User Interface Techniques to Avoid Wrong Patient Selection Errors
Participants
- Awalin Sopan, Graduate Student in the Department of Computer Science, University of Maryland
- Catherine Plaisant, (Co-PI) - Research Scientist, UMIACS, Associate Director of Research at HCIL
- Ben Shneiderman, (Co-PI) - Professor, Computer Science, Researcher (and Founding Director) at HCIL
- Dr. Seth Powsner, Psychiatry and Emergency Medicine, Yale University
- Meirav Taieb-Maimon, Visiting Professor from the Department of Information Systems Engineering, Ben-Gurion University of the Negev
- Jyothi Vinjumur, University of Maryland
News
2017: We rarely hear from companies who put our suggestions to work... so it is a pleasure to be able to post this quote:
We implemented all of your suggestions, with the exception of including patient images, which we hope to add at some point in the future. We are extremely happy with the results, and it is great to be able to demonstrate that we care about patient safety by having integrated your work into our product.
Jules Maitland, Director of UX and Adoption at Populus Global Solutions (2017)
Context: the SHARP-C Project
The University of Maryland is one of the nine institutions participating in the National Center for Cognitive Informatics and Decision Making in Healthcare (NCCD) led by The University of Texas Health Science Center at Houston. NCCD is funded by the Office of the National Coordinator for Health IT under the Strategic Health IT Advanced Research Projects (SHARP) Program. This program seeks to support improvements in the quality, safety and efficiency of health care through advanced information technology. The NCCD award was one of four presented by the Office of the National Coordinator for Health Information Technology (ONC) through the American Recovery and Reinvestment Act of 2009 to address key barriers to health information technology. NCCD's research focus area is Patient-Centered Cognitive Support.
For more information see:1) the NCCD website at the University of Texas in Houston (the main PI) OR
2) the Maryland webpage which provides a summary of all the SharpC projects HCIL participated in.
Project Description
In this SharpC project we reviewed the user interface and human factor issues in EHR systems that cause the problem of selecting a wrong patient. We suggest a series of user interface design techniques that might reduce this type of errors and improve patient safety.
This works complements the recent (2013) SAFER guidelines released by ONC on HealthIT.gov, in particular the SAFER guidelines on Patient Identification
A prototype was developed to illustrate some of those techniques (the source code is available on request. Please contact Catherine Plaisant
A paper describes 21 user interfaces techniques developers can use.
A controlled experiment comparing 3 variants showed dramatic improvements
(see publications below).
Video
-
Demonstration of a subset of the techniques:
Watch it on YouTube: URL: http://youtu.be/FGKdC1t-t4c
or stream/download our mp4 version (9 MB, 854x430 resolution, 6:20 min.)
Publications
- Overview paper describing 21 new user interface techniques:
A. Sopan, C. Plaisant, S. Powsner, B. Shneiderman
User Interface Techniques to Reduce Wrong Patient Selection Error.
Proc. AMIA 2014 Annual Symposium, 1056-65. - Comparison of 3 user interface techniques:
M. Taieb-Maimon, C. Plaisant, Z. Hettinger, and B. Shneiderman.
Increasing Recognition of Wrong-Patient Errors through Improved Interface Design of a Computerized Provider Order Entry System
International Journal of Human-Computer Interaction 34, no. 5 (2018): 383-398
(Journal web page) - Safety Enhanced Design Brief (One-page guideline document - posted in NCCD website)
- University of Texas: Todd Johnson
- University of Texas, Houston: Jiaje Jang and Vimla Patel (PIs of the SHARP Project); Eliz Markovitz. Jorge Herskovic, Elmer V Bernstam, Dean Sittig, Hardeep Singh, Archana Laxmisan, and many more...
- Washington Hospital Center/MedStar, Washington, DC: A. Zachary Hettinger
Project Partners
Sponsorship
This work is 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.
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