneem et al., 2019). This framework is developed following relevant laws and regulations, such as the Health Insurance Portability and Accountability Act (HIPAA). The organizations should have a dedicated team responsible for the implementation and monitoring of the data governance framework. The framework should include policies and procedures that define who has access to patient data and how it is used, as well as measures to ensure data security and privacy. Healthcare organizations should have a dedicated team responsible for the management and oversight of data governance. This team ought to be composed of experts from various disciplines, such as clinical experts, data scientists, and information technology professionals. With a comprehensive data governance framework in place, healthcare organizations can ensure that patient data is protected, while still being able to take advantage of the potential benefits of big data.
References
Haneem, F., Kama, N., Taskin, N., Pauleen, D., & Abu Bakar, N. A. (2019). Determinants of master data management adoption by local government organizations: An empirical study. International Journal of Information Management, 45, 25–43. https://doi.org/10.1016/j.ijinfomgt.2018.10.007Links to an external site.
HealthLeaders, H. L. (2016). Big data means big potential, and challenges for nurse execs. HealthLeaders Media. Retrieved March 20, 2023, from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execsLinks to an external site.
Khan, F., Kim, J. H., Mathiassen, L., & Moore, R. (2021). Data Breach Management: An integrated risk model. Information & Management, 58(1), 103392. https://doi.org/10.1016/j.im.2020.103392Links to an external site.
Sanchez-Pinto, L. N., Luo, Y., & Churpek, M. M. (2018). Big Data and data science in critical care. Chest, 154(5), 1239–1248. https://doi.org/10.1016/j.chest.2018.04.037Links to an external site.
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
Organizations often use data with new developments in organization. Organizations use information to organize and deliver quality health care services to clientele. Big data in the healthcare sector, therefore, refers to huge volumes of data generated from the adoption of digital technologies and interactions between healthcare stakeholders and healthcare systems in the collection, documentation, and retrieval of healthcare data (Wang et al., 2018). For example, government agencies use big data through research studies and laboratory results to manage and organize their performances. Big data can also help and organization to predict changes and trends in diseases with age groups to form evidence-based data, thus forming interventions to enhance the quality of life, value, and cost reduction in patient care services.
Electronic documentations are used a lot more in health care organizations. This is used by collecting and documenting large data in detailed entity of patient care; however, this can lead to flooding of information systems making data unmanageable. One of the greatest challenges of big data is the access to patient data regarding proprietary rights, privacy, and interoperability. According to Perlin (2016), interoperability is the ability of healthcare information systems to exchange vital health data within and across organizational boundaries and present it in a way that is understandable to the user. The HIPAA (Health Insurance Portability and Accountability) often becomes unconsidered for access to patient data is repeatedly breeched, which interferes with healthcare professionals’ ability to share and document patient information effectively. Sharing data, within the parameters of the Health Insurance Portability and Accountability Act, supports the meaningful use of EMRs to distribute patient information in health care (McGonigle et al, 2017).
An essential strategy to solve the accessibility challenges in big data sharing is the implementation of frequent security evaluations and procedures. This action could be carried out by encrypting big data and ensuring that health care professionals are practicing professional integrity. Many health care systems should strive to have mature EMR systems to support meaningful use and honesty. upgrading pre-existing information systems within health facilities will enhance the ability to share health information between providers and between health facilities efficiently (Perlin, 2016). This strategy I believe would aid in eliminating patient privacy breaching and risks associated while sharing big data amongst providers. There is an urgent need to understand the managerial, economic, and strategic impact of big data analytics and explore its potential benefits driven by big data analytics, and this will enable healthcare practitioners to fully seize the power of big data (Wang et al, 2018).
References
McGongile, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.
Perlin, J. B. (2016). Health information technology interoperability and use for better care and evidence. Jama, 316(16), 1667-1668. doi:10.1001/JAMA.2016.12337
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.
Also Read:
HEALTHCARE INFORMATION TECHNOLOGY TRENDS
LITERATURE REVIEW: THE USE OF CLINICAL SYSTEMS TO IMPROVE OUTCOMES AND EFFICIENCIES
THE INCLUSION OF NURSES IN THE SYSTEMS DEVELOPMENT LIFE CYCLE
THE ROLE OF THE NURSE INFORMATICIST IN SYSTEMS DEVELOPMENT AND IMPLEMENTATION
Sample Response for NURS 6051 BIG DATA RISKS AND REWARDS
Hello Nia,
I agree with you that organizations use big data for new developments. However, the nature of the data may vary depending on the type of the organization. For instance, healthcare organizations may have detailed data about issues related to the health status of their patients. Big data are generated from digital space (Mehta & Pandit, 2018). Besides, the interactions between the healthcare stakeholders and the patients provide huge volumes of data. Big data is crucial in generating information that can be used to explain trends in certain health complications and predict changes (Pastorino et al., 2019). Unfortunately, working on the big data may be hectic and tedious to some people due to the huge volume. Besides, people handling the big data may fail to protect the data from malicious use. As a result, the data may be misused without the owners’ consent. In healthcare institutions electronic documentations are used to perform various obligations (Kaur et al., 2018). Therefore, healthcare providers gather confidential information from their clientele. Work ethics require healthcare professionals to protect patient health information from an unauthorized access.
References
Kaur, P., Sharma, M., & Mittal, M. (2018). Big data and machine learning based secure healthcare framework. Procedia computer science, 132, 1049-1059. https://doi.org/10.1016/j.procs.2018.05.020
Mehta, N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics, 114, 57-65. https://doi.org/10.1016/j.ijmedinf.2018.03.013
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
Sample Answer 2 for NURS 6051 BIG DATA RISKS AND REWARDS
Big data affects everything from the food we eat, the car we drive, and the healthcare we receive. Advances in technology has continuously increased the amount of data available, while also expanding the type of data we can collect. According to Pastorina et al. (2019) big data can potentially detect patterns and turn high volumes of information into actionable knowledge that can be used to improve clinical decision making. One of the biggest potential benefits that big data can provide is cost reduction for both the organization and the patient. There are several opportunities for big data to reduce costs, accurate record keeping reduces repetitive procedures or treatments, expanded networking allows for patients to find cheaper treatment options and medications, and new applications can detect patters of fraud and waste. “Applications use large volumes of complex codified and free text data extracted from claims and hospital discharge data to identify claims that represent fraud, abuse, waste, and errors” Srinivasan & Arunasalam, (2013).
One of the potential risks of big data in healthcare is the opportunity for a patients personal information to be stolen and exploited illegally. HIPAA laws are in place to protect identifying patient health information, however, the expansion of big data presents more and more opportunities for information to be illegally obtained. The use of personal cellphones by patients and providers makes it easier for a person to access their information without the direct physical protection a healthcare facility provides. It is very common to lose or misplace a cellphone. Social media also presents an additional problem, as patients may be tempted to post information that could be used to hack passwords.
The best strategy I have experienced that may effectively mitigate this challenge, is the use of private VPNs, encrypted cellphones and laptop, and forbidding the use of personal electronics. I have personally witnessed the success of these actions. Electronic devices provided by the healthcare organization can be more easily tracked and protected than most personal devices. Data security is an ever-evolving issue, as competing technologies challenge each other for protection vs the ability to infiltrate electronic devices. Healthcare organizations and providers need to remain current on everything that affects patient data.
References
HealthLeaders. (n.d.). Big data means big potential, challenges for nurse
Execs. https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-
challenges-nurse-execs
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.
Srinivasan, U., & Arunasalam, B. (2013). Leveraging big data analytics to reduce healthcare
costs. IT professional, 15(6), 21-28.
Sample Response for NURS 6051 BIG DATA RISKS AND REWARDS
Kelsey I concur with you that in the modern digitalized world big data affects everything related to humanity. Technological advancement has resulted to increased use of technology (Pastorino et al., 2019). As people use technology they avail their personal and other information to various technology. Hence, it becomes easy for experts to gather big data even without the owners’ knowing. The benefit of big data is cost reduction for organization and the patient (Dash et al., 2019). Healthcare organizations may incur high expenses to collect the information that can be easily generated by the big data. Besides, the process of obtaining the large volumes of information may take a long duration. Therefore, healthcare organizations and patients have saved both their resources and time while using big data (Bates et al., 2018). However, the nature of information in the big data exposes patients to the risk. Healthcare providers and other experts using big data may be duped into misusing the information. Alternatively, the experts may be unaware of leaking crucial patient health information.
ReferencesBottom of Form
Bates, D. W., Heitmueller, A., Kakad, M., & Saria, S. (2018). Why policymakers should care about “big data” in healthcare. Health Policy and Technology, 7(2), 211-216. https://doi.org/10.1016/j.hlpt.2018.04.006
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25.
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
Sample Response for NURS 6051 BIG DATA RISKS AND REWARDS
Kelsey,
Thank you for your post. We live in a world of electronics where more time is spent staring into a screen rather than looking at the person. This opens up many risks for data to be stolen, seen, or misused. I think we have all been a part of a data breach at one time or the other from our hospital or physician’s office. In 2005-2019 there were reported to be almost 250 million people were involved in data breaches (Seh et al., 2020). I don’t remember breaches occurring as much prior to the advancement of technology or maybe it just was not found as easily. There are many ways to safeguard against breaches within an organization. One way is role-based controls where only workers in that specific role can access information with a private username/password (Kruse et al., 2017). We just implemented an additional security measure to our charting system at my place of employment. When logging on we have to enter our username/password then be issued a code that must be entered as well prior to access. Thanks again for your post.
Tina
References
Kruse, C.S., Smith, B., Vanderlinden, H., & Nealand, A. (2017). Security Techniques for the Electronic Health Records. Journal of medical systems, 41(8), 127. https://doi.org/10.1007/s10916-017-0778-4
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
Sample Answer 3 for NURS 6051 BIG DATA RISKS AND REWARDS
The healthcare industry continues to use data to improve healthcare technology to enhance patient care outcomes. Relevant patient care information recorded and stored in electronic format makes sharing information between nurses and health care providers more efficient (Office of National Coordinator of Health Information Technology, 2017). An article found in Medical New Bulletin (2016) states that big data will continue to influence health care positively by improving patient quality of care, diagnosing diseases, and delivery of care. Although big data is positively impacting the delivery of health care information, barriers exist. Challenges to using big data include security issues, technical difficulties, and employee skills and knowledge (Medical News Bulletin, 2016).
The survey process in the long term care setting is one example of how surveyors use big data to validate compliance with federal regulation. The annual survey process has changed significantly over the last three years. Before recent updates, facilities in my state could estimate which patients were on the case mix (list of patients selected for monitoring) based on patient data submitted in the Minimum Data Set (MDS). Now, facilities cannot predict a case mix because a real-time algorithm generates at list based on the facility’s census and initial observation rounds. For example, a surveyor observes an immobile patient sitting in a chair greater the two hours without being repositioned by staff, that patient would trigger for the case mix after data is placed into the algorithm. The surveyors have remote access to patients’ electronic records (ER). By looking at the ER, surveyors can see all patient information. They can use that data to determine if patients are at risk for pressure ulcers, have impaired mobility, are incontinent, or at risk for pressure ulcers. Surveyors monitor the selected patients for positioning to establish an isolated incident or pattern. According to McGonigle and Mastrain (2018), determining patters is more than collecting data to answer questions. Big data collection is gathering information to identify patterns to establish meaningful insight (McGongile & Mastrain, 2018, a.).
Once the survey is complete, the facility’s 2567 (survey report) is forwarded to the Department of Health and Human Services (DHS) and Centers for Medicare and Medicaid Services (CMS) database. One advantage of using big data in the scenario is after survey results are uploaded to their perspective databases, the nursing home association collects the data. The association compiles a list of the top 10 cited decencies from all facilities in the state. Patient and facility confidentiality is protected during this process. That information is distributed facilities across the state to validate their compliance and provide opportunities to improve patient-centered care outcomes before the next survey. McGongile & Mastrain (2018) states that nurses should continue to learn innovative ways to collect data, and nurses must continue to become knowledgeable in analyzing and interpreting information (McGongile & Mastrain, 2018, b.). On risk for using this bid data is the validity of accurate information. I have worked at a few facilities where the MDS coordinators were imputing inaccurate information in the MDS. The patient in the electronic record did not reflect the patient in person; therefore, the MDS was incorrect, which lead to negative survey outcomes. The MDS coordinator and members of the interdisciplinary team received extra training from a MDS and other consultants. With proper training, the problem was corrected.
References
McGonigle, D., & Mastrain, K. G. (2018, a.). Nursing informatics and the foundation of
knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning
McGonigle, D., & Mastrain, K. G. (2018, b.). Nursing informatics and the foundation of
knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning
Medical News Bulletin (2016, February 18). Barriers to the use of big data for healthcare.
Retrieved from https://medicalnewsbulletin.com/barriers-to-the-use-of-big-data-for-healthcare/
Office of the National Coordinator of Health Information Technology. (2017). Standard nursing
terminologies: A landscape analysis. Retrieved from https://www.healthit.gov/sites/
default/files/snt_final_0530219.pdf
Sample Response for NURS 6051 BIG DATA RISKS AND REWARDS
Thank you for your post. When working on this discussion, I did not even think about surveys which is an excellent example of data collection. Surveys are great tools to collect data quickly to get insight into patient care and experience (National Library of Medicine, n.d.). With all the information obtained, someone needs to look at it and decide what it means. A large amount of data can be collected through surveys that must be extracted and used for improvements (Glassman, 2017). With extraction and interpretation comes room for errors. Human error, computer error, and understanding the significance of the data are just a few ways for errors to occur. Once again, great post.
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
National Library of Medicine. (n.d.). Surveys. https://www.nlm.nih.gov/nichsr/stats_tutorial/section3/mod1_surveys.html
Sample Response for NURS 6051 BIG DATA RISKS AND REWARDS
Thank you for your input. The hospital we serviced was already utilizing EPIC before our arrival. We worked with the EPIC development team, IT, and clinical staff to create the workflows, orders, and documentation, but analytics still needed to be addressed at the time of the build, which was short-sighted on our part. We had an EPIC analytic advisor employed by the hospital and would contact the EPIC developers for assistance. After months of back and forth, it was determined that EPIC, in its current form, could not provide the analytics that we required. The easiest and quickest fix was to create a new documentation stream that would allow information to be mined from EPIC in a more efficient and usable manner. My example perfectly describes the challenges of too much data and information making the workaround to be more time-consuming and cumbersome (Wang, Kung, & Byrd, 2018). It also provided an excellent example of poor data mining tool selection, which led to creating a time-consuming and cumbersome workaround (Yang et al., 2020).
References:
Wang, Y., Kung, L., & Byrd, T. A. (2018, Januaray). 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
Yang, J., Li, Y., Lui, Q., Li, L., Feng, A., Wang, T., . . . Lyu, J. (2020, February). Brief introduction of medical database and data mining technology in big data era. Journal of Evidence Based Medicine, 13(1), 57-69. doi:10.1111/jebm.12373
Mecuryhealthcare.com (2022) defines big data as the collection, examination, and balancing of data from all outlooks of healthcare, including clients, patients, and biological and clinical data that is more complex and needs to be measured in more extensive studies through computer programs.
The advantage of big data is vast amounts of information that are logged into a computer, generates analyzed data in a shorter period, and results are given much quicker. This also saves time for healthcare workers. Algorithms produced can make separating data much easier and quicker. Data can be bundled into sections of certain healthcare system aspects. According to Yan et al. (2022), “Technologies related to big medical data have solved some difficulties and problems that could not be solved before” (pg. 1). This can help physicians make timely decisions when it comes to a patient’s health and well-being.
One disadvantage of big data would be a breach of access. If large amounts of data from healthcare become breached, it can be very detrimental to anyone. If a breach happens, there is a possibility that information about patients, their insurance, and their finances can be leaked out. Nowadays, many healthcare systems have been hacked, and patient information has been leaked to the world. According to Shi et al. (2020), “All healthcare behaviors and their results are based on human information. The information leakage in the health care big data environment is not only the data itself but more severe than the hacker steals patients’ social security accounts and personal finance, etc., by mining the hidden information behind the data, endangering patients’ personal and property safety and even brings severe moral and ethical issues to hospitals” (pg. 428).
One way to avoid an access breach of patient confidentiality is by monitoring access and increasing security measures to avoid hacking or leaking information. A security specialist should be on the team to ensure all measures are taken to secure patient information. Secured databases should be used to ensure patient safety.
References:
What is Big Data in healthcare?: Mercury Healthcare. What is Big Data in Healthcare? | Mercury Healthcare. (n.d.). Retrieved March 29, 2023, from https://www.mercuryhealthcare.com/faq/what-is-healthcare-big-data#:~:text=Healthcare%20big%20data%20refers%20to,learning%20algorithms%20and%20data%20scientistsLinks to an external site..
Shi, M., Jiang, R., Hu, X., & Shang, J. (2020). A privacy protection method for health care big data management based on risk access control. Health Care Management Science, 23(3), 427–442. https://doi.org/10.1007/s10729-019-09490-4
Yan, L., Chen, Y., Caixia, G., Jiangying, W., Xiaoying, L., & Zhe, L. (2022). Medical Big Data and Postoperative Nursing of Fracture Patients Based on Cloud Computing. BioMed Research International, 2022, 4090235. https://doi.org/10.1155/2022/4090235
NURS 6051 Module 4 Literature Review The Use of Clinical Systems to Improve Outcomes and Efficiencies
Literature Review: The Use of Clinical Systems to Improve Outcomes and Efficiencies Islam, M. M., Poly, T. N., & Li, Y. C. J. (2018). Recent Advancement of Clinical Information Systems: Opportunities and Challenges. Yearbook of medical informatics, 27(01), 083-090
Despite substantial financial investments, the healthcare business continues to exhibit suboptimal performance, prompting widespread worries over the occurrence of many clinical mistakes in recent years. The aforementioned mistakes incur significant financial burdens on the government, mostly stemming from readmissions, compensating exams, and superfluous tests. In 2008, it was reported that clinical mistakes incurred a financial burden of around $19.5 billion in the United States, as shown by recent research conducted by Islam et al., (2018). The incurred expenditures were directly associated with superfluous medical expenses, including auxiliary services, both inpatient and outpatient care, as well as prescription medication services. Based on the findings of the research, it has been determined that around 250,000 fatalities in the United States may be attributed to clinical mistakes. These errors have emerged as prominent causes of mortality, ranking alongside cancer and heart illnesses.
Clinical information systems (CISs) play a crucial role in the healthcare delivery process. The use of clinical information systems facilitates the provision of treatment that is both grounded in empirical data and focused on the needs and preferences of the patient (Islam et al., 2018). The implementation of a clinical information system has promised to reduce clinical mistakes, hence mitigating superfluous healthcare expenditures, and enhancing global healthcare quality. The primary function of a healthcare Information System is to efficiently gather, store, analyze, and disseminate information to facilitate timely decision-making by healthcare professionals.
After perusing the aforementioned article, it has come to my attention that the implementation of a Clinical Information System (CIS) presents a significant opportunity for the healthcare sector to mitigate clinical mistakes and enhance the capabilities of healthcare workers via the provision of timely and comprehensive patient information updates. The integration of a Clinical Information System (CIS) within the healthcare business has the potential to enhance workflow and efficiency of care, hence leading to an overall improvement in the quality of healthcare services.
Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., & Suh, K. S. (2015). Clinical decision support systems for improving Diagnostic accuracy and achieving precision medicine. Journal of clinical bioinformatics, 5(1).
As the United States moves toward universal implementation of electronic health records, it is imperative that clinical settings and research laboratories work together to ensure accurate medication administration. Partners should evaluate the state of record-keeping and institute changes to standardize and institutionalize the collection of almost all data using electronic means. Improved health care at lower cost is possible via collaborative efforts between academia and industry. The current state of digital information and data is characterized by a wide variety of formats and a high degree of unstructured. Departments and charities continue to amass data without a unified management structure (Castaneda et al., 2015).
As a result, vital and cutting-edge information is difficult to get. Clinical and bioinformatics frameworks must use standard information components to produce structured comment forms enabling laboratories and centers to capture shareable information continually in order to speed up biological research and reduce the prices of medical services. The transformation of such data sets into consumable information should be a standard operating procedure. Incorporating learning environments with flexible information models and widespread use of metrics, ontologies, vocabularies, and glossaries facilitates the dissemination of new logical knowledge and clinical discoveries.
In order for physicians, non-specialist lab staff, medical caregivers, information/explore facilitators, and end-clients to be able to input information, access data, and grasp the output, I’ve learnt that accumulated knowledge must be presented in simple organizes. The effort to integrate vast volumes of information sources is an enormous challenge. This includes ‘- omics’-based atomic data, unique genomic sequences, trial data, persistent clinical phenotypes, and follow-up data.
Wang, Y., & Hajli, N. (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, 287-299.
Despite the significant benefits that big data analytics offers to the healthcare sector, existing research has failed to adequately explore its commercial value. In order to address this knowledge gap, this study proposes a significant data analysis enabled business value framework, utilizing the resource-based theory (RBT) and capability building perspective. The aim is to explain the development of big data analysis capabilities and the potential benefits that can be derived from these capabilities in the healthcare industries. In this study, the authors analyze a total of 109 case studies from 63 healthcare organizations in order to explore the causal relationships between the capabilities of big data analytics and their impact on business value. The study also investigates the role of value chains in achieving success in big data analytics (Wang & Hajli, 2017).
The findings of our research provide valuable insights for healthcare professionals on the effective integration of advanced data analysis techniques for business improvement. Furthermore, our empirical evidence serves as a foundation for further in-depth exploration of the use of big data analytics. After perusing the aforementioned article, I have acquired knowledge regarding the significance of big data analytics within the healthcare sector. The cultivation of robust big data analytics capabilities in this industry is poised to bestow numerous benefits upon healthcare providers. Specifically, it will enable the provision of patient-centered care, wherein each patient receives tailored treatment based on their individual health information.
Williams, C. (2018). Information Quality in Electronic Medical Records. Int J Clin Med Info, 1(1), 36-42.
The accessibility of health-related information is increasing throughout the healthcare sector. The provision of high-quality healthcare is a multifaceted endeavor that necessitates the effective exploitation of data contained inside electronic medical records. The acquisition of information plays a crucial role in the decision-making process inside various corporate enterprises and healthcare facilities. Nurses use the information contained inside medical computerized records to mitigate vulnerability, provide solutions, and maybe stimulate the need for further information. Clinical data may influence nursing decisions on patient well-being and enhance the effectiveness of clinical practices. As the intricacies of nursing care increase, there is a growing need for high-quality information in electronic medical records (Williams, 2018). Nurses use electronic medical records (EMRs) as a means to access a vast amount of data, hence enhancing the potential for facilitating significant transformations in healthcare services.
First and foremost, the use of new technology in treatment records serves to enhance communication. Healthcare professionals have the ability to transmit and receive medical information, which enables other professionals to effectively address the requirements of their patients. This exchange of data on the health state and wants of patients has the potential to surpass the information demands of healthcare providers. In addition, it is possible to conduct an audit of previous medications administered and evaluate patients’ responses to various forms of treatment. Healthcare providers have the opportunity to share this information with patients and their caregivers for the purpose of education and self-care management. The use of data derived from electronic medical records (EMRs) has the potential to enhance the caliber and efficiency of nursing care.
Upon perusing this article, I have acquired knowledge pertaining to the utilization of electronic medical records (EMRs) as a means to enhance healthcare quality. This is achieved through leveraging the information contained within EMRs to address uncertainties, offer resolutions, and potentially alleviate the demand for additional healthcare information.
References
Islam, M. M., Poly, T. N., & Li, Y. C. J. (2018). Recent Advancement of Clinical Information Systems Opportunities and Challenges. Yearbook of medical informatics, 27(01), 083-090
Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., & Suh, K. S. (2015). Clinical decision support systems for improving Diagnostic accuracy and achieving precision medicine. Journal of clinical bioinformatics, 5(1).
Wang, Y., & Hajli, N. (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, 287-299.
Williams, C. (2018). Information Quality in Electronic Medical Records. Int J Clin Med Info, 1(1), 36-42.