Medical Social Networks, Epidemiology and Health Systems

Medical Social Networks, Epidemiology and Health Systems

Patrícia C. T. Gonçalves, Ana S. Moura, M. Natália D. S. Cordeiro, Pedro Campos
Copyright: © 2021 |Pages: 12
DOI: 10.4018/978-1-7998-3479-3.ch126
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The increasing use of medical software as an interface between patients and medical staff has raised alarming questions on the safety of data privacy and assurance of patients' rights. This issue has reached a new level with the emergent use of medical social networks in Health Information Systems. Medical networks, which work as an interface between the patient medical data and geographical and/or social connections, as well as between the patient individual needs and the attending medical doctor, can allow feasible and fast visualization/information systems. As new models for medical social networks and health data visualization and information systems are planned and presented, the need for protocols regarding data privacy in this context is becoming a subject of analysis and discussion. This chapter reviews the evolution and status quo of prospective medical social networks within data privacy and patients' rights, and discusses the ideal model and its future venues and interaction with ethics in the areas of Law, Health Policies, and Human Rights.
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The advent of the concept ‘medical social network’ came in 2007, when Barabási published a work entitled Medicine Network: From Obesity to the “Diseasome” (Barabási, 2007). Though Barabási used the term ‘medicine network’ instead of ‘medical social network’, he defended that since networks can be found on every particulars and features of human health, as knowledge regarding its effects on the patients’ biomedical aspects increased, a new field of expertise on medical practice and research would appear. These medical social networks were constructed and studied through graph theory visualization, namely as nodes and edges (Newman, 2010).

A node, within a social network, is defined as a distinct entity, which can be single, like a person or a molecule, or collective, like a country or a company. An edge, in the same context, represents the connection between two different nodes. The connections are multi-natured, i.e., they can be friendship or working connections, for example, if one thinks of human individuals; but they can also be of chemical definition, for example, as the bond type between intramolecular atoms. Exemplifying, when restricting to the medical context, the nodes may represent people, coupled by their biological or psychological factors, such as obesity (Christakis & Fowler, 2009) or depression (Rosenquist, Fowler, & Christakis, 2011), but they can also represent diseases themselves, like in comorbidity studies, i.e., the causal interaction between diseases (Folino, Pizzuti & Ventura, 2010). In the same context, the edges are the type of connections between the people, i.e., genetic/familiar or geographical, or different causal levels of comorbidity, if the nodes are diseases. These simple examples do no exempt the graph model to have added complexity, since the nodes can also comprise the information of gender, for instance, apart from the genetic or otherwise bio, social, or psychological factors. Figure 1 illustrates in a simplified manner the relation between real interactions and corresponding graphs.

Figure 1.

Example of a multi-layer social network representing real life connections between people. (Image obtained with software MuxViz(De Domenico, Porter & Arenas, 2015).)


Key Terms in this Chapter

Social Network: A graph representing social interactions between individuals.

Medical Network: A medical-based application of the principles of social networks.

Data Protection: Process of protecting important information from corruption, compromise, or loss.

Public Policy: Activities of governments acting directly or through delegation and influencing citizens' lives.

Public Health: Health of the population as a whole.

Information System: A software to collect, process, store, and disseminate information to support decision making, analysis, and visualization in an organization.

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