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Professional implications of introducing artificial intelligence in healthcare: an evaluation using radiation medicine as a testing ground

Published online by Cambridge University Press:  03 October 2018

Caitlin Gillan ,
Emily Milne ,
Nicole Harnett ,
Thomas G. Purdie ,
David A. Jaffray  and
Brian Hodges
Caitlin Gillan*
Affiliation:
Princess Margaret Cancer Centre, Toronto, Ontario, Canada University of Toronto, Toronto, Ontario, Canada
Emily Milne
Affiliation:
Princess Margaret Cancer Centre, Toronto, Ontario, Canada
Nicole Harnett
Affiliation:
Princess Margaret Cancer Centre, Toronto, Ontario, Canada University of Toronto, Toronto, Ontario, Canada
Thomas G. Purdie
Affiliation:
Princess Margaret Cancer Centre, Toronto, Ontario, Canada University of Toronto, Toronto, Ontario, Canada TECHNA Institute, Toronto, Ontario, Canada
David A. Jaffray
Affiliation:
Princess Margaret Cancer Centre, Toronto, Ontario, Canada University of Toronto, Toronto, Ontario, Canada TECHNA Institute, Toronto, Ontario, Canada
Brian Hodges
Affiliation:
University of Toronto, Toronto, Ontario, Canada University Health Network, Toronto, Ontario, Canada
*
Author for correspondence: Caitlin Gillan, Princess Margaret Cancer Centre, 610 University Ave, Toronto, M5G2M9. E-mail: Caitlin.gillan@rmp.uhn.ca
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Abstract

Aim

This study will evaluate radiation medicine professionals’ perceptions of clinical and professional risks and benefits, and the evolving roles and responsibilities with artificial intelligence (AI).

Methods

Radiation oncologists (ROs), medical physicists (MPs), treatment planners (TP-RTTs) and treatment delivery radiation therapists (TD-RTTs) at a cancer centre in preliminary stages of implementing an AI-enabled treatment planning system were invited to participate in uniprofessional focus groups. Semi-structured scripts addressed the perceptions of AI, including thoughts regarding changing roles and competencies. Sessions were audiorecorded, transcribed and coded thematically through consensus-building.

Results

A total of 24 participants (four ROs, five MPs, seven TP-RTTs and eight TD-RTTs) were engaged in four focus groups of 58 minutes average duration (range 54–61 minutes). Emergent themes addressed AI’s impact on quality of care, changing professional tasks and changing competency requirements. Time-consuming repetitive tasks such as delineating targets, generating treatment plans and quality assurance were thought conducive to offloading to AI. Outcomes data and adaptive planning would be incorporated into clinical decision-making. Changing workload would necessitate changing skills, prioritising plan evaluation over generation and increasing interprofessional communication. All groups discussed AI reducing the need for TP-RTTs, though displacement was thought more likely than replacement.

Conclusions

It is important to consider how professionals perceive AI to be proactive in informing change, as gains in quality and efficiency will require new workflows, skills and education.

Keywords

artificial intelligencecompetencyeducation, practiceprofessionaltreatment planning
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 18 , Issue 1 , March 2019 , pp. 5 - 9
Copyright
© Cambridge University Press 2018 

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Footnotes

Cite this article: Gillan C, Milne E, Harnett N, Purdie TG, Jaffray DA, Hodges B. (2019) Professional implications of introducing artificial intelligence in healthcare: an evaluation using radiation medicine as a testing ground. Journal of Radiotherapy in Practice18: 5–9. doi: 10.1017/S1460396918000468

References

1. Copeland, BJ. Artificial intelligence. https://www.britannica.com/technology/artificial-intelligence Accessed on 18th January 2018.Google Scholar
2. Purdie, TG, Dinniwell, RE, Fyles, A et al. Automation and intensity modulated radiation therapy for individualized high-quality tangent breast treatment plans. Int J Radiat Oncol Biol Phys 2014; 90: 688–695.Google Scholar
3. Brynjolfsson, E, McAfee, A. Race Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. Lexington MA: Digital Frontier Press, 2012.Google Scholar
4. Johnson, MJ, May, C. Promoting professional behaviour change in healthcare: what interventions work and why? A theory-led overview of systematic reviews. Br Med J Open 2015; 5: e008592.Google Scholar
5. Callan, VJ, Gallois, C, Mayhew, MG, Grice, TA, Tluchowska, M, Boyce, R. Restructuring the multi-professional organisation: professional identity and adjustment to change in a public hospital. J Health Hum Serv Admin 2007; 29 (4): 448–477.Google Scholar
6. McNeil, KA, Mitchell, RJ, Parker, V. Interprofessional practice and professional identity threat. Health Sociol Rev 2013; 22 (3): 291–307.Google Scholar
7. Mitchell, R, Parker, V, Giles, M. When do interprofessional teams succeed? Investigating the moderating roles of team and professional identity in interprofessional effectiveness. Hum Relat 2011; 64 (10): 1321–1343.Google Scholar
8. Medical Research Council. Developing and evaluating complex interactions: new guidance. Medical Research Council. Available at: https://www.mrc.ac.uk/documents/pdf/complex-interventions-guidance/ 2011. Accessed on 4th December 2017.Google Scholar
9. May, C, Finch, T, Mair, F et al. Understanding the implementation of complex interventions in health care: the normalization process model. BMC Health Serv Res 2007; 7: 148–154.Google Scholar
10. White, E, Kane, G. Radiation medicine practice in the image-guided radiation therapy era: new roles and new opportunities. Semin Radiat Oncol 2007; 17 (4): 298–305.Google Scholar
11. Gillan, C, Wiljer, D, Harnett, N, Briggs, K, Catton, P. Changing stress while stressing change: the role of interprofessional education in mediating stress in the introduction of a transformative technology. J Interprof Care 2010; 24 (6): 710–721.Google Scholar
12. Schein, E H. Career Dynamics: Matching Individual and Organisational Needs. Reading, MA: Addison-Wesley, 1978.Google Scholar