Doctor discusses x-ray with father and little girl.

Doctor discusses x-ray with father and little girl.

Image SIM: An Online Learning Model

ImageSim provides a comprehensive and evidence-based on-line education system that teaches health care professionals the interpretation of visually diagnosed medical tests using the concepts of deliberate practice and simulation.

Visually diagnosed medical tests (e.g. radiographs) are the most commonly ordered tests in front-line medicine. As such, front-line health care professionals are faced with the task of learning the skill of interpreting these images to an expert performance level by the time they provide opinions that guide patient management decisions. However, discordant interpretations of these images between front-line physicians and expert counterparts (e.g radiologists) is a common cause of medical error. In pediatrics, this problem is even greater due to the changing physiology with age leading to increased risk of interpretation errors.

The ImageSIM learning model includes sustained active practice of hundreds of cases where the learner is forced to commit to diagnosis for every case and then receives immediate specific feedback on their interpretation so that the participant instantly learns from each case. Importantly, we have simulated these images as we encounter them in practice, and included a normal to abnormal radiograph ratio (with a spectrum of pathology) reflective of our day-to-day practice. Our research to date shows that this learning method works – and all physicians at varying levels of expertise had significantly increased their accuracy in image interpretation. This system started with pediatric musculoskeletal images and in the next 6-12 months pediatric chest X-ray, point-of-care ultrasound, and the pediatric pre-pubertal female genital examination will be launched.


  1. Boutis K, Cano S, Pecaric M, et al. Interpretation Difficulty of Normal versus Abnormal Radiographs Using a Paediatric Example. CMEJ. 2016;In press.
  2. Pusic M, Chiaramonte R, Pecaric M, Boutis K. A Simple Method for Improving Self-Assessment While Learning Radiograph Interpretation. Med Educ. 2015;49(8):838-46.
  3. Pusic M, Boutis K, Hatala R, Cook D. Learning curves: A Primer for Health Professions Education. Acad Med. 2014;90(8):1034-1042
  4. Beckstead J, Boutis K, Pecaric M, Pusic M. The Influences of Sequential Effects in Randomized Online Learning Tasks. Applied Cognition. 2014; 27(5):625-632
  5. Boutis K, Pecaric M, Ridley J, Andrews J, Gladding S, Pusic M. Hinting Strategies for Improving the Efficiency of Medical Student Learning of Deliberately Practiced Web-based Radiographs. Med Educ. 2013;47(9):877-887.
  6. Pusic MV, Andrews JS, Kessler DO, Boutis K. Determining the optimal case mix of abnormals to normals for learning radiograph interpretation: a randomized control trial. Medical Education. 2012;46(3):289-298.
  7. Pusic MV, Kessler D, Szyld D, Kalet A, Pecaric M, Boutis K. Experience Curves as an Organizing Framework for Deliberate Practice in Emergency Medicine Learning. Acad Emerg Med. 2012;19(12):1476-80.
  8. Pusic M, Pecaric M, Boutis K. How Much Practice is Enough? Using Learning Curves to Assess the Deliberate Practice of Radiograph Interpretation. Acad Med. 2011;86(6):731-736.
  9. Boutis K, Pecaric M, Pusic M. Using Signal Detection Theory to Model Changes In Serial Learning of Radiological Image Interpretation. Adv Health Sci Educ Theory Pract. 2010;15:647-658.
  10. Boutis K, Pecaric M, Pusic M. Selecting Radiographs For Teaching The Interpretation of X-Rays Based On Ratings by the Target Population. Med Educ. 2009;43(5):434-441.