Socially Constructed Treatment Plans: Analyzing Online Peer Interactions to Understand How Patients Navigate Complex Medical Conditions
- URL: http://arxiv.org/abs/2503.21986v1
- Date: Thu, 27 Mar 2025 21:06:07 GMT
- Title: Socially Constructed Treatment Plans: Analyzing Online Peer Interactions to Understand How Patients Navigate Complex Medical Conditions
- Authors: Madhusudan Basak, Omar Sharif, Jessica Hulsey, Elizabeth C. Saunders, Daisy J. Goodman, Luke J. Archibald, Sarah M. Preum,
- Abstract summary: We characterize how treatment plans for complex conditions are "socially constructed"<n>We investigate whether and how socially constructed treatment-related knowledge is reflected in a state-of-the-art large language model (LLM)
- Score: 1.660288273261283
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
Related papers
- MAGI: Multi-Agent Guided Interview for Psychiatric Assessment [50.6150986786028]
We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational navigation.
We show that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning.
arXiv Detail & Related papers (2025-04-25T11:08:27Z) - A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions [13.17064228097947]
Large language models (LLMs) offer promising solutions in psychotherapy by enhancing the assessment, diagnosis, and treatment of mental health conditions.
This survey provides a comprehensive overview of the current landscape of LLM applications in psychotherapy.
We present a novel conceptual taxonomy to organize the psychotherapy process into three core components: assessment, diagnosis, and treatment, and examine the challenges and advancements in each area.
arXiv Detail & Related papers (2025-02-16T12:18:40Z) - LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains.
The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
arXiv Detail & Related papers (2024-06-09T09:03:11Z) - Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions.
VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information.
We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
arXiv Detail & Related papers (2024-05-29T23:19:28Z) - Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts [4.403408362362806]
We introduce the Chain-of-Interaction prompting method to contextualize large language models for psychiatric decision support by the dyadic interactions.
This approach enables large language models to leverage the coding scheme, patient state, and domain knowledge for patient behavioral coding.
arXiv Detail & Related papers (2024-03-20T17:47:49Z) - COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling [14.04866656172336]
We present a novel framework to infer the therapeutic working alliance from the natural language used in psychotherapy sessions.
Our approach utilizes advanced large language models (LLMs) to analyze transcripts of psychotherapy sessions and compare them with distributed representations of statements in the working alliance inventory.
arXiv Detail & Related papers (2024-02-22T16:56:44Z) - AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs.
This setup allows for realistic assessments of LLMs in clinical scenarios.
We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
arXiv Detail & Related papers (2024-02-15T06:46:48Z) - Investigating Collaborative Data Practices: a Case Study on Artificial
Intelligence for Healthcare Research [1.3178083420209858]
We look at the collaborative data practices of research consortia tasked with applying AI tools to understand and manage multiple long-term conditions in the UK.
Our findings reveal the adaptation of tools that are used for sharing knowledge and the tailoring of information based on the audience.
We identify meetings as the key setting for facilitating exchanges between disciplines and allowing for the blending and creation of knowledge.
arXiv Detail & Related papers (2023-11-30T10:19:33Z) - Safe and Interpretable Estimation of Optimal Treatment Regimes [54.257304443780434]
We operationalize a safe and interpretable framework to identify optimal treatment regimes.
Our findings support personalized treatment strategies based on a patient's medical history and pharmacological features.
arXiv Detail & Related papers (2023-10-23T19:59:10Z) - Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual
Assistance for Telehealth: The Mental Health Case [20.602347045884617]
We propose Alleviate to assist patients with mental health challenges with personalized care and assist clinicians with understanding their patients better.
Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed decisions.
In this paper, we explain the different modules of Alleviate and submit a short video demonstrating Alleviate's capabilities to help patients and clinicians understand each other better.
arXiv Detail & Related papers (2023-03-31T16:41:15Z) - POETREE: Interpretable Policy Learning with Adaptive Decision Trees [78.6363825307044]
POETREE is a novel framework for interpretable policy learning.
It builds probabilistic tree policies determining physician actions based on patients' observations and medical history.
It outperforms the state-of-the-art on real and synthetic medical datasets.
arXiv Detail & Related papers (2022-03-15T16:50:52Z) - Hierarchical Reinforcement Learning for Automatic Disease Diagnosis [52.111516253474285]
We propose to integrate a hierarchical policy structure of two levels into the dialogue systemfor policy learning.
The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms.
arXiv Detail & Related papers (2020-04-29T15:02:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.