The Solo Practice of Medical Physics in Radiation Oncology

10.37206/80 ◽  
2003 ◽  
Author(s):  
Per H. Halvorsen ◽  
Julie F. Dawson ◽  
Martin W. Fraser ◽  
Geoffrey S. Ibbott ◽  
Bruce R. Thomadsen
Keyword(s):  
Radiation Oncology ◽  
Medical Physics ◽  
Solo Practice

scholarly journals Development, Implementation, and Initial Participant Feedback of an Online Anatomy and Radiology Contouring Bootcamp in Radiation Oncology

2021 ◽  
Vol 8 ◽  
pp. 238212052110377
Author(s):  
Paige Eansor ◽  
Madeleine E. Norris ◽  
Leah A. D’Souza ◽  
Glenn S. Bauman ◽  
Zahra Kassam ◽  
...  
Keyword(s):  
Learning Environment ◽  
Radiation Oncology ◽  
Medical Physics ◽  
Course Completion ◽  
Online Course ◽  
Design Development ◽  
Subject Knowledge ◽  
Course Content ◽  
Face To Face ◽  
The Creation

BACKGROUND The Anatomy and Radiology Contouring (ARC) Bootcamp was a face-to-face (F2F) course designed to ensure radiation oncology residents were equipped with the knowledge and skillset to use radiation therapy techniques properly. The ARC Bootcamp was proven to be a useful educational intervention for improving learners’ knowledge of anatomy and radiology and contouring ability. An online version of the course was created to increase accessibility to the ARC Bootcamp and provide a flexible, self-paced learning environment. This study aimed to describe the instructional design model used to create the online offering and report participants’ motivation to enroll in the course and the online ARC Bootcamp's strengths and improvement areas. METHODS The creation of the online course followed the analysis, design, development, implementation, and evaluation (ADDIE) framework. The course was structured in a linear progression of locked modules consisting of radiology and contouring lectures, anatomy labs, and integrated evaluations. RESULTS The online course launched on the platform Teachable in November 2019, and by January 2021, 140 participants had enrolled in the course, with 27 participants completing all course components. The course had broad geographic participation with learners from 19 different countries. Of the participants enrolled, 34% were female, and most were radiation oncology residents (56%), followed by other programs (24%), such as medical physics residents or medical students. The primary motivator for participants to enroll was to improve their subject knowledge/skill (44%). The most common strength identified by participants was the course's quality (41%), and the most common improvement area was to incorporate more course content (41%). CONCLUSIONS The creation of the online ARC Bootcamp using the ADDIE framework was feasible. The course is accessible to diverse geographic regions and programs and provides a flexible learning environment; however, the course completion rate was low. Participants’ feedback regarding their experiences will inform future offerings of the online course.

Report of the ACPSEM radiation oncology medical physics workforce modelling project task group

2021 ◽  
Author(s):  
Scott Crowe ◽  
Trent Aland ◽  
Lotte Fog ◽  
Lynne Greig ◽  
Lynsey Hamlett ◽  
...  
Keyword(s):  
Radiation Oncology ◽  
Medical Physics ◽  
Task Group

scholarly journals Grand challenges for medical physics in radiation oncology

2020 ◽  
Vol 153 ◽  
pp. 7-14 ◽  
Author(s):  
Claudio Fiorino ◽  
Robert Jeraj ◽  
Catharine H. Clark ◽  
Cristina Garibaldi ◽  
Dietmar Georg ◽  
...  
Keyword(s):  
Radiation Oncology ◽  
Medical Physics ◽  
Grand Challenges

scholarly journals Radiation oncology alternative payment model to medical physics profession: More benefits than detriments

2019 ◽  
Vol 20 (12) ◽  
pp. 6-9 ◽  
Author(s):  
Todd Pawlicki ◽  
Eric Ford ◽  
Yi Rong
Keyword(s):  
Radiation Oncology ◽  
Medical Physics ◽  
Payment Model

scholarly journals The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

Oncology ◽  
2020 ◽  
pp. 1-11
Author(s):  
Tucker J. Netherton ◽  
Carlos E. Cardenas ◽  
Dong Joo Rhee ◽  
Laurence E. Court ◽  
Beth M. Beadle
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Treatment Planning ◽  
Radiation Oncology ◽  
Radiation Treatment ◽  
Medical Physics ◽  
External Beam Radiotherapy ◽  
Radiation Treatment Planning ◽  
Planning Team ◽  
The Future

<b><i>Background:</i></b> The future of artificial intelligence (AI) heralds unprecedented change for the field of radiation oncology. Commercial vendors and academic institutions have created AI tools for radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI’s impact upon the future landscape of radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated? <b><i>Summary:</i></b> In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy treatment planning and how these deep learning-based tools and other AI-based tools will impact members of the radiation treatment planning team. <b><i>Key Messages:</i></b> Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the radiation treatment planning team. However, accessibility to these tools will be determined by each clinic’s access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the radiation treatment planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated treatment planning tools, may refocus tasks performed by the treatment planning team and may potentially reduce resource-related burdens for clinics with limited resources.

Assessment of Radiation Oncology Medical Physics Residents: The London, Ontario (Canada) Experience

2012 ◽  
pp. 181-198
Author(s):  
Jacob Van Dyk ◽  
Jerry J. Battista
Keyword(s):  
Radiation Oncology ◽  
Medical Physics

scholarly journals COVID-19 pandemic planning: considerations for radiation oncology medical physics

2020 ◽  
Vol 43 (2) ◽  
pp. 473-480 ◽  
Author(s):  
May Whitaker ◽  
Tomas Kron ◽  
Matthew Sobolewski ◽  
Richard Dove
Keyword(s):  
Radiation Oncology ◽  
Medical Physics ◽  
Pandemic Planning

scholarly journals Radiation Oncology and Medical Physics

2015 ◽  
Vol 2015 ◽  
pp. 1-3 ◽  
Author(s):  
Tsair-Fwu Lee ◽  
Jack Yang ◽  
Cheng-Shie Wuu ◽  
An Liu ◽  
Fu-Min Fang ◽  
...  
Keyword(s):  
Radiation Oncology ◽  
Medical Physics
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