282. Optimization of nursing pathways for patients with depression based on computer data analysis

Abstract Background Depression, as a globally prevalent mental disorder, has individual differences and insufficient targeted intervention measures in its nursing pathway, which leads to low treatment compliance and high recurrence rate among patients with depression. However, existing research heavily relies on empirical nursing plans and lacks systematic integration and analysis of multidimensional patient data, making it difficult to achieve precise nursing care. In order to improve the nursing intervention effect on patients with depression, computer data analysis technology was used in the study. By integrating clinical indicators, psychological assessment results, and lifestyle behavior data of patients with depression, a personalized nursing pathway optimization model was developed, providing new methodological ideas for improving the quality of nursing care for patients with depression. Methods The study selected 200 patients with depression admitted to a psychiatric hospital from January 2024 to January 2025 as the research subjects. According to the principle of balanced baseline data, the patients with depression were divided into an experimental group (100 cases) and a control group (100 cases). Among them, the control group received routine depression care pathways, including medication guidance, psychological counseling, and regular follow-up. On the basis of routine nursing, the experimental group optimized the nursing pathway through computer data analysis. The specific steps are as follows: ① Data collection: Firstly, extract general information of patients with depression through the hospital information system. Secondly, the Hamilton Depression Scale (HAMD) and Self rating Depression Scale (SDS) were used to collect psychological assessment data. Finally, use smart wristbands to collect lifestyle behavior data such as sleep duration and exercise steps from patients. ② Data analysis: Use Python data analysis tools to clean and integrate the collected data, and use random forest algorithm to construct a nursing effect prediction model. Then, optimize nursing intervention measures in reverse based on the output results of the model. ③ Observation indicators: The total intervention duration is 8 weeks, and the observation time nodes include before intervention, 4 weeks after intervention, and 8 weeks after intervention. The observed indicators are the HAMD score and SDS score of the two groups of patients. Results After 8 weeks of intervention, the HAMD score of the experimental group was 12.34 ± 3.52, significantly lower than the control group's 18.67 ± 4.22, and the difference was statistically significant (t = 11.519, p<.001). Meanwhile, the SDS score of the experimental group was 45.87 ± 5.12, which was significantly lower than the control group's 56.27 ± 6.36 (t = 12.738, p<.001). In addition, there was a significant difference (p<.05) in the HAMD and SDS scores of the experimental group before and after intervention. Discussion It can be seen that optimizing the nursing path for patients with depression through computer data analysis can effectively reduce their depression scores and improve the effectiveness of nursing interventions. This plan integrates multidimensional data of patients to achieve personalized adjustment of nursing interventions, filling the limitations of traditional nursing pathways. Future research can combine artificial intelligence technology to optimize data analysis models and further enhance the dynamic adjustment capability of nursing plans.