A challenge with current Computerized Provider Order Entry (CPOE) systems includes patient identification errors, i.e. when an incorrect patient’s record is referenced. These types of errors can lead to patient safety issues such as administrating medication to the incorrect patient. Eye tracking technology can provide insights into the visual search patterns of healthcare professionals and shed light on how patient identification errors occur. This study investigates whether there are differences in visual search metrics, response time, and accuracy when searching for a patient by two identifiers – name or date of birth – from a list of patients with similar names. The findings revealed there was no effect of search strategy on speed or accuracy; however, there was an effect on fixation duration and number of fixations within specific areas of interest. Across both search strategies, there were more fixations on names. This demonstrates the importance of a patient’s name regardless of search strategy and is an important consideration to take into account if multiple patients share the same name. This study shows that eye tracking technology can be used to investigate the visual search patterns employed during patient identification and provide insights as to how patient identification errors occur. It also demonstrates a need to develop alternative methods to prevent patient identification errors apart from relying on healthcare professionals to verify patient identity.
[1]
Marc Berg,et al.
Viewpoint Paper: Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-related Errors
,
2003,
J. Am. Medical Informatics Assoc..
[2]
C. Ranger,et al.
Making sure the right patient gets the right care
,
2004,
Quality and Safety in Health Care.
[3]
D. Fisher,et al.
Nurses' behaviors and visual scanning patterns may reduce patient identification errors.
,
2011,
Journal of experimental psychology. Applied.
[4]
Donald L Fisher,et al.
Providers do not verify patient identity during computer order entry.
,
2008,
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.
[5]
S. Dekker.
New Technology, Automation, and Patient Safety
,
2016
.
[6]
Donald L Fisher,et al.
Patient identification errors are common in a simulated setting.
,
2010,
Annals of emergency medicine.
[7]
Dean F Sittig,et al.
Matching identifiers in electronic health records: implications for duplicate records and patient safety
,
2013,
BMJ quality & safety.
[8]
Tommy Strandvall,et al.
Eye Tracking in Human-Computer Interaction and Usability Research
,
2009,
INTERACT.