基于失效模式与效果分析的神经外科医院感染风险识别与干预效果评估

Risk identification and intervention efficacy evaluation of hospital-acquired infections in neurosurgery department based on failure mode and effect analysis

  • 摘要:
    目的 构建适用于综合性医院神经外科的区域性医院感染风险评估体系,并评价其感染防控效果。
    方法 采用失效模式与效果分析(FMEA)法识别神经外科感染风险因素,计算风险优先系数(RPN)并确定优先干预项目,据此制订干预措施并实施计划-执行-检查-处理(PDCA)循环法。收集2023年1—6月(对照组)与7—12月(干预组)数据,比较两组在环境卫生学监测合格率、住院患者医院感染发病率及细菌耐药检出率等方面的差异。
    结果 神经外科感染高风险因素包括患者自身存在的风险因素、特殊感染患者隔离措施落实不足、手术部位感染防控措施执行率低。干预后,环境卫生学监测合格率由81.55%上升至100.00%(χ²=120.49,P<0.001);住院患者感染发病率由2.62%下降至2.45%,发病例次率由3.12%下降至2.84%;多重耐药菌感染检出率由43.72%下降至36.79%;抗菌药物使用率由48.75%降至42.53%(χ²=34.09,P<0.001)。
    结论 基于FMEA法构建的风险评估体系可有效识别神经外科医院感染风险环节,针对性干预措施的实施可显著提升感染防控效果。

     

    Abstract:
    Objective To establish a regional risk assessment system for hospital-acquired infections in neurosurgery department of general hospital, and to evaluate its prevention and control effectiveness.
    Methods Failure mode and effect analysis (FMEA) was used to identify the core risk factors for infections in neurosurgery department. The risk priority number (RPN) of each risk factor was calculated to determine the priority intervention targets. Targeted interventions were developed and continuously refined through the plan-do-check-act (PDCA) cycles. Data from January to June 2023 (control group) and July to December 2023 (intervention group) were collected to compare the differences in environmental hygiene monitoring qualification rate, incidence rate of hospital-acquired infections among inpatients, and detection rate of bacterial antimicrobial resistance.
    Results High-risk factors for hospital-acquired infections in neurosurgery department included patient-related risk factors, inadequate implementation of isolation measures for special infections, and poor compliance with surgical site infection (SSI) prevention protocols. After intervention, the environmental hygiene qualification rate significantly increased from 81.55% to 100.00% (χ²=120.49, P<0.001). The overall hospital-acquired infection rate among inpatients decreased from 2.62% to 2.45%, the infection rate of per case declined from 3.12% to 2.84%, and the detection rate of multidrug-resistant organism infections reduced from 43.72% to 36.79%. Additionally, antimicrobial utilization rate decreased from 48.75% to 42.53% (χ²=34.09, P<0.001).
    Conclusion The FMEA-based risk assessment system can effectively identify critical infection risks in neurosurgery department, and targeted interventions can significantly improve infection prevention and control performance.

     

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