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Competency Based Learning of Pediatric Musculoskeletal Radiographs Using ImageSim


Abstract

​Background: Competency in pediatric musculoskeletal (p-MSK) radiograph interpretation amongst emergency medicine residents is sub-optimal. We have developed ImageSim, a validated education system that uses an “assessment for learning” cognitive simulation platform. The p-MSK course includes seven body regions and cases are practiced using deliberate practice with feedback and continuous assessment (https://imagesim.com/course-information/demo/). Competency is achieved when a participant reaches a pre-set performance benchmark. However, before this can be launched as a residency learning tool standard to help bridge the existing knowledge gap, the education experience for residents should be characterized.

Research Questions: In participating residents, what was the median number of cases required to achieve competency per module and what percent of residents achieved competency in all seven modules? Further, what knowledge gains did the residents experience from baseline to competency and how long did it take them to complete the cases?

Methods: Thirty-five pediatric emergency medicine residents participated for 12 months in this cross-sectional study. Participants did cases until they reached competency, defined as at least 80% accuracy, sensitivity and specificity.

Results: Overall, the median number of cases to competency was 118 (min 56, max 756). The median number of cases to competency per specific module was as follows: skull 67 (56, 129), shoulder 60 (55, 172), elbow 70 (66, 214), forearm-hand 56 (56, 121), pelvis-femur 68 (57, 121), knee-tib/fib 93 (72, 213), and ankle-foot 410 (128, 756), p < 0.001. Eight-five percent of residents completed competency in all seven modules. The mean increase in accuracy from baseline to competency was 13% [95% CI 10, 15)]. The mean time on case 35.8 (SD 0.45) seconds.

Conclusions: Competency was achieved on average in about 120 cases or one hour per module, except for the ankle case-set. Accuracy increased to a competency standard for most participants. Future research could explore the effectiveness of this learning intervention on patient outcomes.
 

Poster
non-peer-reviewed

Competency Based Learning of Pediatric Musculoskeletal Radiographs Using ImageSim


Author Information

Kathy Boutis Corresponding Author

Pediatrics, The Hospital for Sick Children, University of Toronto

Jennifer Stimec

Department of Medical Imaging/ University of Toronto, Hospital for Sick childr


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  • Author Information
    Kathy Boutis Corresponding Author

    Pediatrics, The Hospital for Sick Children, University of Toronto

    Michelle L. Lee
    Martin Pusic
    Martin Pecaric
    Jennifer Stimec

    Department of Medical Imaging/ University of Toronto, Hospital for Sick childr

    Benoit Carriere
    Andrew Dixon
    Poster Information
    Meeting

    Sim Expo 2017 November 30, 2017 - December 03, 2017

    Publication history

    Received by Cureus: October 31, 2017
    Published: November 30, 2017

    License

    This is an open access poster distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

​Background: Competency in pediatric musculoskeletal (p-MSK) radiograph interpretation amongst emergency medicine residents is sub-optimal. We have developed ImageSim, a validated education system that uses an “assessment for learning” cognitive simulation platform. The p-MSK course includes seven body regions and cases are practiced using deliberate practice with feedback and continuous assessment (https://imagesim.com/course-information/demo/). Competency is achieved when a participant reaches a pre-set performance benchmark. However, before this can be launched as a residency learning tool standard to help bridge the existing knowledge gap, the education experience for residents should be characterized.

Research Questions: In participating residents, what was the median number of cases required to achieve competency per module and what percent of residents achieved competency in all seven modules? Further, what knowledge gains did the residents experience from baseline to competency and how long did it take them to complete the cases?

Methods: Thirty-five pediatric emergency medicine residents participated for 12 months in this cross-sectional study. Participants did cases until they reached competency, defined as at least 80% accuracy, sensitivity and specificity.

Results: Overall, the median number of cases to competency was 118 (min 56, max 756). The median number of cases to competency per specific module was as follows: skull 67 (56, 129), shoulder 60 (55, 172), elbow 70 (66, 214), forearm-hand 56 (56, 121), pelvis-femur 68 (57, 121), knee-tib/fib 93 (72, 213), and ankle-foot 410 (128, 756), p < 0.001. Eight-five percent of residents completed competency in all seven modules. The mean increase in accuracy from baseline to competency was 13% [95% CI 10, 15)]. The mean time on case 35.8 (SD 0.45) seconds.

Conclusions: Competency was achieved on average in about 120 cases or one hour per module, except for the ankle case-set. Accuracy increased to a competency standard for most participants. Future research could explore the effectiveness of this learning intervention on patient outcomes.
 

Kathy Boutis, M.D., M.Sc.

Pediatrics, The Hospital for Sick Children, University of Toronto

For correspondence:
kathy.boutis@sickkids.ca

Michelle L. Lee

Martin Pusic

Martin Pecaric

Jennifer Stimec

Department of Medical Imaging/ University of Toronto, Hospital for Sick childr

Benoit Carriere

Andrew Dixon

Kathy Boutis, M.D., M.Sc.

Pediatrics, The Hospital for Sick Children, University of Toronto

For correspondence:
kathy.boutis@sickkids.ca

Michelle L. Lee

Martin Pusic

Martin Pecaric

Jennifer Stimec

Department of Medical Imaging/ University of Toronto, Hospital for Sick childr

Benoit Carriere

Andrew Dixon

Kathy Boutis, M.D., M.Sc.

Pediatrics, The Hospital for Sick Children, University of Toronto

For correspondence:
kathy.boutis@sickkids.ca

Michelle L. Lee

Martin Pusic

Martin Pecaric

Jennifer Stimec

Department of Medical Imaging/ University of Toronto, Hospital for Sick childr

Benoit Carriere

Andrew Dixon

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