Navigating fairness aspects of clinical prediction models

Area of Science:

• Health Informatics
• Medical Ethics
• Algorithmic Fairness

Background:

• Healthcare algorithms are widely adopted but often lack comprehensive evaluation, risking inequitable clinical outcomes across diverse demographic and socioeconomic groups.
• Societal biases embedded in development data can lead algorithms to favor privileged populations, exacerbating existing health disparities.
• Limited understanding and application of algorithmic fairness metrics hinder their use in real-world healthcare settings.

Purpose of the Study:

• To guide healthcare professionals in critically assessing healthcare algorithms for societal and data-driven biases.
• To promote transparency and accountability in algorithm development and implementation.
• To encourage proactive identification and mitigation of biases to ensure equitable patient care.

Main Methods:

• A participatory approach was used, involving clinicians and patients with type 2 diabetes experience.
• Guiding questions were developed to aid healthcare professionals in critically evaluating algorithms.
• Focus on identifying and mitigating biases in societal factors, data, algorithms, and healthcare systems.

Main Results:

• Development of a set of guiding questions for healthcare professionals to assess algorithmic bias.
• Emphasis on the need for transparency and critical thinking in algorithm evaluation.
• Highlighting the importance of a participatory approach involving diverse stakeholders.

Conclusions:

• Healthcare professionals need to critically evaluate algorithms for embedded biases.
• Addressing fairness in algorithms is crucial for building an equitable healthcare system.
• Proactive mitigation of biases is essential to prevent harm to marginalized patient groups.