Surgery Residents Spend Nearly 8 Months of Their 5-Year Training on the Electronic Health Record (EHR)

Journal of Surgical Education(2020)

引用 14|浏览4
暂无评分
摘要
OBJECTIVE: Electronic health records (EHRs) are an integral part of the medical system and are used in all aspects of care. Despite multiple advantages of an EHR, concerns exist over the amount of time that residents spend on computers rather than in direct patient care. This study aims to quantify the time a general surgery resident spends on the EHR during their training.DESIGN/PARTICIPANTS: Active usage time data from our institution's EHR were extracted for 34 unique general surgery residents from October 2014 to June 2019. Career time on the EHR was calculated and a "work month" was defined as a 4-week period of 80 hours per week.SETTING: Carolinas Medical Center, Charlotte, NC.RESULTS: Total career EHR usage for a general surgery resident was 2512 continuous hours, corresponding to 31.4 work weeks or 7.9 work months. In total, 7133 charts were opened with an average of 20.5 minutes on the EHR per patient chart. Career time spent on specific tasks included: chart review 10.6 work weeks, documentation 10.4 work weeks, and order entry 5.4 work weeks. The total number of orders entered were 57,739 and total number of documents created were 9222. EHR time in all aspects, patient charts opened, documents created, and number of orders entered decreased as postgraduate year increased.CONCLUSIONS: This is the first study quantifying the total time a general surgery resident spends on the EHR during their clinical training. Total EHR time equated to nearly 8 work months. General surgery residents spend considerable time on the EHR and this underscores the importance of implementing methods to improve EHR efficiency and maximize time for clinical training. (C) 2020 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.
更多
查看译文
关键词
Electronic health records,internship and residency,graduate medical education,work hours,general surgery
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