Data Trauma: A Concept Analysis.

Area of Science:

• Health Informatics
• Digital Health Ethics
• Medical Sociology

Background:

• The increasing integration of artificial intelligence (AI) and algorithms in healthcare raises concerns about unintended consequences.
• Patient data is sensitive and potentially stigmatizing, necessitating careful consideration of its collection, storage, and use.
• The
• datafication
• of individuals in healthcare settings presents new ethical challenges.

Purpose of the Study:

• To introduce and define the concept of iatrogenic data trauma.
• To explore the risks and harms associated with the collection, storage, and use of patient data.
• To analyze how data processes can impact patient well-being and health equity.

Main Methods:

• Concept analysis using Walker and Avant's framework.
• Comprehensive literature review of academic and digital sources.
• Evidence synthesis and practical example illustration.

Main Results:

• Iatrogenic data trauma arises from flawed data processes in healthcare.
• This trauma can exacerbate health disparities, marginalization, and privacy breaches.
• It erodes trust in patient-provider relationships across the healthcare continuum.

Conclusions:

• Iatrogenic data trauma is a significant issue affecting multiple healthcare disciplines.
• Understanding and addressing data trauma is crucial for ethical AI implementation in healthcare.
• Future research should adopt a trauma-informed approach to investigate data-related harms.