MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling
Taewan Kim, Seolyeong Bae, Hyun Ah Kim, Su-woo Lee, Hwajung Hong,, Chanmo Yang, Young-Ho Kim

TL;DR
MindfulDiary is a mobile app using large language models to assist psychiatric patients in journaling, improving record quality and aiding psychiatrists' understanding in a clinical setting.
Contribution
This paper introduces MindfulDiary, a novel LLM-based journaling app designed with mental health professionals' input for safe, effective use in psychiatric care.
Findings
Patients enriched their daily records more consistently.
Psychiatrists gained better insights into patients' thoughts.
The app demonstrated feasibility in clinical settings.
Abstract
In the mental health domain, Large Language Models (LLMs) offer promising new opportunities, though their inherent complexity and low controllability have raised questions about their suitability in clinical settings. We present MindfulDiary, a mobile journaling app incorporating an LLM to help psychiatric patients document daily experiences through conversation. Designed in collaboration with mental health professionals (MHPs), MindfulDiary takes a state-based approach to safely comply with the experts' guidelines while carrying on free-form conversations. Through a four-week field study involving 28 patients with major depressive disorder and five psychiatrists, we found that MindfulDiary supported patients in consistently enriching their daily records and helped psychiatrists better empathize with their patients through an understanding of their thoughts and daily contexts. Drawing…
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Taxonomy
TopicsMental Health via Writing · Digital Mental Health Interventions · Machine Learning in Healthcare
