Socially Constructed Treatment Plans: Analyzing Online Peer Interactions to Understand How Patients Navigate Complex Medical Conditions

2025-03-27

AI Summary

This study investigates how patients collaboratively construct treatment regimens in online peer communities—particularly within high-uncertainty domains such as oncology, mental health, and medication-assisted recovery—and examines patterns, drivers, and clinical implications of deviations between these “socially constructed treatment regimens” (SCTRs) and formal clinical guidelines. Employing a mixed-methods approach, we conducted digital ethnography and forum content analysis to identify six recurrent deviation patterns; validated the pragmatic validity of patient-generated knowledge through clinical expert interviews and guideline mapping; and assessed large language models (LLMs) using knowledge consistency evaluation, revealing systemic gaps in their representation of experiential therapeutic knowledge. We introduce the novel concept of SCTRs and integrate digital ethnography, clinical validation, and LLM auditing to provide the first empirical evidence of structural tensions between patient practice knowledge and authoritative guidelines—and demonstrate their clinically meaningful implications.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

When faced with complex and uncertain medical conditions (e.g., cancer, mental health conditions, recovery from substance dependency), millions of patients seek online peer support. In this study, we leverage content analysis of online discourse and ethnographic studies with clinicians and patient representatives to characterize how treatment plans for complex conditions are"socially constructed."Specifically, we ground online conversation on medication-assisted recovery treatment to medication guidelines and subsequently surface when and why people deviate from the clinical guidelines. We characterize the implications and effectiveness of socially constructed treatment plans through in-depth interviews with clinical experts. Finally, given the enthusiasm around AI-powered solutions for patient communication, we investigate whether and how socially constructed treatment-related knowledge is reflected in a state-of-the-art large language model (LLM). Leveraging a novel mixed-method approach, this study highlights critical research directions for patient-centered communication in online health communities.

Problem

• Analyzing online peer interactions to understand patient navigation of complex medical conditions
• Investigating deviations from clinical guidelines in socially constructed treatment plans
• Assessing large language models' reflection of socially constructed treatment knowledge

Innovation

• Content analysis of online patient discourse
• Ethnographic studies with clinicians and patients
• AI-powered large language model evaluation