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Big Data and Ethics
Big Data and Ethics
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Big Data and Ethics

The Medical Datasphere

Jérôme Béranger

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📖 eBook - ePub

Big Data and Ethics

The Medical Datasphere

Jérôme Béranger

About This Book

Faced with the exponential development of Big Data and both its legal and economic repercussions, we are still slightly in the dark concerning the use of digital information. In the perpetual balance between confidentiality and transparency, this data will lead us to call into question how we understand certain paradigms, such as the Hippocratic Oath in medicine. As a consequence, a reflection on the study of the risks associated with the ethical issues surrounding the design and manipulation of this "massive data" seems to be essential. This book provides a direction and ethical value to these significant volumes of data. It proposes an ethical analysis model and recommendations to better keep this data in check. This empirical and ethico-technical approach brings together the first aspects of a moral framework directed toward thought, conscience and the responsibility of citizens concerned by the use of data of a personal nature.

  • Defines Big Data applications in health
  • Presents the ethical value of the medical datasphere via the description of a model of an ethical analysis of Big Data
  • Provides the recommendations and steps necessary for successful management and governance of personal health data
  • Helps readers determine what conditions are essential for the development of the study of Big Data

Information

Year
2016
ISBN
9780081010624
1

The Shift towards a Connected, Assessed and Personalized Medicine Centered Upon Medical Datasphere Processing

Abstract:

Today’s world corresponds to a universe where digital data is omnipresent, thus opening up prospects around reality that we have never known before. Hence, we are witnessing the emergence of the process of “datafication” which consists of digitizing and assessing everything, so that data emerges from written works, locations, individual actions or even fingerprints. Such a phenomenon contributes to transforming our ecosystem by providing the possibility of analyzing infinite quantities of increasing amounts of data, the acceptability of both approximation and disorder and the search for correlations rather than relationships between cause and effect. It may be observed that this notion of “correlations” stemming from biology has been used for a long time in economics.

Keywords

Economic market; Ethical risks; Law and regulation; Medical Big Data; Medical Datasphere Processing; Medicine 4.0; Open Data; Personal health data; Personalized Medicine; Quantified Self
Today’s world corresponds to a universe where digital data is omnipresent, thus opening up prospects around reality that we have never known before. Hence, we are witnessing the emergence of the process of “datafication” which consists of digitizing and assessing everything, so that data emerges from written works, locations, individual actions or even fingerprints. Such a phenomenon contributes to transforming our ecosystem by providing the possibility of analyzing infinite quantities of increasing amounts of data, the acceptability of both approximation and disorder and the search for correlations rather than relationships between cause and effect. It may be observed that this notion of “correlations” stemming from biology has been used for a long time in economics.
Big Data, which nowadays appears to both optimize processes and to participate in diagnosis and health care delivery, will clearly emerge into a metamorphosis, not only of the health system as we know it today, but also of medicine. We are thus returning to the post-industrial era. As Bell said in 1973, “A post-industrial revolution society is based on services. What counts is not raw muscles and power or energy, but information”. We are now in a new world which is centered upon digital data and where “Hippocrate’s medicine has given way to [email protected]” [BER 15] all being linked, measured and personalized. [FLO 09] characterizes this new ecosystem based upon the information philosophy as the fourth revolution1 after Copernicus2, Darwin3 and Freud4), allowing the reconciliation of nature (derived from the Greek word physis) and technology (derived from the Greek word technè) through a philosophical interpretation of the info-sphere.

1.1 The digital gap and the medical paradigm shift

The paradigm notion is revealed when a society takes a gamble that a model is sufficiently pertinent to be able to be substituted for reality. Once established, this model, which is hoped will open up a large field of discoveries, becomes exclusive and leads, de facto, to the overshadowing of the entire complex scope that fails to comply with it. As soon as this paradigm reaches breaking point, the new model generally assumes ownership of previous achievements, within a broader perspective. A paradigm shift is complex and always takes time. In 1977, Edgar Morin said, “It is difficult to change the starting points for reasoning, both associative and repulsive relationships between some initial concepts, but upon which the structure of reasoning, and indeed, all possible discursive developments depend”. This revolution not only changes our understanding of the outside world but also our notion of what we are as living beings.
In a world of ever-increasing data, where perceptions are becoming infinite, where everything will become a sum of infinite values, organizations are attempting to understand how to extract the value of all of this data that they are retrieving. This new mass of data, which has never been seen before, generates new knowledge. This causes a paradigm shift in health data, whose value lies in both sharing and pooling it. The best-known health applications fall within the personalization of the doctor–patient relationship.
Consequently, the digital turning point appears to be an epistemological revolution, since data IS are no longer positioned within categories of reason, but we are able to make use of it one piece at a time, in both a singular and differential way. For the philosopher Gaspard Koenig5, this has repercussions on science (moving from deduction to correlation), on language (with the identification of each object through its own characteristics), on knowledge (based upon the fact that reasoning because of its ability to conceive will lose its status and that knowledge becomes quantitative and not qualitative), on politics, on philosophy (with the field of immanence, if all objects are connected), and on insurance, politics and war (through cyber-crime), and also other associated fields. Subsequently, we are witnessing a convergence of data which is all homogeneous, digitized and that can be integrated, and therefore, have more meaningful correlations. “Data” is not the product of knowledge, but the material of such knowledge.
Thus, this new data science has been able to materialize, from the simple fact that in the last few years, databases, processing tools, server management and large-scale storage have been re-evaluated entirely, which has allowed their operational performance to be favored considerably. This data input for everything provides the means for mapping the world progressively in a quantifiable and analyzable way. Hence, these digital technologies may precisely emulate the behavior and habits to an increased extent, which, has not, as yet, been achieved [MAR 14].
This digital revolution represents as much a change in our ecosystem as the elaboration of new realities in which digital (based on the silicon chip or online technology) expands and is increasingly linked to analogical (carbon-based or offline technology), to both absorb it and amalgamate with it in the medium term. Stemming from this transformation, the concept of the “info-sphere” will be displaced as a means of referring to the information space till it becomes synonymous with reality. Consequently, real-time analysis is gradually becoming a major issue. The analysis of Big Data may be conducted using two data measurement indicators:
at the individual and personal level, where we focused on the collective and aggregated data before;
on a real-time basis, whereas we worked with retrospective statistics previously.
The convergence of these two components, both individual and real-time, is the cornerstone for these “massive data”.
We do intend to define this digital data conversion in the sense of a rupture; there is no continuity. Telemedicine digitization distinguishes itself as a “transformational space”, even if it is still achieved around a “perceptive structure” by symbols, images and writing [GHI 00]. This change may accentuate a digital gap between individuals as a result of:
the inability to access the method, the processing algorithm and the logic of the decision-making criteria which are used in Big Data analysis [TEN 13];
the difficulty for individuals and organizations to have access to or to buy data [MCN 14];
the complexity involved for actors in modifying data [BOY 12];
the possibility or impossibility for the individuals concerned to be informed as to the traceability of the data during its lifecycle [COL 14];
the difficulty of understanding both when and why specific processed Big Data have been classified in a particular category. This understanding is essential so as to reinforce self-monitoring of the latter [LYO 03].
Lastly, algorithmic knowledge has also progressed allowing both faster search and structuring of databases. From both chemical and post-traumatic health care, we are moving progressively towards preventative and personalized health care.
In the past, business data was seen as a management activity by-product, analyzed by “Data Mining” teams whose influence within the business was, as a consequence, reduced. Nowadays, we are at the beginning of an era where all professional and personal services and activities for individuals are becoming digitized. The attitudes of company directors are changing regarding recognized data as a significant innovation lever, which is likely to cause both new economic models and significant productivity gains. Only organizations and bodies with the knowledge to adapt their ISs to new perspectives of multifaceted data will really attain optimum value.
In just a few years, a large amount of other data, the so-called “unstructured data” or “semi-structured data”6 has been grafted onto structured data and run within traditional data processing applications (ERP, CRM, SCM and other applications). Thus, we may list several types of unstructured or semi-structured data:
electronic messages (e-mails and instant messaging), data entries and evidence placed on the Web, digitized contractual documents, and conversations with call centers and websites;
mobility-linked data: web browser history, identifiers (SIM cards, ID numbers such as IMEI, UID etc.) and location-based positioning;
data generated by connected objects: machines, sensors, home automation, “smart” cars and meters, set-top boxes (Internet operator gateways, cable TV boxes or other similar devices) and personal biometric systems;
data which are created and shared outside of traditional business communication circuits through Internet social networks.
This data will be identified and designated as unstructured, once they require a more complex transformation, before their significance is revealed. Processing of such data (particularly, in real time) irretrievably through powerful algorithms. Lastly, these new types of “data” may have the purpose of enriching other types of “data”. However, they may also constitute, in certain cases, the core data being processed [BEN 14]. Subsequently, in this context, there are different analytical approaches around digitized data (see Figure 1.1).
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Figure 1.1 Analytical approaches to data according to analytical complexity and digital data size
Traditional approaches to health research may be noted, with hypotheses based on deductive reasoning, generally relying on a small quantity of data, collected in highly controlled circumstances, such as randomized clinical trials. With Big Data, new additional possibilities appear in terms of scope, flexibility and also data visualization. Techniques such as the extraction of large amounts of data facilitate inductive reasoning and an exploratory analysis of data is revealed. This allows researchers to identify data models which are independent of specific hypotheses [ROS 14].
In this context, data clustering is justified by the concept that says...

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Citation styles for Big Data and EthicsHow to cite Big Data and Ethics for your reference list or bibliography: select your referencing style from the list below and hit 'copy' to generate a citation. If your style isn't in the list, you can start a free trial to access over 20 additional styles from the Perlego eReader.
APA 6 Citation
Béranger, J. (2016). Big Data and Ethics ([edition unavailable]). Elsevier Science. Retrieved from https://www.perlego.com/book/1834522/big-data-and-ethics-the-medical-datasphere-pdf (Original work published 2016)
Chicago Citation
Béranger, Jérôme. (2016) 2016. Big Data and Ethics. [Edition unavailable]. Elsevier Science. https://www.perlego.com/book/1834522/big-data-and-ethics-the-medical-datasphere-pdf.
Harvard Citation
Béranger, J. (2016) Big Data and Ethics. [edition unavailable]. Elsevier Science. Available at: https://www.perlego.com/book/1834522/big-data-and-ethics-the-medical-datasphere-pdf (Accessed: 15 October 2022).
MLA 7 Citation
Béranger, Jérôme. Big Data and Ethics. [edition unavailable]. Elsevier Science, 2016. Web. 15 Oct. 2022.