Day surgery, which requires patients to complete preoperative preparations before hospitalization, is associated with a higher risk of same-day cancellation compared to traditional inpatient surgery. The rate of same-day cancellations has thus become a critical issue impacting healthcare efficiency, patient experience, and the broader adoption of this care model. This article systematically reviews the status of same-day cancellations in day surgery, both domestically and internationally, and analyzes their multifactorial causes and influencing elements. It further summarizes key management strategies and recent advances aimed at reducing the cancellation rate, which include the development of predictive models, optimization of the entire preoperative management process, multidisciplinary collaboration, the application of digital and intelligent technologies, and the use of quality improvement tools. Finally, the paper proposes an integrated patient management pathway for day surgery, aiming to provide evidence-based references for improving the overall quality of day surgery programs in clinical practice.
Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.