NURS-6051 Week 5: Discussion BIG DATA RISKS AND REWARDS

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Discussion Week 5: Big Data Risks and Rewards

Technology is first growing, which is why we all live in a world connected through computers, cell phones, social media, and many other platforms. The connection is unending since we carry our phones to all places we go to and are still required to log into computers while at work. As nurses, we are also obligated to help in connecting patients to database banks. Therefore, it can be deduced that we live in a world of big data that is priceless. In this sense, the data comes with both negative and positive outcomes.

Potential Benefits of using Big Data (Clinical System)

Several benefits can be derived from having big data in the clinical/healthcare system. The benefits are evident from the time that one log into the electronic health record (EHR) to the time they log out. Valuable information is entered into the computers that can be used to develop better protocols. Make better patient outcomes, help enhance patient safety, and even ease the nursing profession. Other benefits include developing more insightful diagnoses and treatment, which could result in higher quality care (Wang, Kung & Byrd, 2018). Though collected information/ patients data, trends and patterns can be analyzed hence leading to high-quality care.

The collected and stored big data can also be beneficial because through records of signs and symptoms; diseases can be detected at an early stage. By identifying the lifestyle factors that increase the risks of contracting diseases, patients can then be advised on the best way of protecting themselves against such. Populations health can also be monitored regardless of where they are, hence quickly adjusting the treatment plans. The operational, financial and clinical data can also be analyzed for resource utilization and productivity in real-time (Raghupathi & Raghupathi, 2014). These are just but part of the anticipated benefits of having big data.

Potential Challenges/Risks of Big Data

            There are particular challenges evident in using big data. The most common one is the inability to implement standardized nursing technology (SNT) fully. Should this challenge be addressed, then it can improve data analysis. The use of SNTs in nursing care helps in easy retrieval and data analysis through nurse’s clinical reasoning (Macieira et al., 2017). Through SNTs, the visibility of nursing intervention could be increased. Failing to implement its use is thus a significant challenge to the health care systems.

Besides, another significant challenge is the lack of data standardization. In such a case, healthcare systems find it hard to assess organizations’ performance and hence, cannot make well-informed decisions on what needs to be changed (Thew, 2018). According to Englebright, breaking down data silos or big data can be used to facilitate improved nursing performance (Thew, 2018). Having big data can also be risky because should the data leak to the wrong hands, such as cybercriminals, colossal damage can be experienced. It is thus demanding to have proper systems that protect the data to the fullest.

Big data risks are not limited to cyber-attacks, but internal data mishandling can also be risky enough. According to statistics, a quarter of all the cases related to healthcare data breaches resulted from unauthorized disclosure/access. More than twice the data breaches caused by internal mishandling were caused by hackers (Fox & Vaidyanathan, 2016). As learnt through the course materials, nursing informatics and big data are of great use to patients and professions, but all the benefits come with some challenges/risks.

Proposed Strategy

In my opinion, I would propose the use of stringent measures to implemented through Acts of parliament on privacy in healthcare or various professional bodies. An example of such is the Health Insurance Portability and Accountability Act (HIPPA). Data should thus be strictly kept, and only a few authorized agencies and organizations can access it. By so doing, patient data will fully be protected from the hands of ill intending people. Though maximum data security, good use can be derived, and health care systems making better the services they offer.

References

Fox, M., & Vaidyanathan, G. (2016). IMPACTS OF HEALTHCARE BIG DATA: A FRAMEWORK WITH LEGAL AND ETHICAL INSIGHTS. Issues in Information Systems, 17(3).

Macieira, T. G., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2017). Evidence of progress in making nursing practice visible using standardized nursing data: a systematic review. In AMIA Annual Symposium Proceedings (Vol. 2017, p. 1205). American Medical Informatics Association.

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 1-10.

Thew, J. (2016, April 19). Big data means big potential, challenges for nurses execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-bigpotential-challenges-nurse-execsLinks to an external site.

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.

Week 5 Discussion: Big Data Risks and Rewards

Data is a key driving force for change within an organization 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. 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). Big data can also be collected from research studies, government agencies, and laboratory results, and this has proved to be useful in managing organizational performance. One of the major rewards of using big data in the healthcare sector is a prediction of future trends and events of certain parameters which would then act as actionable information that forms the basis of evidence-based interventions (Wang et al., 2018). For example, big data may help an organization to predict future trends in the occurrence of lifestyle diseases within a particular population group. The prediction might be useful for the organization to formulate evidence-based interventions that would seek to better the quality of services delivered, focus on value-based care, and cost reduction.

However, as organizations are becoming more used to electronic documentation, they have started to collect and document data about every aspect of the care continuum until information systems are flooded with large volumes of unmanageable data. In this line, accessibility to patient data in terms of interoperability, proprietary rights, and privacy is increasingly becoming the most challenging domain of big data. Access to patient data is affected by vulnerability to breaching patient privacy considerations as protected by the HIPAA act. The risk of breaching patient privacy policy often discourages providers from documenting and/or sharing patient health information effectively. 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. Currently, interoperability is a bit compromised in many organizations (Ramadas, 2018).

I identified several strategies that would be essential in solving accessibility challenges when it comes to big data sharing. Most importantly, employing common security procedures such as encrypting sensitive data and practicing professional integrity could potentially eliminate the risk of breaching patient privacy considerations among providers while sharing big data. Additionally, Perlin (2016) identified that upgrading pre-existing information systems within health facilities will enhance the ability to share health information between providers and between health facilities efficiently. Such strategies are crucial in eliminating the risks associated with sharing and access to big data.

References

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

Ramadas, A. (2018). The usefulness and Challenges of Big Data in Healthcare. Journal of Healthcare Communications, 3(21), 1-4. doi:10.4172/2472-1654.100131

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 Risks and Rewards

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Menard Tchatchou

Walden University

NURS 6051 N

Dr. Lynne Taylor

12/27/2022

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

Big data refers to vast and complex data sets generated by various sources, such as electronic medical records, wearable devices, and social media (Abouelmehdi, Beni-Hessane & Khaloufi, 2018). When integrated into clinical systems, big data has the potential to bring numerous benefits to the healthcare industry.

Improved patient care is a potential benefit of using big data in clinical systems. Healthcare providers can gain a more comprehensive understanding of a patient’s health history, risk factors, and current condition by analyzing large amounts of data from various sources. It helps them to identify patterns and trends that may not be clear from individual patient records, enabling them to make more accurate diagnoses and treatment recommendations. For example, healthcare providers may identify early warning signs of a potential health problem, such as an increase in heart rate or blood pressure and take preventive measures to prevent the problem from worsening by analyzing data from electronic medical records and wearable devices.

Another potential benefit of using big data in clinical systems is increased efficiency. Healthcare providers can reduce the time and effort required to make informed decisions about patient care by automating the collection, organizing, and analyzing data. It improves the efficiency of the healthcare system as a whole, reducing the burden on healthcare providers and freeing up resources that can be used to treat more patients. Using big data in clinical systems can improve the quality and efficiency of healthcare and lead to better patient outcomes.

Potential Challenges or Risks of Using Big Data as Part of a Clinical System

One potential challenge or risk of using big data as part of a clinical system is the issue of data privacy and security. Clinical systems often handle sensitive patient information, including medical histories, diagnoses, and treatment plans (Katkade et al., 2018). If this data is not secured adequately, it could be accessed by unauthorized individuals, leading to privacy breaches and potential harm to patients.

Another challenge is the risk of biased or misleading conclusions from the data. Big data sets can be vast and complex, and it is essential to ensure that they are appropriately analyzed and interpreted (Katkade, Sanders & Zou, 2018). If the data is adequately cleaned, validated, and analyzed, it could lead to correct or biased conclusions. Thus, such findings affect patient care negatively.

There is the risk of technical issues or errors in handling and analyzing big data. For example, data may be lost or corrupted during transfer or storage, or there may be errors in the algorithms or statistical models used to analyze the data (Katkade, Sanders & Zou, 2018). These issues lead to incorrect or unreliable results that affect patient care adversely. It is essential to consider the potential risks and challenges of using big data in clinical systems and the appropriate safeguards to mitigate them. It includes measures such as robust data security protocols, thorough data validation and cleaning processes, and the use of qualified professionals to analyze and interpret the data.

Proposed Strategies to Mitigate the Challenges or Risks of Using the Big Data

Implementing robust data governance policies and practices mitigates the challenges or risks of using big data (Shahid, Rappon & Berta, 2019). Data governance involves establishing clear guidelines and procedures for collecting, storing, using and sharing data within an organization. It includes establishing roles and responsibilities for managing data, setting up processes for obtaining consent from individuals whose data is being collected, and implementing security measures to protect data from unauthorized access or misuse. For example, a healthcare organization might enforce data governance policies that require employees to undergo data privacy and security training and establish clear guidelines for how data can be used in research or business decision-making. Also, implementing security measures such as encryption and secure storage to protect data from being accessed or misused by unauthorized parties is a brilliant idea.

Also, thorough data audits and assessments can moderate the risk associated with big data. It involves evaluating the quality and accuracy of the collected data and assessing the potential risks and impacts of using the data for different purposes (Shahid, Rappon & Berta, 2019). Organizations can identify and address any issues or concerns with their data and ensure that they use data ethically and responsibly by conducting regular data audits and assessments. For example, a healthcare organization might conduct a data audit to ensure that patient data is collected, stored, and used by privacy laws and regulations. The organization might also assess the potential risks and benefits of using patient data for research or clinical decision-making and establish protocols for obtaining consent and protecting patient privacy. Effective data governance and regular data audits and assessments can help organizations effectively manage the risks and challenges of using big data and ensure that they use data responsibly and ethically.

Conclusion

Data sets, mass processing of information results, and high demand for bulk information increase big data risks and rewards. In Clinical fields, professionals use big data to perform diverse tasks and solve medical problems. Some benefits of big data include increased efficiency and a comprehensive understanding of a patient’s health history, risk factors, and current condition. Data privacy and security biased or misleading data, and technical errors when analyzing data are possible challenges of big data in clinical fields. The most efficient ways of mitigating these risks include robust data governance policies and thorough data audits.

References

Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: preserving security and privacy. Journal of big data, 5(1), 1-18. https://link.springer.com/article/10.1186/s40537-017-0110-7Links to an external site.

Katkade, V. B., Sanders, K. N., & Zou, K. H. (2018). Real world data: an opportunity to supplement existing evidence for the use of long-established medicines in health care decision making. Journal of multidisciplinary healthcare, 11, 295. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6033114/Links to an external site.

Shahid, N., Rappon, T., & Berta, W. (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS one, 14(2), e0212356. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212356Links to an external site.

Benefit of Big Data

With technology continuing to evolve and become a part of every aspect of our work environment, we must continue to utilize big data to improve upon processes. Big data describes the immense amount of information that we collect when taking care of our patients (Kantode et al., 2022). This data begins to collect the moment the patient enters the facility and continues to build throughout the entirety of the patient stay. Big data includes specific details about the patients that would prove difficult to analyze without the assistance of technology. The benefit to big data use in clinical systems is the potential to improve the overall quality of patient care through increasing efficiency for staff members. According to Goyal et al. (2022) the healthcare industry obtains and analyzes massive amounts of information due to the overwhelming number of consumers and the information needed from them to provide adequate care. By successfully having systems in place to adequately analyze this data, organizations can streamline processes that improve quality of care. One such method of maintaining big data is through the use of electronic health record systems. Electronic health record systems collect, store, and organize big data that can be utilized by healthcare teams to identify appropriate treatments.

Big Data Potential Challenges

            One of the major potential challenges with big data being used as part of a clinical system is that there may be issues with privacy. Healthcare organizations are all expected to maintain HIPAA compliance when handling any patient care data and mishandling this data could lead to a leak in sensitive information. Wang et al. (2018) states the consequences of mishandling big data due to poor healthcare data management can lead to increased healthcare costs as well as poor patient outcomes. Patient data breaches could be detrimental to both the patients and the healthcare organizations involved. It is important to have security systems in place to ensure data is kept safe and so that it is not taken by individuals who seek to gain from stealing data. Additionally, since big data is uploaded into clinical systems such as electronic health records, there is potential for data to be lost due to system errors. The data could be lost as a result of human error, as a result of a system error, or possibly as a result of an organization wide cyber-attack.

Strategy to Mitigate Big Data Risks

As a nurse leader, a strategy that I would use to assist in mitigating big data risks would be to ensure that there are policies in place to prevent the misuse and handling of sensitive patient data. Training would be provided to every staff member within an organization that educates them on the proper handling of sensitive information. The training would also highlight the importance of reporting any potential breaches or losses related to patient data. All staff would receive in-services on the proper usage of electronic health records as this is a common source for big data within the healthcare industry. Kantode et al. (2022) states electronic health records are a source of healthcare big data that is accessed by nearly every department within most healthcare settings. It is the organization that is responsible for ensuring staff are adequately equipped with the tools to successfully utilize this source of big data.

References

Goyal, I., Singh, A., & Saini, J. K. (2022). Big Data in Healthcare: A Review. 2022 1st International Conference on

             Informatics (ICI), Informatics (ICI), 2022 1st International Conference On, 232–234.

https://doi.org/10.1109/ICI53355.2022.9786918

Kantode, V., Sharma, R., Singh, S., Ankar, R., & Gujar, S. (2022). Big-Data in Healthcare Management and Analysis: A

Review Article. 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC),

             Electronics and Sustainable Communication Systems (ICESC), 2022 3rd International Conference On, 1139–1143.

https://doi.org/10.1109/ICESC54411.2022.9885609

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for

healthcare organizations Links to an external site. Technological Forecasting and Social Change, 126(1), 3–13.

Module 3, Week 5 Discussion: Big Data Risks and Rewards

Big data are generated every day, and not just in healthcare.  Data generated across any industry is so large and complex it is difficult to efficiently store and process.  It is important for healthcare organizations to find a way to sort through big data to use the information to improve quality of care, decrease cost of care, and improve efficiency (McGonigle & Mastrian, 2022).

Benefit of using big data

One potential benefit of using big data as part of a clinical system is that data can be used to generate new ideas and innovations that will increase productivity and build competitive advantages (Wang et al., 2018).  Rather than waiting for a specific time period for data to be collected and analyzed, big data has the potential to quickly provide information that will give us knowledge to suggest interventions or innovations (Threw, 2016).  For example, the cancer clinic I work for has recently taken a new initiative to add palliative care services for our patients.  Before beginning this new initiative, it would have been beneficial for nurse informaticists and nurse executives to use big data about palliative care for oncology patients to create the most effective program for our patients.  Information could be analyzed such as at what point after diagnosis and during treatment do patients typically need palliative care services, are in person or virtual visits most effective, and how can nursing best support a palliative care program for oncology patients.  With this data, an effective program could be created, leading to better patient outcomes.

Challenge of using big data

One potential challenge of using big data as part of a clinical system is that there is often a lack of data standardization across different healthcare organizations or even within one healthcare organization (Threw, 2016).  In addition, more than 75% of big data is unstructured data meaning it is data that resides in text files and can easily be overlooked when analyzing data (McGonigle & Mastrian, 2022).  This is especially important when looking at the example of creating a palliative care program for oncology patients.  The nature of palliative care is that it is more than just a collection of numerical data.  Every patient has a different experience and need for palliative care, therefore most of the data includes patient opinions and experiences.  Including this data in the analysis is essential to creating an effective program.

Strategy to mitigate the challenge of using big data

One challenge of using big data is to find a way to “create structured representations of the unstructured data that can make it more understandable” (Oubenali et al., 2022, p. 2).  When researching the issue of unstructured data, I found that one strategy to mitigate the challenge of using unstructured data to yield effective information is to use a Natural Language Processing (NLP) model when analyzing the data (Oubenali et al., 2022).  NLP uses word embedding methods in which they replace individual words with numerical values (Oubenali et al., 2022).  This will allow feedback from patients about their palliative care needs and experiences during their cancer treatment to be added to data and analyzed.  It will also allow provider experiences and comments to be part of the data collection.  The addition of this unstructured data will provide much more in depth data and allow nurse informaticists and nurse executives to create a more effective palliative care program for patients.

References

McGonigle, D. & Mastrian, K. (2022). Nursing informatics and the foundation of knowledge (5^th ed.). Jones & Bartlett Learning.

Oubenali, N., Messaoud, S., Filiot, A., Lamer, A., & Andrey, P. (2022).  Visualization of medical concepts represented using word embeddings: A scoping

review. BMC Medical Informatics & Decision Making, 22(1), 1-14. doi/org/10.1186/s12911-022-01822-9

Threw, 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-execsLinks to an external site.

Wang, Y., Kung, L., & Byrd, T. (2018), Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological

          Forecasting and Social Change, 126(1), 3-13.

Potential benefit of Big Data 

 The nursing industry continues to advance with the proper use of research and data. By advancing patient care, we can improve patient outcomes by reviewing and analyzing statistical data. Numerous data in ample information necessitate careful analysis due to their frequently complex content. Thanks to big data, nurses can review large amounts of data in an organized manner. Nurses can evaluate the efficacy of their patient interventions through extensive data analysis and then make the necessary adjustments to improve patient outcomes. Analytical capabilities in healthcare can find treatment patterns and extract links from vast amounts of medical data, providing a broader perspective for evidence-based clinical practice (Wang, Kung, and Byrd, 2018). Using big data daily can help nurses improve patient care interventions and outcomes. To monitor and assess patient risk and restraint behaviors, the psychiatric hospital where I work conducts extensive data analysis. We utilize electronic clinical record information to finish calculation sheet data sets on month-to-month, quarterly, and yearly premises. After that, additional care analysis is performed using these databases. 

Additionally, we compare our facility’s data to our affiliated local hospital and sister programs. Access to this data makes it possible to plan future interventions and modify patient care using big data. In our program, integrated documentation includes all patient risk assessments, including self-harm, suicidal, and homicidal ideation. We can monitor a patient’s safety as well as the program’s overall safety standards while they are enrolled in our program. We then compare these levels across all residential programs and the inpatient units at our affiliated psychiatric hospital to evaluate policy and make any necessary adjustments. 

Potential Challenge of Using Big Data 

One of the biggest obstacles to using big data in a therapeutic system would be the possibility of human error. The technology we use to analyze and document clinical data was designed to work well with the data we input. When entering our data, we must adhere to the correct procedures to avoid making mistakes in the future. In the data review I previously described, the risk is currently only recorded by personnel in the nursing department. We supervise the inclusion of patient risk assessments in the subsequent data reviews. Our monthly assessments do not automatically include restraints and risk assessments that a nurse records in her shift documentation. We review each chart, and this information is passed on to the following link in our review chain. When transferring this documentation to a spreadsheet, a nurse could easily overlook it or enter it incorrectly. Every piece of documentation needs to be clear and precise. Although incorporating big data into nursing practice may result in positive outcomes, we must ensure that the available technologies are utilized appropriately. 

Plan to deal with This Challenge 

There are several ways to reduce the likelihood of human error when working with big data. To ensure the accuracy of the data we are analyzing, we have enhanced our facility’s data research method with several additional features. There is a possibility of human error beginning with the entry of each shift note and continuing throughout the entire review process. The best option would be a database that automatically populates with information from each recorded shift note. Although that might be a solution in the future, we must be careful to avoid mistakes now. When working with a lot of data, it is essential to ensure that it is entered correctly and consistently. The nurses are manually entering our risk data in our designated areas. In these facilities, each nurse receives ongoing instructions on accurately recording risk information and entering it into patient safety databases. We have incorporated evidence-based risk evaluations into the selected paperwork to guarantee that the risk conditions meet standard hospital requirements. McGonigle and Mastrian (2017) claim that by creating a single, comprehensive database, healthcare organizations can enhance interprofessional relationships while still adhering to privacy regulations. In all nurse meetings and communications, we emphasize the significance of accurate nursing record-keeping. Additionally, we have discussed and implemented strategies to lessen the likelihood of errors occurring at that time. By providing nurses with quiet areas to record their notes and then review the data, we have been able to evaluate and implement these data review strategies in our practice. 

 References 

 McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning. 

Thew, J. (2016). Big data means significant potential challenges for nurse execs. Health Leaders Media. Retrieved https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execsLinks to an external site. 

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. 

BIG DATA RISKS AND REWARD

“Big data” refers to the enormous data sets that produce substantial facts as compared to smaller groups of the same data output that are not integrated. An individual’s first activity upon waking up is often data entry. They’ve finished reading updates from friends and relatives on social media and turned off their phone’s alarm. Today, data entry is essential to almost every aspect of our lives. These things—shopping, social media, and smart devices—create data and statistics. The gathered information will be used to advance healthcare technologies and delivery systems. It is vital to improve and grow the use of big data in healthcare because it changes how doctors and other multidisciplinary teams care for patients. The application of big data in the medical field has proven revolutionary. According to Thew (2016, April l9), the forms of data analytics that have been proposed have been constrained by an inability to grasp how this data interacts throughout the system.

BENEFITS OF BIG DATA IN CLINICAL SYSTEM

Some doctors are known to have unreadable handwriting. Due to the widespread use of cutting-edge technology in the medical field, medication errors have gone down. As one way that big data has made the clinical system better, the number of wrong prescriptions has gone down. According to the Center for Pharmaceutical Evaluation and Research (n.d.), the FDA receives more than 100,000 reports annually connected with a suspected medication error; reducing these errors improves results and saves lives. Every year, approximately 7 million people in the United States are affected by these errors. As a result, 7,000 people lose their lives, according to the Center for Drug Evaluation and Research (n.d.).

STRATEGIES TO EFFECTIVELY ALLEVIATE CHALLENGE.

The greatest worry is a breach in security. Around 25 million patient records may have been compromised in the first half of 2019 (Davis, 2019). Because of help from outside sources. A possible tactic is restricting patient access to the organization’s in-house interdisciplinary team. No outdoor group should be privy to a patient’s medical records because of the sensitive nature of their information. Personal information misuse can have serious repercussions. It would seem harmless to give experts access to patient’s records, but what about giving the entire group of specialists working in practice that kind of access? That would cause trouble on the inside. If you want to get this fixed, you need to hire a big data computing business that knows what they’re doing and has the security measures in place to protect your data. Employees should be reminded to take safety measures like changing their passwords often and only at their workstations, deleting any suspicious emails they get, and constantly logging out when they’re done.

References

Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.

Thew, J. (2016, April 19) Big Data Means Big Potential, Challenges for Nurse Execs. Health Leaders. Retrieved from: https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execsLinks to an external site..

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

Main Post

Big Data

Big data is continuing to grow exponentially.  It is aggregated by the internet and social networks, text messages media files and web searches, transactions processing systems, customer databases, documents etc. In the clinical setting Big Data is associated with digitalizing medical records, improving care by allowing data tracking, and lowering cost. With Big Data, patients who visits the hospital on a regular basis can be identified quickly and easily classified based on their health condition which saves time and money because they can be treated rather quickly and discharged.

One Challenge of Big Data is lack of effective data governance procedures.  The biggest obstacle for healthcare organizations is capturing data.  Data requires specifics measures to be used efficient such as precision, formatted correctly, must be clean to be used across various healthcare system.  Implementation costs is also another challenge.  Costs of rise in use of ancillary services such as laboratory tests and x-rays, clinical chemistry tests and other little-ticket technology, cost of computers for units and departments, IT personnel/s, training, and education programs for healthcare professionals.

One strategy to streamline cutting costs while improving health is to have all the relevant stakeholders collaborate and adapt the design and performance of their systems by building the technological infrastructure to house and converge the massive volume of healthcare data and invest in the human capital to guide citizens into their new frontier of human health and well-being.

Reference

Pastorino, R., De Vito, C., […], & Boccia, S. (2019) Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. Eur J Public Health. (Suppl 3): 23-27 doi:10.1093/eurpub/ckz168

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of

knowledge (5th ed.). Jones & Bartlett Learning.

Glassman, K. S. (2017). Using data in nursing practice Links to an external site.Links to an external site.. 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 organizationsLinks to an external site.Links to an external site.. Technological Forecasting and Social Change, 126(1), 3–13.

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

Initial Post – Big Data Risk and Rewards

Big data is large and complex data that is so much so that it is difficult to process using traditional data processing

methods (McGonigle & Mastrain, 2022).  In healthcare, for instance, the amount of data collected from patients even in one

day is enormous. This data comes from multiple sources such as physicians, nurses, laboratories, radiology, other clinicians,

and other facilities.  Unfortunately, over 75% of the data collected in most organizations are unstructured and mostly

overlooked because they are usually in text files rather than databases (McGonigle & Mastrain, 2022).

The ability to sort through such files requires good data mining software.  Data mining is a process that uses a specific

type of software to sort data for patterns and determine relationships. Six Sigma and Lean are data mining tools that can

eliminate defects, avoid waste, or assess quality control issues. The big data revolution challenged healthcare professionals to

assess datasets using advanced technology for seamless collection and analysis of information from numerous sources

(McGonigle & Mastrain, 2022).

Potential Benefit of Using Big Data

There are many potential benefits of using big data.  There are managerial, strategic, operational/clinical, and IT benefits.

Clinical systems benefits include improvement in the quality and accuracy of clinical decisions; the ability to process large

numbers of health records in seconds; immediate access to clinical data for analysis; decrease in the time of the diagnostic

test; reduction in surgery-related hospitalizations; and exploration of new research avenues (Wang et al., 2019).  Big data

allows clinicians to view patient trends and make decisions accordingly.  Also, the information to make the decision is easily

accessible.

Potential Challenge or Risk Using Big Data and Possible Solution

A potential challenge of using big data in clinical systems is processing information without human supervision, which

might lead to inaccurate conclusions and lead to a patient being wrongly diagnosed and subsequently treated for the wrong

diagnosis, which could be detrimental and fatal if the error is not recognized early. A possible solution or strategy would be to

create a big data analytics system that is simple, convenient, and transparent enough to be applied to actual patient cases

(Mehta & Pandit, 2018). It should be a simple, relatively straightforward system that is easy to assimilate.  A system that is too

complicated to understand and use in the long run becomes obsolete and not cost-effective.

References

Mehta, N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International Journal of

medical informatics, 114, 57-65.

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, 3-13.

              Big Data is a field of study that involves data management and analytics—defined as high volume, high velocity, and a great variety of information assets requiring new processing forms to enable enhanced decision-making (Tech America,2022). Big data’s increase in healthcare has developed meaningful benefits for clients and medical professionals. It refers to vast and complex data sets generated by various sources, such as electronic medical records, wearable devices, and social media (Abouelmehdi, Beni-Hessane & Khaloufi, 2018). The health field, especially in a clinical setting, is not immune to growth and has quickly embraced the use of big data. We see the evidence with that our EHR system. Thew (2016), reminds us to ensure big data is used to influence outcomes that are meaningful to the nursing profession.

             One potential benefit of using big data as part of a clinical system is that it has created more accessibility. Telemedicine and using big data technology intelligent devices have led to remodeling and revolutionizing the entire healthcare industry. Clinical services can be delivered by way of technology, increasing patient outcomes, and reducing unnecessary ER visits by education on diagnosis, facilitating consultations to provide personalized treatment plans, or even monitoring patients remotely. According to Tulane University (2021), Big data allows doctors to serve patients in rural areas and other locations where a robust medical infrastructure may not exist. For example, patients can use smart home devices to communicate with medical providers. Big data approach is essential in improving evidenced-based healthcare decisions and patient outcomes. (Wang et al., 2018).

Despite the great benefits, we can still identify the cons. One potential challenge or risk of using big data as part of a clinical system is a breach of privacy through poor data security. There is a high risk of unauthorized users like hackers, cybercriminals, or persons outside of one’s treatment team accessing confidential information from electronic health recording system.

            One strategy that may effectively mitigate these challenges is cloud Data protection agency. Cloud data protection is securing a company’s data in a cloud environment, wherever that data is, whether at rest or in motion, and whether it is managed internally by the company or externally by a third party (PrismaCloud, n.d). Using cloud data can continuously and actively work to identify and block security breaches, suspicious user behavior, cybercriminals, or hackers.

                                                                      References

Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: preserving security and privacy.

 https://journalofbigdata.springeropen.com/articles/10.1186/s40537-017-0110-7

PrismaCloud, (n.d) What is Cloud Data Protection? Palo Alto Networks.

https://www.paloaltonetworks.com/cyberpedia/what-is-cloud-data-protection#:~:text=Cloud%20data%20protection%20is%20the%20practice%20of%20securing,the%20company%20or%20externally%20by%20a%20third%20party.

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs Links to an external site.

https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Tech America (2022). What is Big Data and why is it important?, Tech America.

https://www.techamerica.org/what-is-big-data-and-why-is-it-important/#:~:text=Big%20Data%20is%20a%20term%20that%20has%20gained,requiring%20new%20processing%20forms%20to%20enable%20enhanced%20decision-making.

Tulane University, (2021). Big data in health care and patient outcomes, School of Public Health.

https://publichealth.tulane.edu/blog/big-data-in-healthcare/

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations Links to an external site.. Technological Forecasting and Social Change, 126(1), 3–13.

Main Post

The definition of “big data” is data that has different variations of information collected and grouped, with three sections: volume or amount, velocity, and variety (What Is Big Data? | Oracle, n.d.). Big data can help organize and coordinate incoming data, regardless of type or difference. Three types of big data are structured, unstructured, and semi-structured. (Daniels, 2022). Structured is making the data organized and easier to understand, unstructured is out of order, and semi-structured is a mix of both, sometimes organized and sometimes uncoordinated.

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

A clinical system is a technology system that helps healthcare providers with patient data and patient care (eHealth Network, 2019), and its users could benefit from using big data because it can help with the accumulation of patient data, all the variations, types and the speed or rate that is entered into any system or database. The data can be calculated and sorted using big data in a clinical system. Big data can help push and assist an organization in moving forward faster than sitting around and waiting for things to happen and risking being left using outdated things (Thew, 2016). In the clinical setting, big data can be used to obtain data for research purposes (Olivera et al., 2019). When conducting research, big data can help organize and maintain data collected no matter the state it is in or the speed it is submitted. Researchers can use big data to insert their findings from all over the world, in different varieties and speeds, to be stored indefinitely (Scientific Research and Big Data (Stanford Encyclopedia of Philosophy), 2020).

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

One potential challenge of using big data could be not having the right capacity to manage or maintain the information added to systems using big data. With big data, the name almost immediately tells you the main part of it; it accumulates patient data together. With this, you would need a system with a large amount of storage or capacity to hold patient data for an indefinite amount of time. You would also need a way to ensure that confidentiality is maintained, keeping all patient data and research private from public usage or viewing (Bruno, 2019). Another potential risk is breaches of data. There could be data leakage or break-ins that can cause patient data to be exposed to those who are not permitted (Daniels, 2022). Let’s say an important patient comes to a facility, and their health records and data need to stay confidential. Big data can help with this, but it runs the risk of the system getting hacked and the information getting stolen, exposing all the conditions the person has to the public.

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

The addition of cloud computing to physical servers would address both issues of storage and security. Most current cloud computing services (such as Google Cloud and Microsoft Azure) have 24/7 accessibility and are governed by privacy regulations. They also give the end user the option of public, private, or even hybrid cloud services. (indeed.com – n.d.).

References

Bruno, R. (2019, April 4). Big Data Storage Challenges. Big Data HPC | Raid Incorporated.

https://www.raidinc.com/2015/07/big-data- storage-challenges/Links to an external site.

Daniels, N. (2022, December 25). Big Data and Privacy: What is it and What are the Risks? VPNoverview.com.

https://vpnoverview.com/privacy/anonymous-browsing/big-data-and-privacy/Links to an external site.

eHealth Network. (2019, July 2). Clinical Information Systems: An Overview – Elets eHealth. eHealth Magazine.

Retrieved from https://ehealth.eletsonline.com/2008/07/clinical-information-systems-an-overview-dr-pramod-d-jacob-consultant-clinical-information-systems-emr/Links to an external site.

Indeed Editorial Team. (2021). 9 Types of Cloud Computing (With Definition and Tips).

https://www.indeed.com/career-advice/career-development/what-is-cloud-computingLinks to an external site.

Olivera, P., Danese, S., Jay, N., Natoli, G., & Peyrin-Biroulet, L. (2019). Big data in IBD: a look into the future. Nature Reviews Gastroenterology and

Hepatology

https://moh-it.pure.elsevier.com/en/publications/big-data-in-ibd-a-look-into-the-futureLinks to an external site.

Oracle. (2022). What Is Big Data? | Oracle. Oracle | Cloud Applications and Cloud Platform. (n.d.).

Retrieved from https://www.oracle.com/big-data/what-is-big-data/Links to an external site..

Stanford Encyclopedia of Philosophy – Scientific Research and Big Data – (2020, May 29).

Retrieved from https://plato.stanford.edu/entries/science-big-data/Links to an external site.

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. Links to an external site

Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execsLinks to an external site.

Module 3 Week 5 Discussion:

Potential benefits and risks associated with the use of big data in the healthcare system.

In healthcare, big data can be described as a large set of electronic health data sets that is complex and too large to manage with traditional hardware and software. The data set cannot be easily managed with common data or traditional management methods and tools (Keenan, 2014).

Potential Benefits of using Big Data

In the healthcare system, there are various benefits of big data, such as the ability to keep patients safe and healthy, reduce hospital costs and expand diagnostic services (Adibuzzaman, et al., 2018). An example of how big data enables healthcare professionals to observe, analyze, and interpret trends that influence patient outcome, is the electronic health record (EHR) documentation of the blood sugars of diabetic patients. The EHR organizes the blood glucose numbers, and patterns with which the provider can observe if the patient blood glucose is controlled or not controlled and implement appropriate measures to normalize the patient’s blood sugar in a timely manner. The incorporation of this data is beneficial to the interdisciplinary team involved in the care of the patient, the physician, the nurse, the dietician, the endocrinologist, and the patient. Patients can also communicate with the healthcare professional using an app on their phone which allows easy access to the healthcare system. Big data, allows healthcare professionals to monitor patients closely and intervene timely, which thereby reduces frequent re-admissions hospital stays, and healthcare

Big Data Risks

There are great benefits in using big data to improve patient outcomes in the healthcare system, but there are also risks. One of the risks observed in big data usage is the compromise of patient confidentiality, and possible health insurance portability and accountability act (HIPAA) violations. Gaining access to patient information is getting easier, due to the ever-increase in advanced technology. Though clinicians access this information, with a protected password, and other safety measures, there are still cases of patient information getting compromised.

Possible Strategy

A possible strategy to prevent the risk of compromising patient information and HIPAA violations is the deidentification of protected health information for secondary use. This can be done by removing features that identify patients such as photos, names, zip codes, telephone numbers, social security numbers, and addresses before sharing the healthcare data with researchers and other secondary personnel (Kayalp, 2018). This is an essential process that can maintain patient privacy, though the process is tedious and time-consuming.

References

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

Kayaalp M. (2018). Patient privacy in the era of big data. Balkan medical journal, 35(1), 8–17. https://doi.org/10.4274/balkanmedj.2017.0966

Keenan G. M. (2014). Big data in health care: An urgent mandate to change nursing EHRs!. On-line journal of nursing informatics, 18(1), http://www.himss.org/ResourceLibrary/GenResourceDetail.aspx?ItemNumber=28968.

    Big data is a term used to describe large and complex datasets that are difficult to process using traditional methods. It is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications (Elhoseny et al., 2018). Big data enables businesses to identify trends and correlations in their data, allowing them to make better decisions, optimize operations, and gain a competitive advantage. Big data is used to power predictive analytics, machine learning, artificial intelligence, and more. By leveraging the power of big data, organizations can improve their efficiency, increase their profitability, and gain a competitive edge.

The Benefit of Big Data

One potential benefit of using big data as part of a clinical system is the potential to improve patient outcomes. By collecting and analyzing data from a wide range of sources, such as electronic health records, research studies, and patient surveys, healthcare providers identify patterns in patient health and develop personalized treatments tailored to individual patient needs (Dash et al., 2019). This leads to improved diagnostic accuracy and better overall patient care. Glassman (2017) has pointed out that using big data helps reduce healthcare costs by detecting potential problems early and providing more efficient treatments. The use of big data also has the benefit of reducing medical errors and increasing the accuracy of medical research. Thew (2016) has explained that by harnessing the power of big data, healthcare providers gain insights that help them provide better patient care.

Risk of Using Big Data

A potential challenge or risk of using big data that researchers in healthcare have pointed out is the potential for privacy and security breaches (Thew, 2016). As data is collected from different sources, it is vital to ensure that it is secure and that patient information is kept confidential. It is essential to consider the data’s accuracy and its potential implications for clinical decision-making. If the data is not correctly managed and monitored, it could lead to inaccurate results and incorrect decisions. Furthermore, using big data can lead to algorithmic bias, which, in effect, could lead to unfair treatment of specific patient groups (Glassman, 2017). From this, healthcare providers need to ensure the privacy and security of the data and consider the potential implications of using big data for clinical decision-making.

Mitigation Strategy

Several strategies could be used to mitigate the identified risks of big data use. One strategy that effectively mitigates the challenges or risks of using big data is using secure data management systems (Dash et al., 2019). A secure data management system enables healthcare providers to ensure that patient data is kept confidential and secure. What is more, data management systems are used to monitor the data’s accuracy and ensure that it is used appropriately. Furthermore, data management systems are used to detect algorithmic bias and identify potential data patterns that could lead to unfair treatment of specific patient groups. For example, organizations such as the Mayo Clinic have implemented secure data management systems to ensure the privacy and accuracy of their data. The Mayo Clinic has implemented a secure data management system to ensure the privacy and accuracy of its data. This system monitors the data’s accuracy and helps detect any algorithmic bias.  In short, the secure data management system helps ensure that patient data is kept confidential while preventing potential algorithmic bias problems.

In conclusion, big data has the potential to improve patient outcomes and reduce healthcare costs. However, there are challenges and risks associated with using big data, such as privacy and security breaches and algorithmic bias. To mitigate these challenges, healthcare providers must implement secure data management systems and algorithms for detecting and mitigating algorithmic bias.

References

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. https://doi.org/10.1186/s40537-019-0217-0Links to an external site.

Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. (2018). A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems, 86, 1383-1394. https://doi.org/10.1016/j.future.2018.03.005Links to an external site.

Glassman, K. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45-47. https://www.myamericannurse.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf

Thew, J. (2016). Big data means big potential, challenges for nurse execs. https://www.healthleadersmedia.com/nursing/big-data-means-big-Links to an external site. potential-challenges-nurse-execs

Main Post

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

Although broadly used, there is no unique definition for big data, but generally, big data is describing a volume of data that beyond human being’s comprehension and it is impossible managed by standard computing systems. Beside extremely high volume, big data also has the other characters: 1. velocity, data are generated or collected in very rapid rate; 2. variety, data are collected from multiple places or points simultaneously, and when the data are retrieved, they can be retrieved in different forms. 3. veracity, data’s completeness is uncertain; and 4. Value, data collection should be guided by certain purpose and should always be considered with any data-related project (Carter-Templeton, et al., 2021).

Big data can be used for analysis and provide objective support facilitating informed decision-making about the diagnosis and treatment of patients, prevention of diseases (Batco & Slezak, 2022). But more important application of big data is that they can be used in the healthcare sector to predict the trends and provide relative intervention options (Wang et al., 2018), which is essential for healthcare plans in a big scale. For example, big data collective from different sources stored a massive amount of information about people infected COVID-19 virus around the world, scientists then analyzed the data to understand the nature of the COVID-19 virus. This big data analytics on one hand supported the rapid development of COVID-19 vaccines and advanced medications to treat patients, and on other hand predicted the spread of COVID-19 virus (Hallem et al., 2020). Big data sharing will allow healthcare information to be processed much more rapidly than in the past during other pandemics such as Spanish flu in 1918. In the hospital system I’m working in, the management used the big data to predict the trend of COVID-19 infection and distributed resources to dealing effectively with the tread.

Potential challenges or risks of using big data as part of a clinical system

One of the biggest potential challenges or risks of using big data is that the data do not fully capture temporal and process information. Most of the time, clinical data are collected and captured in different systems, for example, even in a same facility. Each of system collects clinic data with own specific purpose and at times not well integrated (Adibuzzama, et al., 2018). For example, when a COVID-19 patient is care in our hospital, an EHR is purposefully designed for documenting patient care and facilitating insurance company billing for this patient, while the pharmacy records are designed for managing inventory. The systems together are not designed to capture the temporal and process information which is essential for understanding COVID-19 progression, severeness, the therapeutic effectiveness, and patient result (Adibuzzama, et al., 2018).

Proposed strategies to mitigate the challenges or risks of using the big data

As the analysis above, too much not reverent information, too much data collection sources are a big challenge for application of big data. The best proposed strategy to mitigate the challenge or risk of using the big data is to scale down their data when the organization accessing them (Adibuzzama, et al., 2018). Which means that the organization should identify what data are important and relevant to the purpose of the usage of the data and get rid of the not relevant data. Through this process, quantitative of stored data reduced but quality of data achieved, healthcare quality improved.

References

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

Batko, K., & Ślęzak, A. (2022). The use of Big Data Analytics in healthcare. Journal of big data, 9(1), 3. https://doi.org/10.1186/s40537-021-00553-4

Carter-Templeton, H., Nicoll, L.H., Wrigley, J., Wyatt, T.H., (September 30, 2021) “Big Data in Nursing: A Bibliometric Analysis” OJIN: The Online Journal of Issues in Nursing Vol. 26, No. 3, Manuscript 2.

Haleem, A., Javaid, M., Khan, I. H., & Vaishya, R. (2020). Significant Applications of Big Data in COVID-19 Pandemic. Indian journal of orthopaedics, 54(4), 526–528. https://doi.org/10.1007/s43465-020-00129-z

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

Week 5 Discussion.

Information is fundamental to a better organization and new developments. The more information available, the better we can organize and deliver the best outcomes. That is why data collection is a crucial part for every organization. Data can be used to predict current trends of certain criterion and future events. Individuals and organizations daily activities are inundated with tons of data generated from social activities, science, work, health, etc. The present situation can be compared to a data flooding. Technological advances have helped us in generating more and more data, even to a level where it has become unmanageable with the current available technologies. This has led to the creation of the term ‘big data’ to describe data that is large and unmanageable. Like every other industry, healthcare organizations are producing data at a tremendous rate that presents many advantages and challenges at the same time. (Dash, et al, 2019).

One potential benefit of using big data as part of a clinical system

At this time, the health care industry is yet to appreciate the immense benefits that can be amassed from the analytic of big data. One of the benefits of big data is its use in predictive capability. “Predictive Capability” uses a set of sophisticated statistical data to build patterns and forecast future events. Predictive capability highlights future trends and investigation of new perceptions through distilling of information from large store of data. This system assists managers to reference current and historical data to generate context-aware recommendations that enable them to accurately predict future events and trends (Wang, et al., 2018). Being able to use data to predict future outcomes is beneficial as it will help in the quick treatment for a patient that presents with the same ailment where there is data of successful treatment with the model.

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

One major challenge in the EHRs the lack of standardization of the systems. All of a patient’s record can only be seen when all the systems used interface. The lack of standardization of data is challenging as the analytic person has difficulty assessing the performance of the organization or a certain unit in order to make an informed decision (thew, 2016).

Data security presents another problem for big data. Health systems must improve their information security to protect their database from breach. Hacking, cybertheft, or data phishing can result in patients’ data stolen and used for criminal activities. The risk of data compromise can be limited by using cloud technology for storing data. Cloud storages are often highly protected by healthcare organizations and the cloud service provider (Wang et al., 2018).

To safeguard the risk of big data, institutions must safeguard their data. During my time working at a county jail facility, there were multiple attempts by hackers to get into the system and change the release dates of some of the people locked up in the jail. The attempt was discovered, and the department decided to contract their record security to an outside cyber security firm.

References

Dash, S., Shakyawar, S., Sharna, M., Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. J Big Data 6, 54 (2019). https://doi.org/10.1186/s40537-019-0217-0Links to an external site.. https://link.springer.com/article/10.1186/s40537-019-0217-0#citeasLinks to an external site.

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs Links to an external site.Links to an external site.. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Data is collected worldwide in every profession/industry; in the medical field, this collection of data can be analyzed to discover trends and other meaningful data to improve healthcare (McGonigle & Mastrian, 2022). One potential benefit of using big data is the ability to use applications or mobile devices to monitor patients and detect risk factors for disease; this data can be analyzed in real-time and prompt users to make changes to reduce health risk, thus improving health outcomes (Pastorino et al., 2019).

One potential challenge of using big data in the clinical setting is privacy; big data is said to have access to almost everything, including private health information, private recordings, and even social media (Awrahman et al., 2022). This is concerning as, time and time again, we have seen cyber-attacks on information systems leading to personal information being stolen or leaked to the public. If this were to occur in a clinical setting, it could have devasting effects on a patient’s well-being.

I would propose increased monitoring and surveilling of this data to prevent such incidences from occurring; in addition, I would push for the development of systems that only collect pertinent information regarding our personal health history and avoid using social media platforms or similar as a means to collect data as they can be inaccurate.

References

Awrahman, B. J., Aziz Fatah, C., & Hamaamin, M. Y. (2022). A Review of the Role and Challenges of Big Data in Healthcare Informatics and Analytics. Computational Intelligence and Neuroscience, 2022, 5317760. https://doi.org/10.1155/2022/5317760Links to an external site.

McGonigle, D. & Mastrian, K. (2022). Nursing informatics and the foundation of knowledge (5^th ed.). Jones & Bartlett Learning.

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/ckz168Links to an external site.

Main Post – Erica Schulte

Benefits of Using Big Data

Utilizing big data as part of a clinical system has many benefits.  These can include the use of Electronic Health Records and other tools for patient monitoring as well as communication.  In a video communication focusing on the benefits of big data in diabetes management, practicing physician Grant Shevchik, (Walden University, 2018) discusses some of the tools that are being used in his field.  Collecting the progression of data from a patient is the one area in particular that can be extremely beneficial.  While an initial view of a patient’s status could be deemed negative, understanding that patient’s progression up to that point may change the viewpoint of a provider.  This progression is most easily understood through the data collected within an Electronic Health Record.  In addition, the ability to communicate through available technology, specifically smart phones, with the patients has hallowed for the ability to quickly contact individual patients and also efficiently distribute mass information to multiple patients.

Challenges of Using Big Data

While the future of big data within healthcare and clinical systems is extremely bright, there are also challenges that need to be considered.  These challenges include security, ownership, storage, and even what data should be used (Shanthagiri, 2014).  To focus on one of these challenges in particular, the decision of the data to focus can be a critical element.  There is such a vast amount of data and information that is produced in today’s world, the term ‘drowning in data’ can be a real issue (Thew, 2016).  This creates an issue of nurses and nurse leaders abandoning certain data and instead relying on topics that they feel most strongly about.

Strategies to Overcome the Challenges of Big Data

For myself, currently working in the healthcare insurance field, utilizing informatics and big data is necessary to all aspects of my work.  When I look at the ‘Big Data Checklist’ discussed in Jennifer Thew’s article, (Thew, 2016), the steps really identify and are apparent within the culture that has been established.  The elements that are described within the checklist includes creating a culture that thrives on data.  This is necessary in my current field as the data collected is what we rely on to make an educated decision.  The next step to the checklist includes creating big data competencies which is necessary to maintain efficiencies, this is a requirement within my position.  Finally, creating an infrastructure with the ability to support big data is necessary for many reasons.  Ultimately, if the tools are not available to support the big data efforts, then the efforts will not be successful.

References

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs Links to an external site.Links to an external site.. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Walden University, LLC. (Producer). (2018). Health Informatics and Population Health: Analyzing Data for Clinical Success [Video file]. Baltimore, MD: Author

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

The potential benefit of using big data as part of a clinical system

Big data has revolutionized the world of healthcare and medical research. By exploiting the potential of big data, clinicians have gained insight into diagnosing and treating diseases in unprecedented ways. One of the major advantages of using big data in a clinical system is that it allows doctors to review vast amounts of historical information quickly and accurately. This can enable greater accuracy when making diagnoses or prescribing medications for patients, as patterns can be identified across many individuals and circumstances(Wang et al., 2018).

Additionally, using big data can help reduce the amount of time spent researching various treatments and allow doctors to understand better how certain treatments might affect different demographics or conditions. Additionally, by utilizing machine learning algorithms, clinicians can compare new information against historical trends and identify any correlations that may result in improved care delivery practices.

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

Big data is increasingly being used in clinical systems to improve the accuracy of diagnoses and provide better patient care. However, its use comes with several potential challenges and risks. To begin with, data privacy is an important issue to consider when using big data due to the sensitivity of clinical information. If proper safeguards are not implemented, medical records could be exposed or stolen, leading to serious implications for patients and their providers. Personal health information (PHI) must be kept secure, as a data breach could put patients at serious risk. Large volumes of PHI can be collected and stored with big data, increasing the risk of a data breach. It is also important to consider how data is collected, stored, and shared. If data is not properly managed, it can fall into the wrong hands, causing a breach of sensitive information.

Additionally, as large amounts of data are collected from an ever-growing range of sources, and it can be difficult to ensure that the information is accurate and reliable. Inaccurate or incomplete datasets can lead to incorrect patient treatment decisions, which could seriously affect their health (Dash et al., 2019).

Mitigation Strategy

Implementing strong security measures is one strategy for mitigating the risks and challenges associated with using big data in a clinical system. It is essential that all medical data is encrypted and stored securely using methods such as tokenization and authentication. Additionally, access to the data should be limited to those with the appropriate credentials. Doctors should also be trained in data security best practices, such as avoiding the use of weak passwords or storing patient data on unsecured devices. Furthermore, organizations should consider implementing a data breach response plan, which outlines the steps to be taken if a breach is detected. This should include informing the appropriate authorities and supporting affected patients. Finally, organizations should ensure that their data is collected and stored in a way that complies with all relevant regulations, such as HIPAA (Thompson, 2018). This will ensure that patient data remains protected and that organizations remain compliant.

                           References

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.

Thompson, E. C. (2018). Cybersecurity incident response: How to contain, eradicate, and recover from incidents. Apress.

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.