Exploring LLMs’ potential to help facilitators enhance online healthcare communities

This research paper was presented at the Fourth African Human Computer Interaction Conference (opens in new tab) (AfriCHI 2023), the pan-African conference on interactive digital technology design.

Online health communities can be a lifeline for people seeking healthcare support, enabling them to share experiences, ask questions, and receive help. These are particularly vital in low-and-middle-income countries (LMICs), where access to quality healthcare can be limited and online health communities function as a doorway for receiving expert advice and accessing trustworthy content. One platform that is widely used for this purpose is WhatsApp due to its popularity and ability to host facilitated communities for specific groups, like patients affiliated with a particular clinic.

For all their benefits, online health communities also face challenges due to the myriad responsibilities and equal lack of support for facilitators, who must answer questions, respond to ongoing discussions, and review reports. Facilitation requires staying abreast of ongoing chat threads, verifying facts, and generally just being available. Given that most healthcare professionals already have a full day of in-person healthcare work, facilitation occurs during lunch breaks, evenings, and even mornings before the workday begins.

Our paper, “Can Large Language Models Support Medical Facilitation Work? A Speculative Analysis (opens in new tab),” presented at AfriCHI 2023 (opens in new tab), discusses research conducted in collaboration with the University of Washington, where we examined facilitated WhatsApp groups created for young people living with HIV in informal settlements in Kenya. Facilitation involved moderating chats, providing emotional support, conducting administrative tasks, sharing information, and resolving conflicts. Because many discussions occurred at night, facilitators struggled to keep up with the chats, often missing important questions or responding to them a few days after they were posted. Facilitators also found it difficult to defuse tensions, which occurred from time to time.

LLMs’ potential in supporting online health facilitators

To help resolve these challenges, we explored ways large language models (LLMs) could potentially support facilitators, for example, by flagging important messages and helping with content authoring. LLMs’ language translation capabilities and capacity to answer questions and summarize information made them great candidates for online heath communities, understanding that facilitators should always verify the content that LLMs create. To explore their potential, we tested their application on chat log data. We concluded that an LLM-enabled copilot could help facilitators in several ways, such as:

• Coproducing compelling content: LLMs could help facilitators create educational and informative content for group members. They can summarize frequently asked questions, patient stories, and best practices for managing chronic conditions.
• Summarizing messages: LLMs could summarize long discussions in the chat, making it easier for facilitators to get up to date and identify important issues. Summarization can also help participants who need to be offline and might otherwise miss important information.
• Providing recommendations: LLMs could help facilitators conduct research when answering questions. However, facilitators must exercise due diligence and verify any suggestions the LLM makes.
• Performing sentiment analysis: LLMs could flag potential trouble spots in messages, such as declines in mental health, tension among participants, harmful advice, and misinformation.
• Assigning badges: LLMs could assign badges to group members in recognition for participating in discussions, completing tasks, or achieving milestones. This could help to motivate and engage members.

Importance of human facilitation

While LLMs offer numerous potential benefits for healthcare facilitation, it’s important to consider their challenges and limitations. We strongly believe that LLMs should be used to augment, not replace, human facilitation. One crucial reason is that this technology cannot provide the emotional support essential in these groups. Another challenge involves the potential for bias and harm. LLMs are trained on massive datasets of text and code, which might contain harmful biases and stereotypes. Additionally, LLMs can produce errors when dealing with content from outside the training data, such as cultural backgrounds that are underrepresented in this data.

Our research shows that the benefits these groups provide lie beyond merely providing information. Their success, gauged by participation levels, perceived value by members, and adherence to medical protocols, is attributed not only to the facilitators’ expertise but also to their empathy, humor, and care. These are human qualities that LLMs cannot replace.

Looking forward

When used to augment and support existing medical professionals, LLMs show promise in healthcare solutions, such as those for patients with chronic diseases in LMICs. We recommend that future research and practice in this area prioritize the following:

• Developing and testing LLM-enabled copilot systems that are tailored to specific patient populations and online health communities.
• Ensuring that design supports medical professionals, taking special care to preserve their agency.
• Designing copilot systems so that users can easily evaluate output as well as identify and correct erroneous content.
• Developing guidelines and regulations to ensure quality and safety when using LLMs for healthcare purposes.

Overall, the use of LLMs to support the work of online health community facilitation is an exciting new area of research. By making the facilitators’ tasks easier, they can pave the way for groups supporting more patients, improve adherence to medical protocols, and enhance well-being. While our research focused on a specific type of WhatsApp group, the potential of LLMs reaches far beyond. These models have the potential to support facilitators of online health communities across a diverse range of platforms.

S’ouvre dans un nouvel onglet