核心护理质量的大数据分析与改善轨迹

Improvement track of core nursing quality by measurement of big data

目的:对护士给药、药物过敏试验、输血、标本采集送检、观察监测等代表核心护理工作的质量内容进行周期性客观测量与持续改进,寻找改善结果的规律。方法:对2015年至2018年间护士执行医嘱过程在临床信息系统留痕后提取到的201254条对应数据进行研究,以准确性、及时性、有效性指标,分类测量护理质量,3个月为一个质量周期,首个周期的质量为基线值,之后各周期质量与之比较,同时与前一周期质量比较。结果:与基线质量比较,一般第一次存在统计学差异(P<0.05)最迟出现在第二个质量周期,此后凡优于第一次与基线质量比较具有统计学差异的指标的质量,较基线质量均具有统计学意义,无时间局限;与前一周期质量比较,存在统计学差异(P<0.05)出现的质量周期与维持时间不等。结论:大数据的非现场质量测量实现了客观、公平、实时的质量测量,形成精准质量控制,揭示了一定条件下质量改进变化轨迹与规律。

Objective:To analysis the track and rules of improved nursing quality by conducting periodic measurement and continuous improvement of nursing quality of the core work of nurses,including drug administration,blood transfusion,drug allergy experiment,specimen collection and submission,and monitoring.Methods:From 2015 to 2018,201254 pieces of corresponding data were extracted from the clinical information system during the implementation of medical orders by nurses.Nursing quality was classified and measured by indicators including accuracy,timeliness and effectiveness.Three months was referred as a cycle of quality and the data of each quality indicator at different periods were compared.Results:Compared with the baseline quality,statistically significant difference(P<0.05)appeared in the second quality improvement cycle,almost all results of the quality cycle were superior to the baseline data.Compared with the quality of the previous cycles,statistical difference(P<0.05)appeared in the quality improvement cycle and maintenance time.Conclusion:The offsite quality measurement of big data achieves objective,fair and real-time quality measurement,forms a specialized,scientific and accurate quality control process with specificity,collection and replication,and reveals the trajectory and rules of quality improvement.