Nursing Informatics and the foundation of knowledge
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Peer 1
Jaimie Lester
According to McGonigle and Mastrian (2019), big data is defined as “voluminous amounts of data sets that are difficult to process using typical data processing; huge amounts of semi structured and unconstructed data that are unwieldy to manage within relational databases,” (p. 632). In healthcare, we use data every day. It assists us in making decisions and analyzing information to effect policy or change. Big data has both benefits and challenges in healthcare.
Big data has many benefits. One of the potential benefits is that you can combine a large amount of information into one place. You can run reports. You can use the data to make institutional decisions. You can use the data to implement policy. One example of this is you can assess the fall rates of patient on a particular unit. You can also assess fall rate overall for the hospital. If you find one unit has a higher fall rate than others, you can look more into why this is occurring. Once you find a reason, you can develop a policy or procedure aimed at reducing the fall rates.
Big data also consists of challenges. One example is that data can’t explain or measure everything. According to an article by Thew (2016), data can not measure things like nurse competency or commitment. There isn’t a report that you can run to analyze those things. Another example would be evaluating patient commitment following treatment regimens (Thew, 2016).
Another challenge of big data is securing it. According to Gaur (2020), data integration is so complicated and takes so much time that organizations spend less time upfront to secure data. Data security is very important in healthcare. One way to resolve this issue is to realize this important upfront and to have this as a focus from the beginning. Organizations lose millions of dollars and become target of cyber attacks when data security is not sufficient (Gaur, 2020). It is important for organizations to recruit and hire informaticists that specialize in security.
References
Gaur, C. (2020, December 11). Top 6 big data challenges and solutions to overcome. XenonStack. Retrieved June 25, 2022, from https://www.xenonstack.com/insights/big-data-challenges
McGonigle, D., & Mastrian, K. G. (2022). In Nursing Informatics and the foundation of knowledge (pp. 643–643). essay, Jones & Bartlett Learning.
Thew, J. (2016, April 19). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3-13.
AYOMIKUN OLAIYA
Big data typically refers to a large complex data set that yields substantially more information when analyzed as a fully integrated data set as compared to the outputs achieved with smaller sets of the same data that are not integrated. One main benefit of big data in a clinic is its ability to improve the patient’s experience. A potential challenge associated with it is the security issue, and the main strategy to mitigate the challenge is use of the current technology authentication, encryption, data masking and access control.
The potential benefit of using big data as part of a clinical system
The main benefit of using big data is that it improves the patients’ experience in terms of ensured healthiness to patients, cost reduction, error minimization, and enhanced preventive services. The healthiness of patients has been ensured via the use of vital signs monitoring applications. For example, we have apps that help diabetes patients to track insulin dosages, next appointments, etc. Cost reduction is ensured in that big data analysis can show the areas where reduction can be made, whether in diagnosis or treatment. Additionally, accurate and detailed data from big data enables providers to provide quality treatments in terms of accuracy (Svitla Team, 2018).
Potential challenge or risk of using big data as part of a clinical system
Big data is prone to bring security issues to clinical information. The most common security problem common globally is its vulnerability to fake data generation. Cybercriminals are known to deliberately invent and put in place counterfeit data into the system, which undermines the quality of the correct data. For example, when a clinical system uses sensor data to identify malfunctioning processes, the criminals could get into the system and make the sensor produce fake results such as wrong temperatures. This would cause the occurrence of damages in the clinic before they are identified. Additionally, we have the presence of untrusted mappers, absent security audits, and struggles of granular access control (Bekker).
Strategy to effectively mitigate the challenge
Security issues could be solved using the current technology, which entails authentication, encryption, data masking, and access control. Data masking, for example, entails where the sensitive data elements are masked with an unidentifiable value. Here we have the de-identification and masking of personal identifiers such as social security numbers, patients’ names, and birth dates, among others. this will put cybercriminals into confusion on which data to forge which reduces privacy loss issues. In access control, we have a situation where users can only access the data with the patient’s permission or trusted third parties. the ensures that the practitioners can only access only the information the patients wish them to know.
Reference
Bekker, A. (n.d.). Buried under big data: security issues, challenges, concerns.
Retrieved from ScienceSoft: https://www.scnsoft.com/blog/big-data-security-challenges
Jennifer Thew. (2016, April 19), Big Data Means Big Potential, Challenges For Nurse Execs.
Retrieved from Health leader media. https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs
Svitla Team. (2018, September 13). Benefits of using Big Data in healthcare industry.
Retrieved from Svitla: https://svitla.com/blog/benefits-of-using-big-data-in-healthcare-industry
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