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CAEP FEI | ImageSim – An Online Image Interpreting Learning System

In Featured, Featured Education Innovations (FEI) by Kathy BoutisLeave a Comment

Lee is a second year pediatric emergency medicine fellow looking at a pediatric ankle x-ray. She is unsure if what she sees is a normal variant like a growth plate or ossification centre, or a fracture. Lee feels like her orthopedic knowledge is weak and wishes there was a tool to help her better learn to interpret visual tests such as x-rays and ECGs before transitioning to staff.

This Feature Educational Innovation (FEI), titled, “ImageSim – An Online Image Interpreting Learning System” was originally posted by the CAEP EWG FEI Team in 2018 and answers the question: “How can we train physicians to interpret visually diagnosed medical tests? ” A PDF version is available.

Description of Innovation

Visually diagnosed medical tests (such as radiographs, electrocardiograms) 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 (radiologists, cardiologists) 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.

ImageSim​1​ 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. The 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, these images are presented as they would be encountered in practice, and include a normal to abnormal radiograph ratio (with a spectrum of pathology) reflective of day-to-day practice.

Setting

This is a web-based innovation found here.

Required Resources

The creators needed the technical expertise to create the software platform and the skills to couple this to a database where every click became a recorded piece of data to allow for sophisticated learning analytics. Content expertise in cognitive psychology, medical education, and image interpretation were needed. Furthermore, several thousand high resolution diagnostic images that were quality checked and formatted for software presentation were obtained.

Finally, the authors needed expertise for continuing medical education, performance-based competency, and promotion of our education platform.

Education Theories/Frameworks

This education innovation uses cognitive simulation, deliberate practice​2​, and performance-based competency.​3​

Lessons Learned

High quality work takes time – many years of time – before an innovation can be derived, developed, validated, and then launched as an educationally valuable tool. It means you need a team that understands and can invest in this commitment and not give up despite the many barriers that once faces in this multi-year journey. But, at the end, if it helps us be better clinicians, it is all worth it. And, it is wonderful seeing the international community learning from ImageSim.

Bottom Line

ImageSim aims to increase health care professionals’ accuracy in the interpretation of visually diagnosed tests with the goal of improved health outcomes. It offers exposure to hundreds of cases – an experience that would take years to accomplish from clinical exposure alone.

ImageSim​1​ provides courses for Continuing Medical Education and Competency-Based Training. It is CME credited for level three credits with the Royal College of Physicians and Surgeons and College of Family Physicians of Canada. There are currently 350 active CME users and 11 emergency medicine training programs that are using this platform to improve skills in image interpretation.

Does your institution use any techniques to teach trainees ECGs and radiographs?

How could a software like ImageSim be incorporated into your curriculum?

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More about CAEP FEI

This post was originally authored for the Canadian Association of Emergency Physicians (CAEP) Feature Educational Innovations project sponsored by the CAEP Academic Section’s Education Working Group and edited by Drs. Teresa Chan and Julien Poitras. CAEP members receive FEI each month in the CAEP Communiqué. CanadiEM will be reposting some of these summaries, along with a case/contextualizing concept to highlight some recent medical education literature that is relevant to our nation’s teachers.

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References

  1. 1.
    Boutis K, Pecarcic M, Pusic M. MP20: ImageSim – performance-based medical image interpretation learning system. CJEM. May 2018:S47-S47. doi:10.1017/cem.2018.174
  2. 2.
    Pusic M, Pecaric M, Boutis K. How Much Practice Is Enough? Using Learning Curves to Assess the Deliberate Practice of Radiograph Interpretation. Academic Medicine. June 2011:731-736. doi:10.1097/acm.0b013e3182178c3c
  3. 3.
    Boutis K, Lee M, Pusic M, et al. LO41: Competency-based learning of pediatric musculoskeletal radiographs. CJEM. May 2018:S21-S21. doi:10.1017/cem.2018.103
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Kathy Boutis

Dr. Kathy Boutis is a pediatric emergency physician at The Hospital for Sick Children (SickKids), a Senior Associate Scientist in the Child Health Evaluative Sciences Program at SickKids Research Institute, and an Associate Professor with the University of Toronto.

Chirag Bhat

Chirag Bhat is an Emergency Medicine resident at the University of Ottawa. He has interests in medical education and toxicology. He is a basketball fan and cheers for the Toronto Raptors.