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An ethical assessment model for digital disease detection technologies.

Kerstin Denecke1

  • 1Bern University of Applied Sciences, Bern, Switzerland.

Life Sciences, Society and Policy
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Summary

Digital epidemiology (DDD) offers valuable tools for disease surveillance but presents ethical challenges. This study introduces an ethical assessment model to identify and address these concerns proactively in DDD projects.

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Area of Science:

  • Public Health
  • Informatics
  • Bioethics

Background:

  • Digital epidemiology (DDD) leverages information technology for infectious disease monitoring and understanding public health concerns.
  • The integration of internet-based research and social media into epidemiology and healthcare introduces novel technical and ethical challenges.
  • Existing ethical guidelines and EU project findings (M-Eco, SORMAS) inform the development of new assessment frameworks.

Purpose of the Study:

  • To develop an ethical assessment model for digital epidemiology projects.
  • To support the identification and description of ethical dimensions in DDD technologies and use cases.
  • To facilitate interdisciplinary discussions on ethical risks before DDD system implementation.

Main Methods:

  • Development of a four-dimensional ethical assessment model: user, application area, data source, and methodology.
  • Utilizing existing ethical guidelines and findings from EU projects (M-Eco, SORMAS).
  • Application in interdisciplinary meetings to gather diverse perspectives on DDD systems.

Main Results:

  • The model aids in recognizing, identifying, and describing ethical aspects of DDD technology.
  • It helps pinpoint ethical issues from multiple viewpoints, considering technology use.
  • Facilitates early identification and discussion of potential ethical risks.

Conclusions:

  • The ethical assessment model provides a structured approach to evaluating DDD projects.
  • It promotes proactive identification and mitigation of ethical concerns related to privacy, data security, and justice.
  • The model supports informed decision-making in the development and implementation phases of digital epidemiology initiatives.