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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2310.05231 |
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| _version_ | 1866917595498676224 |
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| author | Kim, Taewan Bae, Seolyeong Kim, Hyun Ah Lee, Su-woo Hong, Hwajung Yang, Chanmo Kim, Young-Ho |
| author_facet | Kim, Taewan Bae, Seolyeong Kim, Hyun Ah Lee, Su-woo Hong, Hwajung Yang, Chanmo Kim, Young-Ho |
| contents | 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 on these findings, we discuss the implications of leveraging LLMs in the mental health domain, bridging the technical feasibility and their integration into clinical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_05231 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling Kim, Taewan Bae, Seolyeong Kim, Hyun Ah Lee, Su-woo Hong, Hwajung Yang, Chanmo Kim, Young-Ho Human-Computer Interaction Artificial Intelligence Computation and Language H.5.2; I.2.7 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 on these findings, we discuss the implications of leveraging LLMs in the mental health domain, bridging the technical feasibility and their integration into clinical settings. |
| title | MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language H.5.2; I.2.7 |
| url | https://arxiv.org/abs/2310.05231 |