Cautiously optimistic: A survey of radiation oncology professionals' perceptions of automation in radiotherapy planning

Vikneswary Batumalai; Michael G Jameson; Odette King; Rhiannon Walker; Chelsea Slater; Kylie Dundas; Glen Dinsdale; Andrew Wallis; Cesar Ochoa; Rohan Gray; Phil Vial; Shalini K Vinod

doi:10.1016/j.tipsro.2020.10.003

Cautiously optimistic: A survey of radiation oncology professionals' perceptions of automation in radiotherapy planning

Tech Innov Patient Support Radiat Oncol. 2020 Nov 17:16:58-64. doi: 10.1016/j.tipsro.2020.10.003. eCollection 2020 Dec.

Authors

Vikneswary Batumalai^{1

2

3}, Michael G Jameson^{1

2

3}, Odette King¹, Rhiannon Walker¹, Chelsea Slater¹, Kylie Dundas^{1

2

3}, Glen Dinsdale¹, Andrew Wallis¹, Cesar Ochoa¹, Rohan Gray¹, Phil Vial^{1

4}, Shalini K Vinod^{1

2

3}

Affiliations

¹ Department of Radiation Oncology, South Western Sydney Local Health District, New South Wales, Australia.
² Ingham Institute for Applied Medical Research, New South Wales, Australia.
³ South Western Sydney Clinical School, University of New South Wales, New South Wales, Australia.
⁴ School of Medical Physics, University of Sydney, New South Wales, Australia.

Abstract

Introduction: While there is evidence to show the positive effects of automation, the impact on radiation oncology professionals has been poorly considered. This study examined radiation oncology professionals' perceptions of automation in radiotherapy planning.

Method: An online survey link was sent to the chief radiation therapists (RT) of all Australian radiotherapy centres to be forwarded to RTs, medical physicists (MP) and radiation oncologists (RO) within their institution. The survey was open from May-July 2019.

Results: Participants were 204 RTs, 84 MPs and 37 ROs (response rates ∼10% of the overall radiation oncology workforce). Respondents felt automation resulted in improvement in consistency in planning (90%), productivity (88%), quality of planning (57%), and staff focus on patient care (49%). When asked about perceived impact of automation, the responses were; will change the primary tasks of certain jobs (66%), will allow staff to do the remaining components of their job more effectively (51%), will eliminate jobs (20%), and will not have an impact on jobs (6%). 27% of respondents believe automation will reduce job satisfaction. 71% of respondents strongly agree/agree that automation will cause a loss of skills, while only 25% strongly agree/agree that the training and education tools in their department are sufficient.

Conclusion: Although the effect of automation is perceived positively, there are some concerns on loss of skillsets and the lack of training to maintain this. These results highlight the need for continued education to ensure that skills and knowledge are not lost with automation.

Keywords: Artificial intelligence; Automation; Education; Perception; Radiation oncology; Survey; Treatment planning.