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

Madhusudan Basak, Omar Sharif, Jessica Hulsey, Elizabeth C. Saunders, Daisy J. Goodman, Luke J. Archibald, Sarah M. Preum

TL;DR

Problem: online health communities socially construct MOUD treatment plans that may diverge from clinical guidelines. Approach: a mixed-method study analyzing Reddit MOUD discussions, peer comments, LLM outputs, and clinician interviews to map socially constructed knowledge to guidelines. Findings: nine treatment-aspect themes with forty subthemes; frequent non-guideline information and rumors; LLMs reflect guideline-based content but miss lived experience and may produce unverified guidance. Significance: informs patient-centered communication, highlights how AI tools can complement peer support while ensuring safety and accuracy in online health forums.

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.

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

TL;DR

Problem: online health communities socially construct MOUD treatment plans that may diverge from clinical guidelines. Approach: a mixed-method study analyzing Reddit MOUD discussions, peer comments, LLM outputs, and clinician interviews to map socially constructed knowledge to guidelines. Findings: nine treatment-aspect themes with forty subthemes; frequent non-guideline information and rumors; LLMs reflect guideline-based content but miss lived experience and may produce unverified guidance. Significance: informs patient-centered communication, highlights how AI tools can complement peer support while ensuring safety and accuracy in online health forums.

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.

Paper Structure

This paper contains 39 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Social Constructionism in Healthcare and HCI Scholarship
  4. Collective Sensemaking in Online Communities
  5. Technology Mediated Tools for Treatment Adherence
  6. Medications for Opioid Use Recovery (MOUD)
  7. LLMs for Social Knowledge Construction
  8. Methods
  9. Background
  10. Data Collection
  11. Methodological Overview
  12. Positionality of the Authors
  13. Aspects of socially constructed treatment plan emerging from the patient narratives
  14. Methods
  15. Determining treatment aspects
  16. ...and 24 more sections

Figures (2)

  • Figure 1: Statistics on the adherence to the guideline for posts, comments, and LLM-generated responses. 'Aligns with guidelines': if the post aligns with a guideline instruction, 'Deviates from guidelines': if the post contradicts with a guideline instruction, 'No guideline': The content of the post is not verifiable with the available guideline, and 'Unable to determine': There is nothing to be compared with the guideline
  • Figure 2: Statistics of different strategies discussed by peers and GPT-4. Here, CS, PTE, PS, SP, and PLE indicate 'Clinical Strategy', 'Personal Treatment Strategy', 'Psychological Strategy', 'Speculation', and 'Personal Lived Experience' classes, respectively. Peer comment counts are normalized while comparing with GPT-4 response count to avoid counting the same strategy twice. We took the set of strategies that were discussed in the comments.