Objective: The paper explores the application effect of anesthesia nursing based on lean management mode in patients with bone tuberculosis surgery and its impact on medical expenses. Methods: A total of 56 patients undergoing bone tuberculosis surgery from December 2019 to May 2022 were selected as subjects, and they were divided into two groups with 28 patients in each group. The control group was given routine anesthesia nursing, and the observation group was given anesthesia nursing based on lean management mode. The quality of anesthesia recovery, medical expenses and satisfaction of the two groups were compared. Results: After nursing, there was no significant difference in spontaneous breathing time between the two groups (P>0.05). Extubation time, fully awake time and stay time in anesthesia resuscitation room in the observation group were lower than those in control group (P<0.05). The medical expenses in the observation group were lower than those in the control group (P<0.05), and the satisfaction score in the observation group was higher than that in the control group (P<0.05). Conclusion: Anesthesia nursing based on lean management mode for patients undergoing bone tuberculosis surgery can improve the quality of anesthesia recovery, reduce various medical costs, reduce the economic burden of patients' families, and achieve higher satisfaction.
Key words
lean management mode /
anesthesia nursing /
bone tuberculosis operation /
medical costs
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References
[1] 吕晶. 优质护理在骨结核患者术后康复的应用效果分析[J].中国伤残医学,2021,29(8):62-63.
[2] 何英芳. 综合康复护理措施对骨结核患者生活质量及预后的影响[J].医学理论与实践,2020,33(13):2224-2225.
[3] 潘小华,董安,张洁.精益六西格玛提高手术室后勤管理效率研究[J].护士进修杂志,2018,33(12):1115-1117.
[4] 何剑英. 精益管理在老年病科护理管理中的应用[J].中医药管理杂志. 2018,26(12):139-140.
[5] 秦世炳. 加速康复外科理念规范脊柱结核外科解决方案[J].中国防痨杂志,2022,44(6):529-530.
[6] 方郁岚,杜天天,何绮月,等.基于精益管理模式的麻醉护理在日间手术中的应用[J].广东医学,2020,41(10):1054-1058.
[7] 董华. 麻醉护理在全身麻醉手术患者中的应用研究[J].中国医药指南,2020,18(4):277-277.
[8] Jiang Y, Wang Y, Shen L, et al.Identification of all-against-all protein-protein interactions based on deep hash learning[J].BMC Bioinformatics, 2022, 23(1):1-19.