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Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning

Adrienne Pichon1, Iñigo Urteaga2, Lena Mamykina1

  • 1Columbia University, Department of Biomedical Informatics, USA.

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|September 29, 2025
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Summary
This summary is machine-generated.

Designing intelligent self-management systems requires balancing user needs with AI capabilities. This human-centered AI framework aligns these factors for better personal health technology, especially for complex conditions like endometriosis.

Keywords:
chronic illnessreinforcement learningself-management

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Digital Health

Background:

  • Intelligent systems offer potential for patient self-management and improved quality of life.
  • Designing these systems is complex, requiring consideration of user needs, AI capabilities, and algorithmic constraints.
  • Personal health technologies necessitate a framework that integrates human and technological requirements.

Purpose of the Study:

  • To propose and implement a human-centered AI framework for designing intelligent systems in personal health.
  • To align human and technological requirements and constraints in the design process.
  • To guide the development of effective AI-based self-management tools.

Main Methods:

  • Utilized reinforcement learning concepts to elicit user needs via content analysis of interviews.
  • Analyzed "in the wild" user engagement logs from a self-monitoring app to identify data constraints.
  • Gathered and triangulated human-machine-data requirements for an endometriosis self-management tool.

Main Results:

  • Identified specific human, machine learning, and data requirements for a personal health self-management tool.
  • Demonstrated the application of a human-centered AI framework in a real-world health context.
  • Uncovered practical data constraints through analysis of user engagement logs.

Conclusions:

  • The proposed human-centered AI framework effectively aligns user needs with AI capabilities and data constraints.
  • This approach is crucial for developing intelligent self-management systems, particularly for complex chronic conditions like endometriosis.
  • Recommendations are provided for creating systems that meet human, data, and machine learning perspectives.