The transition to environmentally sustainable healthcare is gaining urgency, yet public discourse shaping this shift remains underexamined. This study employs natural language processing (NLP) to analyze 15,976 English-language tweets (2006–2024) related to sustainable healthcare. Using Latent Dirichlet Allocation (LDA), eight dominant topics were identified, including eco-friendly access, net-zero implementation, climate impact, emissions, cost and waste, education, infrastructure, and green technologies. Sentiment analysis (VADER) of 9433 tweets showed 59.1% positive, 31.1% neutral, and 9.8% negative sentiment, with AI and technology topics receiving the highest positivity (73.5%) and climate-related topics the most negativity. Thematic analysis of 800 tweets revealed six cross-cutting themes, including healthcare’s environmental responsibility, co-benefits for health, urgency of climate action, and optimism in technological solutions. These findings offer a nuanced understanding of public perceptions, informing targeted strategies and communication for healthcare sustainability. The study also demonstrates the value of mixed-method NLP in examining enablers and barriers to health system transformation.
Shankar et al. (Fri,) studied this question.