基于全维度真实世界数据优化的FMEA策略构建重症感染患者用药风险管理模型及评价体系

用药风险是威胁人类健康的重大公共卫生问题，重症感染患者是用药风险发生的高危人群，但国内外尚未形成重症感染患者用药风险的标准化管理体系。失效模式与效应分析法（FMEA）广泛用于医疗工作的前瞻性风险评估，预实验提示FMEA可用于重症感染患者用药风险评估，但单一使用FMEA评估用药风险存在局限。全维度真实世界数据可有效弥补FMEA的不足且更接近真实医疗环境。因此本课题拟将全维度真实世界数据和FMEA理论科学融合，调取我院近10年重症感染患者诊疗记录获取真实世界数据，以可能引发用药风险的指标为自变量集（X），以反映用药风险程度的指标为因变量集（Y），应用统计学模型，从X中筛出具有统计学意义且可看作Y“原因”的指标，形成风险条目，并采用FMEA量化分级形成风险评价指标体系，干预评估后构建重症感染患者用药风险管理模型及评价体系，实现重症感染患者用药风险预防与管控标准化，保障用药安全，提升药事管理水平。

The medication risk is a major public health problem that threatens human health. Patients with severe infection are high-risk group of medication risk. However, there is no standardized management system for medication risk of patients with severe infection at home and abroad. Failure Model and Effect Analysis (FMEA) is widely used in prospective risk assessment in medical work. Our pre-experiments suggested FMEA could be used to assess medication risk in patients with severe infection. However, there are certain limitations in assessment of medication risk using FMEA alone. The full-dimensional Real World Data is closer to the real medical environment, which can effectively make up for the shortcomings of FMEA. Therefore, in the present work, we intend to scientifically and effectively integrate the full-dimensional Real World Data and FMEA theory to realize the standardization of the strategies of medication risk prevention and control of patients with severe infection, thereby ensuring medication safety and improving the level of pharmacy administration. To achieve this goal, firstly, the medical records of patients with severe infection in our hospital in the past 10 years will be retrieved to obtain Real World Data. Next, the independent variables that may cause medication risk are taken as X indicators and the dependent variables that reflect the degree of medication risk are taken as Y indicators, the significant indicators will be screened out which can be regarded as the "causes" of Y from X to form risk items using the statistical model based on the data retrieved. Then, these risk items will be quantified and classified to form a risk evaluation index system using FMEA. Finally, a medication risk management model and evaluation system will be constructed for patients with severe infection after intervention evaluation.