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Discussion Week 5: Big Data Risks and Rewards

Technology is rapidly advancing, connecting us through devices like computers, cell phones, and social media platforms. This connectivity is constant, as we carry our phones everywhere and often use computers at work, including as nurses when we help connect patients to databases. This shows that we live in a world filled with valuable big data. This data brings both positive and negative outcomes.

Benefits of Using Big Data in Healthcare Systems:

Big data offers numerous advantages in clinical and healthcare settings. From the moment one logs into the electronic health record (EHR), valuable information is entered into computers, which can be used to develop better protocols, improve patient outcomes, enhance safety, and streamline nursing duties. Other benefits include more insightful diagnoses and treatments, resulting in higher-quality care. Through the analysis of trends and patterns in collected patient data, high-quality care can be achieved.

Furthermore, stored big data can be beneficial in detecting diseases at early stages through records of signs and symptoms. By identifying lifestyle factors that increase disease risks, patients can be advised on how to protect themselves. Population health can also be monitored regardless of location, allowing for quick adjustments to treatment plans. Operational, financial, and clinical data can be analyzed for resource utilization and productivity in real-time. These are just a few of the expected benefits of big data.

Challenges and Risks of Big Data:

There are specific challenges associated with using big data, such as the incomplete implementation of standardized nursing technology (SNT), which hinders data analysis. SNTs in nursing care facilitate easy retrieval and analysis of data through nurses’ clinical reasoning. Failing to fully implement SNTs poses a significant challenge to healthcare systems. Another major challenge is the lack of data standardization, making it difficult to assess organizational performance and make informed decisions.

Additionally, big data poses risks such as cyber-attacks and internal data mishandling. Unauthorized disclosure or access to data can result in significant damage. Statistics show that a quarter of healthcare data breaches are due to unauthorized access, with hackers causing more breaches than internal mishandling. Nursing informatics and big data offer great benefits to patients and professionals, but these benefits come with challenges and risks.

Proposed Strategy:

To address these challenges and risks, stringent measures should be implemented through legislation on healthcare privacy, such as the Health Insurance Portability and Accountability Act (HIPPA). Data should be securely stored, and access should be limited to authorized agencies and organizations. By ensuring maximum data security, healthcare systems can derive maximum benefits and improve 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.

Benefit of Big Data

With technology advancing and becoming an integral part of our work environment, leveraging big data is crucial for process improvement. Big data refers to the vast amount of information collected while caring for patients. This data accumulates from the moment a patient arrives at the facility and continues throughout their stay, containing specific details that would be challenging to analyze without technology. Utilizing big data in clinical systems offers the potential to enhance overall patient care by increasing staff efficiency. Due to the large number of consumers and the extensive information required for adequate care provision, the healthcare industry obtains and analyzes massive amounts of data. By effectively analyzing this data, organizations can streamline processes to improve care quality. Electronic health record systems play a significant role in maintaining big data, collecting, storing, and organizing information that healthcare teams can use to identify appropriate treatments.

Challenges of Big Data:

One major challenge of incorporating big data into clinical systems is privacy concerns. Healthcare organizations must maintain HIPAA compliance when handling patient care data, as mishandling can lead to breaches and the leakage of sensitive information. Mishandling big data can result in increased healthcare costs and poor patient outcomes. Data breaches can be detrimental to both patients and healthcare organizations. Implementing robust security systems is essential to safeguard data and prevent theft. Additionally, since big data is uploaded into clinical systems like electronic health records, there’s a risk of data loss due to system errors, human error, or cyber-attacks.

Mitigation Strategy:

As a nurse leader, a strategy to mitigate big data risks involves implementing policies to prevent the misuse of sensitive patient data and providing comprehensive training to all staff members. This training would educate staff on proper data handling and emphasize the importance of reporting potential breaches or losses. In-services on the proper usage of electronic health records would be provided, as they are a common source of healthcare big data accessed by various departments. The organization is responsible for ensuring that staff are equipped with the necessary tools to effectively 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.

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

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.

    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.