Using network models in person-centered care in psychiatry: How perspectivism could help to draw boundaries

Area of Science:

• Psychiatry
• Network Science
• Computational Social Science

Background:

• Person-centered care (PCC) in psychiatry aims to tailor treatment to individual patient needs.
• Network analysis offers tools for modeling complex relationships in psychiatric care.
• Personalized network models are increasingly explored for their potential in PCC.

Purpose of the Study:

• To explore conceptual challenges of applying network analysis to person-centered care (PCC) in psychiatry.
• To identify and define 'boundary problems' inherent in personalized network models within psychiatric practice.
• To propose perspectival reasoning as a method to address these boundary issues and enhance clinical relevance.

Main Methods:

• Conceptual analysis of network analysis principles in the context of psychiatric PCC.
• Identification of explanatory and representational boundaries within personalized network models.
• Exploration of perspectival reasoning as a framework for defining model scope.

Main Results:

• Using network analysis in psychiatric PCC introduces significant 'boundary problems,' complicating the inclusion/exclusion of elements in models.
• These boundaries can restrict the capacity of personalized network models to generate clinically actionable insights.
• Perspectival reasoning offers a strategy to explicitly define the scope and applicability of these models.

Conclusions:

• Personalized network models in psychiatric PCC face inherent boundary challenges that can limit their clinical utility.
• Addressing these boundary problems is crucial for the effective implementation of network analysis in mental healthcare.
• Perspectival reasoning provides a valuable approach to refine the application and interpretation of personalized network models in psychiatry.