TY - JOUR
T1 - Improving Patient Care Through Medical Image Perception Research
AU - Krupinski, Elizabeth A.
N1 - Funding Information:
MIPS members attack the problem of error in medical image interpretation from a variety of perspectives, including but not limited to detection and discrimination of abnormalities, cognitive and psychophysical processes, perception errors, visual search patterns, human and ideal observer models, computer-based perception, impact of display and ergonomic factors on image perception and performance, role of image processing on image perception and performance, and assessment methodologies. These areas actually coincide with medical image perception research priorities established by the NIH, the National Cancer Institute (NCI), and the Conjoint Committee on Diagnostic Radiology established priorities for medical image perception research that are still relevant today. These priorities include the development of psychophysical models for detecting abnormalities in medical images, improved understanding of the mechanisms of perception for interpreting medical images, developing aids for interacting with displays that enhance perception, studying alternatives to sequential presentation of cross-sectional imaging data, and performing research to improve the evaluation of medical imaging systems.
Publisher Copyright:
© The Author(s) 2015.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - The interpretation of medical images across medical specialties is critical to patient care. As technology changes, so does health care, and clinicians today are increasingly viewing medical images in a variety of environments. Although access to such data is useful, even clinicians with expertise in image interpretation make errors. These errors may become more frequent as clinician workdays become longer and the number of images to be interpreted becomes larger. To prevent errors in medical image interpretation, we need to understand the underlying perceptual and cognitive mechanisms that guide image interpretation. We can then use what is learned to develop better training methods, automated image analysis, and processing tools. We can devise methods to reduce clinician fatigue and stress, and develop practice guidelines thereby improving patient care and outcomes.
AB - The interpretation of medical images across medical specialties is critical to patient care. As technology changes, so does health care, and clinicians today are increasingly viewing medical images in a variety of environments. Although access to such data is useful, even clinicians with expertise in image interpretation make errors. These errors may become more frequent as clinician workdays become longer and the number of images to be interpreted becomes larger. To prevent errors in medical image interpretation, we need to understand the underlying perceptual and cognitive mechanisms that guide image interpretation. We can then use what is learned to develop better training methods, automated image analysis, and processing tools. We can devise methods to reduce clinician fatigue and stress, and develop practice guidelines thereby improving patient care and outcomes.
KW - diagnostic performance
KW - errors
KW - fatigue
KW - medical image perception
KW - policy
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U2 - 10.1177/2372732215600451
DO - 10.1177/2372732215600451
M3 - Article
AN - SCOPUS:85017500465
SN - 2372-7322
VL - 2
SP - 74
EP - 80
JO - Policy Insights from the Behavioral and Brain Sciences
JF - Policy Insights from the Behavioral and Brain Sciences
IS - 1
ER -