AI Can Help Improve Patient-Clinician Interactions

Source: ChatGPT

A recent study in JAMA provides a valuable framework for improving patient-clinician communication about weight loss, emphasizing that how these conversations are conducted is just as important as what is said. The study highlights evidence-based strategies—such as initiating discussions gently, avoiding stigma, and tailoring messages to each patient—that lead to more effective, well-received interactions. These insights reinforce that communication isn’t just a side element of care, it is a critical factor in patient outcomes.

• Effective patient communication improves weight loss outcomes—clinicians should initiate conversations gently, avoid stigma, and focus on positive, personalized messaging.
• Generic advice is ineffective—patients respond better to tailored discussions that acknowledge their past experiences and offer clear, actionable treatment options rather than vague recommendations.
• Structured support leads to better results—referring patients to behavioral weight loss programs or medical treatments is significantly more effective than advice alone.

Yet, despite emphasizing the need for customization, the paper does overlook a key tool for improving and scaling personalized communication: large language models (LLMs).

How Can Clinicians Efficiently Personalize Communication?

The study rightly recommends that clinicians tailor weight loss discussions to each patient’s unique concerns and history. But here’s the problem: In a time-constrained clinical environment, how can healthcare providers practically achieve this level of personalization—at scale?

This is where LLMs could be transformative.

LLMs: The Missing Tool for Patient Communication

In my recent post, "LLMs in Patient Education: The New Imperative", I explored how AI-driven tools are not just helpful but essential in healthcare communication providing key benefits for both clinician and patient.

• Standardize best practices, ensuring messages are clear, evidence-based, and stigma-free.
• Personalize interactions in real-time, dynamically adjusting responses based on individual patient concerns, health history, and prior attempts at weight loss.
• Reduce clinician burden, helping providers quickly generate empathetic, tailored messaging without adding time pressure.

Making Personalization Practical

The JAMA paper provides a solid foundation for improving weight loss conversations and the data are both compelling and important. But without LLM-powered solutions, these recommendations risk remaining aspirational rather than actionable.

If we are serious about patient-centered care and patient-centric education, we must acknowledge a key reality: LLMs can help bridge the gap between best-practice communication strategies and the real-world demands of clinical practice.

The future of healthcare communication isn’t just about what clinicians should say—it’s about how we empower them to say it better and at scale. LLMs may be a missing piece.