Main Post- 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.
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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 Big Data Risks and Rewards. 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. Big Data Risks and Rewards 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.
To Prepare:
- Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
- Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.
BY DAY 3 OF WEEK 5
Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples.
BY DAY 6 OF WEEK 5
Respond to at least two of your colleagues* on two different days, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.
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.
Big Data Risks and Rewards sample 3
Big Data Risks and Rewards sample 4
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 (5th 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. Big Data Risks and Rewards