Long-Term Sustainability and Adaptation of I-PASS Handovers

Joint Commission journal on quality and patient safety(2023)

引用 0|浏览4
暂无评分
摘要
Background: Inadequate communication during transitions of care is a major health care quality and safety vulnerability. In 2013 Massachusetts General Hospital (MGH) embarked on a comprehensive training program using a standardized handover system (I-PASS) that had been shown to reduce adverse events by 30% even when not completely executed on each patient. In this cross-sectional study, the authors sought to characterize handover practices six years later.Methods: Using a standardized interview tool, the researchers evaluated handovers between responding clinicians in 10 departments and then validated these findings through direct observations, allowing for flexibility and customization in the I-PASS elements. The study qualitatively compared I-PASS element use in verbal handovers to MGH early postintervention data, as well as verbal and written handovers with the I-PASS Study Group's postintervention results.Results: The authors observed 156 verbal and reviewed 182 written patient handovers. Ninety percent of departments adhered at least partially to the I-PASS system. Average handover duration ranged from 0.6 to 2.1 minutes per established patient. The service with best I-PASS adherence also consistently included the most information per unit of time. Acknowl-edging substantial differences in study technique, MGH adherence was, on average, comparable or better on all I-PASS elements in verbal handovers and on three of four elements of written handovers compared with the I-PASS Study Group's postintervention results.Conclusion: Although uptake has varied across services, six years after hospitalwide implementation of I-PASS, the ma-jority of services are performing structured and sequenced handovers, most of which include some elements of the I-PASS system. Those services with the best I-PASS adherence conducted the most efficient handovers.
更多
查看译文
关键词
sustainability,long-term,i-pass
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要