PeerCoPilot: A Language Model-Powered Assistant for Behavioral Health Organizations
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908677524422656 |
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| author | Mo, Gao Raman, Naveen Chai, Megan Peng, Cindy Pagdon, Shannon Jones, Nev Shen, Hong Swarbrick, Peggy Fang, Fei |
| author_facet | Mo, Gao Raman, Naveen Chai, Megan Peng, Cindy Pagdon, Shannon Jones, Nev Shen, Hong Swarbrick, Peggy Fang, Fei |
| contents | Behavioral health conditions, which include mental health and substance use disorders, are the leading disease burden in the United States. Peer-run behavioral health organizations (PROs) critically assist individuals facing these conditions by combining mental health services with assistance for needs such as income, employment, and housing. However, limited funds and staffing make it difficult for PROs to address all service user needs. To assist peer providers at PROs with their day-to-day tasks, we introduce PeerCoPilot, a large language model (LLM)-powered assistant that helps peer providers create wellness plans, construct step-by-step goals, and locate organizational resources to support these goals. PeerCoPilot ensures information reliability through a retrieval-augmented generation pipeline backed by a large database of over 1,300 vetted resources. We conducted human evaluations with 15 peer providers and 6 service users and found that over 90% of users supported using PeerCoPilot. Moreover, we demonstrated that PeerCoPilot provides more reliable and specific information than a baseline LLM. PeerCoPilot is now used by a group of 5-10 peer providers at CSPNJ, a large behavioral health organization serving over 10,000 service users, and we are actively expanding PeerCoPilot's use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21721 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PeerCoPilot: A Language Model-Powered Assistant for Behavioral Health Organizations Mo, Gao Raman, Naveen Chai, Megan Peng, Cindy Pagdon, Shannon Jones, Nev Shen, Hong Swarbrick, Peggy Fang, Fei Computation and Language Computers and Society Machine Learning Behavioral health conditions, which include mental health and substance use disorders, are the leading disease burden in the United States. Peer-run behavioral health organizations (PROs) critically assist individuals facing these conditions by combining mental health services with assistance for needs such as income, employment, and housing. However, limited funds and staffing make it difficult for PROs to address all service user needs. To assist peer providers at PROs with their day-to-day tasks, we introduce PeerCoPilot, a large language model (LLM)-powered assistant that helps peer providers create wellness plans, construct step-by-step goals, and locate organizational resources to support these goals. PeerCoPilot ensures information reliability through a retrieval-augmented generation pipeline backed by a large database of over 1,300 vetted resources. We conducted human evaluations with 15 peer providers and 6 service users and found that over 90% of users supported using PeerCoPilot. Moreover, we demonstrated that PeerCoPilot provides more reliable and specific information than a baseline LLM. PeerCoPilot is now used by a group of 5-10 peer providers at CSPNJ, a large behavioral health organization serving over 10,000 service users, and we are actively expanding PeerCoPilot's use. |
| title | PeerCoPilot: A Language Model-Powered Assistant for Behavioral Health Organizations |
| topic | Computation and Language Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2511.21721 |