Increasing Recognition of Wrong-Patient Errors through Improved Interface Design of a Computerized Provider Order Entry System

ABSTRACT

Wrong-patient errors from inadvertent menu selections while using computerized provider order entry (CPOE) systems could have fatal consequences. This study investigated whether the manipulation of CPOE interface design could improve healthcare providers’ ability to recognize patient selection errors and also decrease the time to error recognition. Using a 2 × 2 design, 120 participants were randomly assigned to one of four groups, interacting with different versions of a simulated CPOE: (1) control – standard version; (2) highlighted selection – the selected patient row was highlighted for 2 s, by blanking the rest of the screen; (3) photo – photographs of patients’ faces were displayed in all screens; (4) combined – with photo and highlighted selection. Each participant navigated through five order scenarios. On the last scenario, an error was simulated by directing the participant to a wrong patient. Recognition rates of the wrong-patient error and times to error recognition were significantly improved for the highlighted selection, photo, and combined groups, relative to the control group. These results suggest that the addition of patient photos and highlighted selection could substantially reduce errors in CPOE systems and other applications.

Funding

This work was supported in part by Grant No. 10510592 for Patient Centered Cognitive Support under the Strategic Health IT Advanced Research Projects Program (SHARP) from the Office of the National Coordinator for Health Information Technology.

Additional information

Funding

This work was supported in part by Grant No. 10510592 for Patient Centered Cognitive Support under the Strategic Health IT Advanced Research Projects Program (SHARP) from the Office of the National Coordinator for Health Information Technology.

Notes on contributors

Meirav Taieb-Maimon

Dr. Meirav Taieb-Maimon is a senior faculty member at the department of Software and Information Systems Engineering at Ben-Gurion University of the Negev, Beer-Sheva, Israel. She specializes in Human Factors, Human–Computer Interaction, Information Visualization, Design and Analysis of Experiments and Evaluation of Information Systems, Interfaces and Visual Analytics Systems.

Catherine Plaisant

Dr. Catherine Plaisant (www.cs.umd.edu/hcil/members/cplaisant) is a Senior Research Scientist at the University of Maryland Institute for Advanced Computer Studies and Associate Director of Research of the Human-Computer Interaction Lab. She was elected to the ACM SIGCHI Academy in 2015 for her contributions to the field of human–computer interaction, medical informatics, and information visualization.

A. Zachary Hettinger

Dr. Aaron Zachary Hettinger is an Assistant Professor of Emergency Medicine at the Georgetown University of School of Medicine and both Medical Director and Director of Cognitive Informatics at MedStar Health’s National Center for Human Factors in Healthcare. He is board certified in both emergency medicine and clinical informatics. He focuses on patient safety research and health IT systems.

Ben Shneiderman

Prof. Ben Shneiderman (http://www.cs.umd.edu/~ben) is a Distinguished University Professor in the Department of Computer Science, Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://www.cs.umd.edu/hcil/), and a Member of the UM Institute for Advanced Computer Studies (UMIACS) at the University of Maryland. His latest book is The New ABCs of Research: Achieving Breakthrough Collaborations (Oxford, April 2016).