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Comparing Response Time, Errors, and Satisfaction Between Text-based and Graphical User Interfaces During Nursing Order Tasks

University of Utah, Salt Lake City, Utah (NS); Pacific Science and Engineering, San Diego, California (DK).

Corresdpondence and reprints: Nancy Staggers, RN, PhD, Associate Professor, Clinical informatics, College of Nursing, University of Utah, 10 S. 2000 E. Front, Salt Lake City, UT 84112; e-mail: <nancy.staggers{at}nurs.utah.edu>.

Received for publication: 07/08/99; accepted for publication: 10/28/99.

Despite the general adoption of graphical users interfaces (GUIs) in health care, few empirical data document the impact of this move on system users. This study compares two distinctly different user interfaces, a legacy text-based interface and a prototype graphical interface, for differences in nurses' response time (RT), errors, and satisfaction when the interfaces are used in the performance of computerized nursing order tasks. In a medical center on the East Coast of the United States, 98 randomly selected male and female nurses completed 40 tasks using each interface. Nurses completed four different types of order tasks (create, activate, modify, and discontinue). Using a repeated-measures and Latin square design, the study was counterbalanced for tasks, interface types, and blocks of trials. Overall, nurses had significantly faster response times (P < 0.0001) and fewer errors (P < 0.0001) using the prototype GUI than the text-based interface. The GUI was also rated significantly higher for satisfaction than the text system, and the GUI was faster to learn (P < 0.0001). Therefore, the results indicated that the use of a prototype GUI for nursing orders significantly enhances user performance and satisfaction. Consideration should be given to redesigning older user interfaces to create more modern ones by using human factors principles and input from user-centered focus groups. Future work should examine prospective nursing interfaces for highly complex interactions in computer-based patient records, detail the severity of errors made on line, and explore designs to optimize interactions in life-critical systems.

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