Smart health coach to persuade Singaporeans to take preventive health measures
In Singapore, less than 1 in 4 go for annual vaccinations. Only a third of eligible adults are screened for common cancers. Convincing people to be vaccinated or screened for cancer will benefit from a personalised approach, but empathetic conversations are difficult to scale.
One way of sustaining empathetic conversations to drive preventive health action could be via LLMs. For persuasive goal-oriented conversation, the LLM has to adequately address the person’s concerns and needs.
Early efforts in goal-based dialogue planning are exploring multi-step planning and using Bayesian techniques to adaptively craft goal-driven utterances. However, there are few efforts that explicitly attempt to address the person’s replies at a psychological or empathetic level.
We introduce the ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model (Hochbaum, Rosenstock, & Kegels, 1952) as a psychological framework to craft empathetic replies.
- Framework: IRIS Interoperability Framework with Embedded Python
- Vector, RAG: IRIS Vector Search
- Model: OpenAI
gpt-4o
The Health Belief Model suggests that individual health behaviours are shaped by personal perceptions of vulnerabilities to disease risk, alongside the perceived incentives and barriers to taking action.
Our approach disaggregates these concepts into 14 distinct belief scores, allowing us to dynamically monitor them over the course of the conversation. You can view the belief scores in tools/belief_tools.json.
In the context of preventive health actions (e.g. cancer screening, vaccinations), we find that the agent is fairly successful at picking up a person’s beliefs around health actions (e.g. perceived vulnerabilities and barriers). We demonstrate the agent’s capabilities in the specific instance of a colorectal cancer screening campaign.
git clone git@github.com:Marymount-Labs/iris-coach.git
cd iris-coachImportant
Duplicate .env.example to create .env file
cp .env.example .envdocker compose upNote
Everything is local, nothing is sent to the cloud, so be patient, it can take a few minutes to start.
| Frontend | localhost:8051 |
| Backend | localhost:53795 |
The first page is the chat interface where you can interact with the ChatIRIS Health Coach. On the sidebar, you may select from a list of FAQs.
Vector Search in RAG pipeline:
- IrisVectorOperation.init_data: IRIS Vector store is initialised with the initial knowledge base
- ChatProcess.ask: Invoke VectorSearchRequest to retrieve the relevant information from IRIS
Scoring Agent:
- ScoreOperation.on_init: Initialise the scoring agent with initial prompt and belief map
- ScoreOperation.ask: Calculate user's belief scores based on verbal cues
- ScoreOperation.create_belief_prompt: Creates the optimal belief prompt using the user's belief scores
Finally, using the chat history, the belief scores from the scoring agent, and the retrieved context from the RAG pipeline, the conversation model will be able to generate an informed and persuasive response for the user
Find out more about it in Enhancing Preventive Health Engagement: The Backend Powering ChatIRIS with InterSystems IRIS
 Zacchaeus Chok |  Crystal Cheong |
Gao, Y. (2023, December 18). Retrieval-Augmented Generation for Large Language Models: A survey. arXiv.org. https://arxiv.org/abs/2312.10997
Hu, Z., Feng, Y., Deng, Y., Li, Z., Ng, S., Luu, A. T., & Hooi, B. (2023). Enhancing large language model induced Task-Oriented dialogue systems through Look-Forward motivated goals. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2309.08949
Jang, Y., Lee, J., & Kim, K.-E. (2020). Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 7994-8001. https://doi.org/10.1609/aaai.v34i05.6308
Lau, J., Lim, T.-Z., Jianlin Wong, G., & Tan, K.-K. (2020). The health belief model and colorectal cancer screening in the general population: A systematic review. Preventive Medicine Reports, 20, 101223. https://doi.org/10.1016/j.pmedr.2020.101223
Reddy, S. (2023). Evaluating large language models for use in healthcare: A framework for translational value assessment. Informatics in Medicine Unlocked, 41, 101304. https://doi.org/10.1016/j.imu.2023.101304
Rosenstock, I. M. (1974). The Health Belief Model and Preventive Health Behavior. Health Education Monographs, 2(4), 354–386. http://www.jstor.org/stable/45240623
X, Y., Chen, M., & Yu, Z. (2023). Prompt-Based Monte-Carlo Tree Search for Goal-oriented Dialogue Policy Planning. ACL Anthology. https://doi.org/10.18653/v1/2023.emnlp-main.439
Grongier. (n.d.). Interoperability embedded Python. GitHub. https://github.com/grongierisc/interoperability-embedded-python