Feasibility of LLM-assisted post-discharge tuberculosis care: A comparative study of medication counseling, patient education, and follow-up planning.

BACKGROUND

Tuberculosis (TB) remains a major global health concern, with poor post-discharge adherence driving relapse and drug resistance. While China's 2024 guidelines advocate for "intelligent post-discharge management," the role of Large Language Models (LLMs) in this specialized care remains unexplored.

OBJECTIVE

To explore the feasibility of LLMs as auxiliary tools to complement TB specialists in post-discharge medication counseling, patient education, and follow-up planning.

METHOD: This exploratory study compared two LLMs (ChatGPT-4o, DeepSeek-R1) with TB physicians using 17 standardized clinical cases. Responses were assessed by three blinded specialists using objective metrics (precision, recall, F1) and subjective ratings across seven domains. Linear mixed models (LMMs) were employed to analyze the fixed effects of participant type while controlling for the random effects of clinical cases.

RESULT: In patient education, LLM-generated responses exhibited higher objective completeness and accuracy than those of physicians (precision: 0.86 vs 0.50, P < .001; recall: 0.53 vs 0.14, P= .026; F1: 0.65 vs 0.22, P= .018). Conversely, no statistically significant differences were observed in objective metrics for medication counseling and follow-up planning (all P > .050), although LLMs demonstrated a tendency toward broader but more discordant coverage. Subjectively, LLMs were rated significantly higher in suitability, understandability, and empathy across all tasks (all P < .050). No significant differences were found regarding perceived safety or text readability (P > .050).

CONCLUSION

This study suggests the potential of LLMs as auxiliary tools in TB post-discharge management. While quantitative performance in high-stakes tasks was comparable to physicians, the complementary qualitative patterns support a hybrid human-AI approach. These preliminary findings provide a basis for integrating LLMs into clinical workflows under professional supervision to enhance efficiency in resource-limited settings.