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Computerized smart infusion pumps have been widely implemented to decrease the rate of intravenous (IV) medication errors in hospitals. However, these devices have not always achieved their potential, and important IV errors still persist. Findings from a previous study [1] that assessed the frequency of IV medication errors and the impact of smart infusion pumps identified major issues related to use of smart infusion pumps in a single facility, but generalizability of these results is uncertain. Additionally, lack of standardized methodology for measuring these errors remains an issue. In this study, we developed an observational tool to capture IV medication errors through iterative participatory design with interdisciplinary experts and then tested the tool by using incident cases regarding smart pump errors. We found that the tool could capture all smart infusion pump errors and is ready for testing for use as standard data collection tool in different hospital settings.

作者:Kumiko, Ohashi;Patricia, Dykes;Kathleen, McIntosh;Elizabeth, Buckley;Matt, Wien;Kevin, Kreitzman;Michael, Dumais;David W, Bates

来源:Studies in health technology and informatics 2013 年 192卷

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收藏
| 浏览:11
作者:
Kumiko, Ohashi;Patricia, Dykes;Kathleen, McIntosh;Elizabeth, Buckley;Matt, Wien;Kevin, Kreitzman;Michael, Dumais;David W, Bates
来源:
Studies in health technology and informatics 2013 年 192卷
Computerized smart infusion pumps have been widely implemented to decrease the rate of intravenous (IV) medication errors in hospitals. However, these devices have not always achieved their potential, and important IV errors still persist. Findings from a previous study [1] that assessed the frequency of IV medication errors and the impact of smart infusion pumps identified major issues related to use of smart infusion pumps in a single facility, but generalizability of these results is uncertain. Additionally, lack of standardized methodology for measuring these errors remains an issue. In this study, we developed an observational tool to capture IV medication errors through iterative participatory design with interdisciplinary experts and then tested the tool by using incident cases regarding smart pump errors. We found that the tool could capture all smart infusion pump errors and is ready for testing for use as standard data collection tool in different hospital settings.