AI and Computing Power Expand Access to Out-of-Hospital Medical Information for Patient Communities
Amid the ongoing advancement of healthcare digitalization, technology systems centered on “AI + computing power” are gradually being integrated into every stage of optimizing medical services. However, ensuring that these benefits reach patients, particularly those in regions with scarce information resources, remains a significant challenge for achieving inclusive healthcare. A recent study led by Peng Cheng Laboratory represents an important practical exploration addressing this challenge, leveraging artificial intelligence and high-performance computing to explore pathways for bridging the gap in authoritative health information availability outside hospital settings.
Recently, the international journal *Journal of Medical Internet Research* published the findings of this study*. Leveraging “Home for Lymphoma Patients,” China’s largest community for individuals with hematologic malignancies, the research analyzed over 110,000 publicly posted forum messages from patients and their families between 2012 and 2023. The results revealed significant regional disparities in user engagement: users in developed eastern regions demonstrated higher posting activity, while participation in central and western regions has gradually increased in recent years but still lags behind. This suggests that patients in certain areas continue to face limitations in accessing information outside of clinical settings. Furthermore, the Lymphoma Coalition’s *2024 Global Report on Lymphoma and Chronic Lymphocytic Leukemia* indicates that patient organizations are the primary source of diagnostic and treatment information for lymphoma patients and their families in China. Therefore, assessing patients’ information needs through patient organizations can provide a better understanding of current information gaps.
An analysis of posts on the Lymphoma Home forum reveals that over 80% focus on core diagnostic and treatment needs. Patient concerns are primarily concentrated in four areas: first, professional interpretation of examination and pathology reports; second, scientific decision-making when faced with multiple treatment options; third, management of adverse drug reactions; and fourth, precise selection of hospitals and physicians. The study also points out that although most patient questions receive responses, only about 30% obtain substantial help, indicating that the need for timely and professional information in out-of-hospital settings remains unmet. Therefore, the study recommends the urgent development of an integrated service combining “authoritative physicians + artificial intelligence computing power + patient mutual-aid community Q&A.” This approach aims to enhance information supply through artificial intelligence and leverage computing power to narrow regional healthcare disparities, thereby providing greater support to patients who are already at an informational disadvantage.
Dr. Ning Kaida from Peng Cheng Laboratory, the first author of the study, stated, “Current medical data modeling, both domestically and internationally, primarily focuses on data related to in-hospital diagnosis and treatment. In contrast, patient-led data accumulated within patient communities, which reflect genuine patient needs, remain an underexplored area. In-depth analysis of such data can identify patients’ most urgent information needs, thereby enabling the design of more targeted intelligent services.” Professor Xia Li, Director of the Department of Statistics at the School of Mathematics, South China University of Technology, and the corresponding author of this study, further pointed out, “This research provides a successful case for leveraging cutting-edge large language models to analyze and summarize high-frequency needs among broader patient populations. Deep mining of patient community data not only precisely identifies directions for optimizing information services for patients but also offers authentic references from frontline patient groups for the formulation of healthcare-related policies, playing a significant role in advancing the field.” Currently, the teams from both institutions are relying on major scientific and technological infrastructure facilities, such as the “Peng Cheng Cloud Brain,” to integrate advanced algorithms and computing power into real-world healthcare scenarios.
AI and computing power will profoundly empower the future of healthcare. Collaboration among AI, computing resources, authoritative physicians, and patient organizations will help patient communities expand access to out-of-hospital diagnostic and treatment information, break down temporal and spatial barriers to high-quality medical resources, inject sustained momentum into building an equitable and accessible universal health system, and propel the digital transformation of healthcare toward a new, more human-centric stage.
*https://www.jmir.org/2025/1/e80497