Big Data Risks and Rewards: Potential Benefit for Using Big Data as Part of a Clinical System – Solution 1

Big Data Risks and Rewards

From the moment you wake, you are in fact a data-generation machine. Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.

As the volume of data increases, information professionals have looked for ways to use big data” large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards” and significant risks to healthcare. In this Discussion, you will consider these risks and rewards.

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.

Solution

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

The healthcare industry is traditionally associated with generating large amounts of data as compliance and regulatory requirements, keeping of records, and patient care takes place. Most healthcare organizations store this data in hard copy form, but emerging trends have seen the facilities move towards digitizing big data. According to Pastorino et al. (2019), big data has several definitions. However, within the context of this essay, it is taken to mean high volume, high velocity, and high variety information that calls for cost-effective and innovative ways of processing information to enhance both insight and decision-making.

Suffice it to say that the 3-V definition rhymes with healthcare data because of healthcare organizations with a staff of over 1000 store more than 400 terabytes of data for every organization (White, 2014). A typical healthcare facility of the 2020shasclaims paid every day, patient data is abstracted into electronic health records (EHRs) several times in a single day, and diagnostic test results have to be recorded in real-time electronically.

Are you looking for answers to a similar assignment? Premium Academic Affiliates has the top and most qualified writers to help with any of your assignments. All you need to do is place an order with us.

All these transactions mean that healthcare data may range from unstructured clinical notes of diagnostic tests to images or even discrete coded data elements. When it comes to utilizing the healthcare data stored, the organization may experience some challenges despite its benefits in digitizing its healthcare data.

Consequently, this essay contends that big data in clinical systems offer benefits to the organization in context. To achieve the goal, the paper examines a potential benefit of applying big data in clinical systems besides looking at a potential challenge of using the same data. Before a conclusion, at least one strategy is proposed as a mitigation measure on the use of big data.

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

Research findings on the use of big data for healthcare across different healthcare settings in the world demonstrate that healthcare firms concerned enjoy a significant impact on both healthcare and medical functions (Adbuzzamani et al., 2017).

The benefits include increased early diagnosis and subsequent appropriate treatment by discovering signs in early stages, lowered chances of adverse drug reactions, enhanced possibilities of disease prevention, and improved pharmacovigilance and patient safety. Be that as it may, the limited scope of this essay necessitates that only one potential benefit is explained further.

Effective utilization of big data in health care is bound to significantly improve its use of new information and communication technology to improve the quality of health care services offered, for example, in public health. The US healthcare system has to devise more efficient strategies to resolve the exponential increase of patients diagnosed with chronic diseases, particularly of the non-communicable type.

The use of e-health platforms can improve chronic patient management within the community setting by creating an interface between the patients themselves, multidisciplinary health care teams, and specialists. Additionally, Pastorino et al. (2017) observe that the use of big data and predictive analytics contributes to precision public health through enhanced surveillance and assessment.

The result of such initiatives results in collecting a large amount of data that is of high value in epidemiology research, analyzing the population’s health needs, informed policymaking, and evaluation of population-based interventions.

A Potential Challenge or Risk of Using Big Data as Part of a Clinical System

The utilization of big data in healthcare is laden with several challenges like the risk of compromising personal privacy and autonomy besides the growing demand for fairness, trust, and transparency by public members. This is because some healthcare organizations lack the requisite structures for data storage, data protection, data heterogeneity, and analytical flows in data analysis, thus rendering a big –data- driven healthcare.

A case in point is that sickle cell disease management driven by big data analytics faces several challenges (Saenyi, 2018). According to this researcher, interoperability – the ability of a healthcare information system to interconnect, exchange and use the information exchanged is a major challenge in SCD management.

This is because of information silos as different teams collect data manually and feed it to a system like the Dynamic Health Data Linkage. Besides information silos, the data collected is not always clean as it is presented in different formats to make sense to the supplier. Subsequently, more effort has to be put in place for sorting before storing or analyzing the data.

Proposed strategy to effectively mitigate the challenge of Interoperability in Use of Big Data

Wong et al. (2021) posit that having interoperable systems would lead to increased sharing of information using a process that enables the exchange of clean data. Healthcare organization management led by healthcare information technology experts should develop common interfaces and standardization of data sets. Interoperability is realized at the level of the dataset metadata and the level of the data.

A clinical system with a common interface and standardized data sets achieves interoperability in three levels: foundational, structural, and semantic. At the foundational level of interoperability, the exchange of data from one information technology system becomes possible to be received by another without the ability of the receiving information technology system to interpret the data sent.

At structural interoperability, the syntax of data exchange ensures that this exchange takes place between information technology systems to be interpreted at the data field level. Finally, semantic interoperability involves the ability of two or more systems or elements to exchange information and utilize the information exchanged (Lehne et al., 2019). The achievement of these levels of interoperability in the management of SCD would ensure mobile app data talk to each other and are more continuous.

Conclusion

In conclusion, this essay acknowledges that the use of bid data in healthcare has many benefits and opens several doors of opportunities. However, clinical science differs from other disciplines due to the additional challenges of data quality, privacy, and regulatory framework policies. Improved diagnoses and subsequent effective treatment is one of the many benefits, even as interoperability remains a key issue that needs to be definitively addressed. Granted that big data in clinical systems is still in its infancy, healthcare scholars and scientists need to carry out more studies to develop concrete strategies to improve the quality of big data used in healthcare.

References

Adibuzzaman, M., DeLaurentis, P., Hill, J., & Benneyworth, B. D. (2017). Big data in healthcare–the promises, challenges and opportunities from a research perspective: A case study with a model database. In AMIA Annual Symposium Proceedings (Vol. 2017, p. 384). American Medical Informatics Association. URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977694/

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.  URL: https://academic.oup.com/eurpub/article/29/Supplement_3/23/5628051

Saenyi, B. (2018). Opportunities and challenges of Big Data Analytics in healthcare: An exploratory study on the adoption of big data analytics in the Management of Sickle Cell Anaemia. URL: http://hj.diva-portal.org/smash/get/diva2:1285791/FULLTEXT01.pdf

White, S. E. (2014). A review of big data in health care: challenges and opportunities. Open Access Bioinformatics, 6, 13-18. URL: https://www.dovepress.com/a-review-of-big-data-in-health-care-challenges-and-opportunities-peer-reviewed-fulltext-article-OAB

Wong, B. L., Khurana, M. P., Smith, R. D., El-Omrani, O., Pold, A., Lotfi, A., … & Saminarsih, D. S. (2021). Harnessing the digital potential of the next generation of health professionals. Human Resources for Health, 19(1), 1-5. URL: https://link.springer.com/article/10.1186/s12960-021-00591-2

Lehne, M., Sass, J., Essenwanger, A., Schepers, J., & Thun, S. (2019). Why digital medicine depends on interoperability. NPJ digital medicine, 2(1), 1-5. URL: https://www.nature.com/articles/s41746-019-0158-1