Big Data in Healthcare Research

Jessica Malenfant provides a presentation on the use of information and its analysis for healthcare research. Anderson (2008) considers the possibility of a new research method that can substitute the scientific method. These two works can contribute some information to the topic of big data and its usefulness for health care.

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Malenfant shows that a variety of institutions agree on an extensive common data model (CDM), which is employed to create the infrastructure for networks that enable the secondary use of large amounts of healthcare data. The extensiveness of CDM is required to ensure its ability to answer multiple questions through queries or specific programs. Naturally, the use of the same CRM by many networks improves the opportunities for research and increases the amounts of big data available. Malenfant mentions several networks as examples, including the FDA’s Sentinel Network, which is used to enable the control of medical products quality, and the PCORnet, which is meant specifically for research. As suggested by Malenfant, the use of these frameworks can be helpful to healthcare through enhancing decision-making and providing information to all stakeholders. Similarly, Flockhart, Bies, Gastonguay, and Schwartz (2016) consider the applicability of network systems and predictions based on their analysis to pharmacology, suggesting that it can be used to identify and eliminate gaps that currently exist in the field.

Malenfant also considers the constraints of different networks; she demonstrates that they depend on the source of the data. For instance, the insurer is capable of tracking health-related purchases anywhere, but EHRs are local and can only appear if a patient-specialist encounter occurs. However, EHRs provide the network with in-depth details. In other words, different networks have their advantages and disadvantages. The speaker also mentions that technology is of great help for the networks and that it proceeds to improve, bringing about new possibilities. Anderson (2008) considers the same idea, suggesting that it is due to technology that we should be able to move from the imperfection of models. Also, it is a technology that can provide a different approach to the new data, which, as Anderson (2008) states, is necessary.

Anderson (2008) points out that the amounts of data that are used nowadays are incredible, and he calls the current age the Age of Petabyte. Anderson (2008) considers the applications that are outside of healthcare (for example, Google), but he admits that the “big target” of the big data is research (para. 8). Anderson (2008) uses the example of Venter, a scientist who managed to discover multiple new species by sequencing organisms, ecosystems, ocean, and air without a clear model. According to Anderson (2008), other scientists are likely to follow Venter’s lead (para. 17). The author demonstrates that these huge amounts of data need to be analyzed before attempting to hypothesize, which contradicts the classic scientific method.

According to Burke (2013), the scientific method can be defined as a “controlled investigative approach” to science that has advanced it greatly by producing data (pp. 201-203). Today, however, there is no issue with getting data, and the challenge, as Burke (2013) suggests, is now in use all of it and strive to make more accurate predictions on its basis. The method is not exactly new; in fact, it can be regarded as a form of inductive reasoning (Mazzocchi, 2015). The results of using this approach might help to make healthcare more effective (including cost-effectiveness), personalized, and timely, and less risky (Burke, 2013, p. 206; Groeneveld & Rumsfeld, 2016). Thus, the topic is of importance to healthcare specialists.

The presentation by Malenfant contributes more information to the subject of the use of big data in healthcare, which sheds some light on its sources and application. As for Anderson (2008), despite being published in a popular magazine and written as a pop-science work, his article has proven to be very influential for modern scientists as it is quoted and debated in the modern scientific literature (Flockhart et al., 2016, p. 804; Mazzocchi, 2015).

References

Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Web.

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Burke, J. (2013). Health analytics: Gaining the insights to transform health care. New York, NY: John Wiley & Sons.

Flockhart, D., Bies, R., Gastonguay, M., & Schwartz, S. (2016). Big Data: Challenges and opportunities for clinical pharmacology. British Journal of Clinical Pharmacology, 81(5), 804-806. Web.

Groeneveld, P. & Rumsfeld, J. (2016). Can big data fulfill its promise? Circulation: Cardiovascular Quality and Outcomes, 9(6), 679-682. Web.

Mazzocchi, F. (2015). Could Big Data be the end of theory in science? A few remarks on the epistemology of data-driven science. EMBO Reports, 16(10), 1250-1255. Web.

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"Big Data in Healthcare Research." StudyCorgi, 24 Nov. 2020, studycorgi.com/big-data-in-healthcare-research/.

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StudyCorgi. 2020. "Big Data in Healthcare Research." November 24, 2020. https://studycorgi.com/big-data-in-healthcare-research/.

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StudyCorgi. (2020) 'Big Data in Healthcare Research'. 24 November.

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