Adverse drug event detection in pediatric oncology and hematology patients: using medication triggers to identify patient harm in a specialized pediatric patient population.

The Journal of Pediatrics(2014)

引用 26|浏览7
暂无评分
摘要
Objective To investigate the use of a trigger tool for the detection of adverse drug events (ADE) in a pediatric hospital specializing in oncology, hematology, and other catastrophic diseases. Study design A medication-based trigger tool package analyzed electronic health records from February 2009 to February 2013. Chart review determined whether an ADE precipitated the trigger. Severity was assigned to ADEs, and preventability was assessed. Preventable ADEs were compared with the hospital's electronic voluntary event reporting system to identify whether these ADEs had been previously identified. The positive predictive values (PPVs) of the entire trigger tool and individual triggers were calculated to assess their accuracy to detect ADEs. Results Trigger occurrences (n = 706) were detected in 390 patients from 6 medication triggers, 33 of which were ADEs (overall PPV = 16%). Hyaluronidase had the greatest PPV (60%). Most ADEs were category E harm (temporary harm) per the National Coordinating Council for Medication Error Reporting and Prevention index. One event was category H harm (intervention to sustain life). Naloxone was associated with the most grade 4 ADEs per the Common Terminology Criteria for Adverse Events v4.03. Twenty-one (64%) ADEs were preventable, 3 of which were submitted via the voluntary reporting system. Conclusion Most of the medication-based triggers yielded low PPVs. Refining the triggers based on patients' characteristics and medication usage patterns could increase the PPVs and make them more useful for quality improvement. To efficiently detect ADEs, triggers must be revised to reflect specialized pediatric patient populations such as hematology and oncology patients.
更多
查看译文
关键词
ADE,CPOE,CTCAE,EHR,NCC MERP,NICU,PPV,St. Jude,TLS
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