PACS Ergonomics: Making Your Working Time Comfortable

Article

Working with your PACS can be more comfortable with a few simple steps.

Workstation ergonomics has been a hot topic throughout radiology in recent years. Recently, at the 2013 New York Medical Imaging Informatics Symposium, David Hirschorn, MD, chair of radiology informatics at Staten Island University Hospital, outlined several recommendations that can increase the comfort of your study reading.

Screen Display: When dealing with routine primary diagnoses, your display should be between 20 inches and 24 inches diagonally unless you’re using a two-panel monitor. For clinical review, however, this standard doesn’t apply.

Viewing: Maintain a viewing distance that’s between one-third to one-half longer than the diagonal size of your monitor. Rather than moving your head, zoom and pan to see images better. For technologists and clinical-care staff, 250 to 300 pixels is appropriate.

Pixels: Set your pixels based on how much your eye can comfortably see at a normal viewing distance. Do not rely on the acquisition resolution. Remember – to recognize this change, in 2012, the American College of Radiology revised the technical standard for electronic practice of medical imaging. For primary diagnosis, your pixels should be between 200 and 275, depending on the viewing distance and your eyesight.

Luminance: For your diagnostic monitors, keep the luminance at least 350 cd/m2; 420 for mammography; 250 for technologist and clinical care. Be sure the background of your screen isn’t too black, and set up ambient light, as well as backlighting.

 

Recent Videos
What New Research Reveals About the Impact of AI and DBT Screening: An Interview with Manisha Bahl, MD
Can AI Assessment of Longitudinal MRI Scans Improve Prediction for Pediatric Glioma Recurrence?
A Closer Look at MRI-Guided Adaptive Radiotherapy for Monitoring and Treating Glioblastomas
Incorporating CT Colonography into Radiology Practice
What New Research Reveals About Computed Tomography and Radiation-Induced Cancer Risk
What New Interventional Radiology Research Reveals About Treatment for Breast Cancer Liver Metastases
New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection
Can Deep Learning Provide a CT-Less Alternative for Attenuation Compensation with SPECT MPI?
Employing AI in Detecting Subdural Hematomas on Head CTs: An Interview with Jeremy Heit, MD, PhD
Pertinent Insights into the Imaging of Patients with Marfan Syndrome
Related Content
© 2025 MJH Life Sciences

All rights reserved.