Effect of Network-Based Positive Psychological Nursing Model Combined With Elemene Injection on Negative Emotions, Immune Function and Quality of Life in Lung Cancer Patients Undergoing Chemotherapy in the Era of Big Data

BACKGROUND: With the development of big data, big data interpenetrate in every person's life. Health care is no exception to this trend, especially in regard to nursing analytics. The data that is analyzed and leveraged in this field is gathered from a variety of sources, including electronic health records (EHRs), medical histories, provider notes and mobile applications, creating an accumulation of personalized health information around each individual. OBJECTIVE: To explore the effect of the network-based positive psychological nursing model combined with elemene injection on negative emotions, immune function and quality of life (QOL) in patients with lung cancer (LC) undergoing chemotherapy. METHODS: The clinical data of 90 LC patients who underwent chemotherapy in our hospital from December 2020 to December 2021 were retrospectively analyzed, and the patients were equally split into experimental group (EG) and control group (CG) according to the order of enrollment. The patients in CG received routine nursing intervention during chemotherapy, while those in EG received the network-based positive psychological nursing model combined with elemene injection to compare negative emotions, immune function and quality of life (QOL) between the two groups. RESULTS: Compared with CG, EG had notably higher immune function indexes (P < 0.001), lower serum VEGF and MMP-2 levels (P < 0.001), lower HAMA and HAMD scores (P < 0.05), and higher scores of PTGI, physiological function, psychological function, social function and physical function (P < 0.001). CONCLUSION: The network-based positive psychological nursing model combined with elemene injection is a reliable method to enhance the immune function and QOL of LC patients undergoing chemotherapy and alleviate their negative emotions, which has a high clinical application value.