Affiliations: [a]
Department of Industrial Engineering, School of Business Administration, Northeastern University, Shenyang, China
| [b]
Dongguan Kanghua Hospital, Dongguan, China
| [c]
School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
Correspondence:
[*]
Address for correspondence: Mingming Li, Doctoral Student, Department of Industrial Engineering, School of Business Administration, Northeastern University, No. 195 Chuangxin Road, Hunnan District, Shenyang 110167, China. E-mail: mingminglixyz@gmail.com.
Abstract: BACKGROUND:Human error types and error factors are two important elements of error analysis. Understanding the relationship between them can contribute to new case analyses, the tendency of error occurrence statistics, error factor identification, and prevention of error recurrence. OBJECTIVE:To provide evidence and guidance for the prevention and improvement of medication communication errors by quantitatively exploring the relationship between error types and error factors. METHODS:Data were collected on self-reported errors in the medication administration process by nurses in all departments of three cooperative medical institutions, and an error sheet of specified style was adopted. Error types were determined by the systematic human error reduction and prediction approach method and human cognition processes. Error factors were extracted using the root cause analysis combined with Berlo’s communication model, and the relationship between error types and error factors was quantitatively studied using the partial least-squares regression method. RESULTS:After a one-by-one analysis of 303 error cases, the communication errors occurring in the nursing medication process could be explained by six error types and 12 error factors. In addition, 20 correlation patterns between the error types and error factors were quantitatively obtained, and their path coefficient distributions ranged from 0.088 to 0.467. CONCLUSION:The results of this study may provide reference to understand errors and establish countermeasures from the statistics of error occurrence trends, extract error factors related to error types and determine key error factors.
