gms | German Medical Science

Background: “Big Data” is a collective term for various digital techniques that are “big” in several aspects, including large quantities of data, processing speed and different types of data. In health promotion and prevention are various possibilities for Big Data projects.

In the context of this contribution the following questions are therefore examined: What types of data exist and how can they be used? How does Big Data Analytics differ from previous data analysis methods? Which exemplary procedures exist for the use of Big Data in the health care system?

Methods: There are many different methods suitable for Big Data approaches in health promotion and prevention. These include geographical questions, evaluation of social media data, search engine analysis, epidemiological approaches, evaluations, analyses of health insurance offers, questions on individualised health care or support for behavioural economic approaches. Basically, descriptive analyses create an information basis from which further need for action can be derived. Technical possibilities include data mining, machine learning, natural language processing, predictive analytics solutions, etc.

Results: On the one hand, not only classical health data can be used, but also data from other areas of life. What is special about Big Data analyses is the new quality of the results, which is the combination of previously unrelated data. By combining these data, it is possible to identify potential diseases at an early stage and to initiate preventive or health-promoting measures.

At the individual level, the protection of privacy remains an important issue. Thus, not all developments are foreseeable or even preventable so far, if the use and analysis of data can contribute to discrimination and disadvantage of individuals or groups.

Conclusion: Increasing digitization and large data resources will continue to provide far-reaching potential for Big Data research projects in health promotion and prevention in the future.

At the technical level, there are various challenges, such as structuring, standardisation and storage of data. Technical challenges will often require cooperation in interdisciplinary teams and it is important not to draw hasty conclusions in data evaluation. It is also important to critically reflect on the origin of the data collected and the data quality. A frequent important point of criticism regarding Big Data is the relationship between correlation and causality. With regard to the actors in the health care system, the question arises as to how data can be managed at the organizational level.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.