Research Article
Towards Augmenting Mental Health Personnel with LLM Technology to Provide More Personalized and Measurable Treatment Goals for Patients with Severe Mental Illnesses
@INPROCEEDINGS{10.1007/978-3-031-59717-6_13, author={Lorenzo J. James and Maureen Maessen and Laura Genga and Barbara Montagne and Muriel A. Hagenaars and Pieter M. E. Van Gorp}, title={Towards Augmenting Mental Health Personnel with LLM Technology to Provide More Personalized and Measurable Treatment Goals for Patients with Severe Mental Illnesses}, proceedings={Pervasive Computing Technologies for Healthcare. 17th EAI International Conference, PervasiveHealth 2023, Malm\o{}, Sweden, November 27-29, 2023, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2024}, month={6}, keywords={SMI mHealth LLM Gamification Goals}, doi={10.1007/978-3-031-59717-6_13} }- Lorenzo J. James
Maureen Maessen
Laura Genga
Barbara Montagne
Muriel A. Hagenaars
Pieter M. E. Van Gorp
Year: 2024
Towards Augmenting Mental Health Personnel with LLM Technology to Provide More Personalized and Measurable Treatment Goals for Patients with Severe Mental Illnesses
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-031-59717-6_13
Abstract
Mobile health (mHealth) tools are increasingly being used in various mental health domains to monitor patients with Severe Mental Illnesses (SMI), with the aim of potentially increasing patient engagement with their treatment. Patients with SMI who are prescribed Flexible Assertive Community Treatment (FACT) create a treatment plan together with their case manager, which serves as the leading document describing the goals that will be worked on during treatment. In order to incorporate the treatment plan goals of a patient in an mHealth application, the treatment plan goals need to be measurable. However, in previous work, we discovered that on average, only 25% of the available treatment plans include measurable goals. We have developed a protocol for making measurable goals with patients with SMI to address this issue. However, we anticipate low adoption of the protocol due to the potentially time-consuming nature of the steps involved. To mitigate this, we are exploring the use of AI to generate measurable treatment plan goals for patients with SMI and introduce a new workflow. In our exploratory study, we created a prototype of a system that may enable case managers and patients with SMI to generate measurable treatment plan goals using Large Language Models.
