Evaluation of the Effectiveness of Pain Management Models for Children with Malignant Tumors in Qinghai Region - Song, Kaiyang; Cao, Haixia (Journal of Contemporary Medical Practice, 2024)
Objective: To understand the current status of pain in children with tumors in Qinghai Province and its influencing factors, and to establish a pain management model suitable for Qinghai. Methods: A multidisciplinary pain management team was formed, consisting of physicians, nurses, pharmacists, volunteers, ethnic minority translators, and medical students, to implement multidimensional pain management. The effectiveness of this management model was statistically analyzed using questionnaires. Results: A total of 103 pediatric tumor patients were enrolled for the survey. The statistical analysis revealed the following results: 1) The intervention group's rates of regular pain assessment, pain assessment compliance, implementation and recording of analgesic measures, and pain management feedback were significantly higher than those of the control group (p < 0.05). 2) Comparison of anxiety and depression scores between the two groups showed that after the intervention, the SAS and SDS scores of the observation group were lower than those of the control group (p < 0.05). 3) Comparing pain relief effectiveness and satisfaction with pain control between the two groups, the total effective rate of pain relief in the intervention group was 88%, higher than the 54% seen in the control group (p < 0.05); satisfaction with pain control was 72.7%, also higher than the control group's 48.8% (p < 0.05). 4) Quality of life comparison between the two groups: physiological function, bodily pain, social function, and mental health scores of the observation group were significantly higher than those of the control group (p<0.01). Conclusion: Pain management through the palliative care model can alleviate the suffering of pediatric cancer patients during treatment, extend their lifespan, and improve their quality of life. This study provides clinical theoretical and data support for cancer pain management.