Utilising an accelerated Delphi process to develop consensus on the requirement and components of a pre-procedural core robotic surgery curriculum

Joshua Richard Burke^{1

2

3}, Christina A Fleming^{4

5}, Martin King^{4

6}, Charlotte El-Sayed^{7

8}, William S Bolton⁹, Chris Munsch⁷, Deena Harji^{10

11}, Simon P Bach^{10

8}, Justin W Collins^{12

13}

Affiliations

¹ The Association of Surgeons in Training, Royal College of Surgeons of England, London, England, UK. joshburke@doctors.org.uk.
² Robotics and Digital Surgery Initiative, Royal College of Surgeons of England, London, England. joshburke@doctors.org.uk.
³ Leeds Institute Medical Research, University of Leeds, Leeds, UK. joshburke@doctors.org.uk.
⁴ The Association of Surgeons in Training, Royal College of Surgeons of England, London, England, UK.
⁵ The Royal College of Surgeons, Dublin, Ireland.
⁶ Craigavon Area Hospital, Craigavon, Northern Ireland.
⁷ Technology Enhanced Learning Directorate of Innovation, Digital and Transformation, Health Education England, London, England.
⁸ Academic Department of Surgery, University of Birmingham, Birmingham, UK.
⁹ Leeds Institute Medical Research, University of Leeds, Leeds, UK.
¹⁰ Robotics and Digital Surgery Initiative, Royal College of Surgeons of England, London, England.
¹¹ Department of Colorectal Surgery, Manchester University NHS Foundation Trust, Manchester, UK.
¹² University College London, Division of Surgery and Interventional Science, Research Department of Targeted Intervention, London, UK.
¹³ Wellcome/ESPRC Centre for Interventional and Surgical Sciences (WEISS), UK, University College London, London, UK.

PMID: 36757562
PMCID: PMC9909133
DOI: 10.1007/s11701-022-01518-2

Utilising an accelerated Delphi process to develop consensus on the requirement and components of a pre-procedural core robotic surgery curriculum

Joshua Richard Burke et al. J Robot Surg. 2023 Aug.

. 2023 Aug;17(4):1443-1455.

doi: 10.1007/s11701-022-01518-2. Epub 2023 Feb 9.

Authors

Affiliations

¹ The Association of Surgeons in Training, Royal College of Surgeons of England, London, England, UK. joshburke@doctors.org.uk.
² Robotics and Digital Surgery Initiative, Royal College of Surgeons of England, London, England. joshburke@doctors.org.uk.
³ Leeds Institute Medical Research, University of Leeds, Leeds, UK. joshburke@doctors.org.uk.
⁴ The Association of Surgeons in Training, Royal College of Surgeons of England, London, England, UK.
⁵ The Royal College of Surgeons, Dublin, Ireland.
⁶ Craigavon Area Hospital, Craigavon, Northern Ireland.
⁷ Technology Enhanced Learning Directorate of Innovation, Digital and Transformation, Health Education England, London, England.
⁸ Academic Department of Surgery, University of Birmingham, Birmingham, UK.
⁹ Leeds Institute Medical Research, University of Leeds, Leeds, UK.
¹⁰ Robotics and Digital Surgery Initiative, Royal College of Surgeons of England, London, England.
¹¹ Department of Colorectal Surgery, Manchester University NHS Foundation Trust, Manchester, UK.
¹² University College London, Division of Surgery and Interventional Science, Research Department of Targeted Intervention, London, UK.
¹³ Wellcome/ESPRC Centre for Interventional and Surgical Sciences (WEISS), UK, University College London, London, UK.

PMID: 36757562
PMCID: PMC9909133
DOI: 10.1007/s11701-022-01518-2

Abstract

Robot-assisted surgery (RAS) continues to grow globally. Despite this, in the UK and Ireland, it is estimated that over 70% of surgical trainees across all specialities have no access to robot-assisted surgical training (RAST). This study aimed to provide educational stakeholders guidance on a pre-procedural core robotic surgery curriculum (PPCRC) from the perspective of the end user; the surgical trainee. The study was conducted in four Phases: P1: a steering group was formed to review current literature and summarise the evidence, P2: Pan-Specialty Trainee Panel Virtual Classroom Discussion, P3: Accelerated Delphi Process and P4: Formulation of Recommendations. Forty-three surgeons in training representing all surgical specialties and training levels contributed to the three round Delphi process. Additions to the second- and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement. There was 100% response from all three rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of > 0.8. There was 97.7% agreement that a standardised PPCRC would be advantageous to training and that, independent of speciality, there should be a common approach (95.5% agreement). Consensus was reached in multiple areas: 1. Experience and Exposure, 2. Access and context, 3. Curriculum Components, 4 Target Groups and Delivery, 5. Objective Metrics, Benchmarking and Assessment. Using the Delphi methodology, we achieved multispecialty consensus among trainees to develop and reach content validation for the requirements and components of a PPCRC. This guidance will benefit from further validation following implementation.

Keywords: Curriculum; Delphi method; Robot-assisted surgery; Robotic surgery; Training.

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Conflict of interest statement

ASiT has an educational and sponsorship relationship with CMR Surgical. DH has received funding from Intuitive Foundation. Simon Bach receives research funding from Intuitive Surgical Inc, USA. CF is in receipt of a European Society of Coloproctology Robotic Surgery Fellowship, supported by Intuitive Surgical Inc. JWC is an associate medical director at CMR Surgical Ltd, UK.

Figures

**Fig. 1**
There was 95.3% agreement that following successful completion of core robotic surgery training, a trainee should be able to proceed to procedural training in their preferred specialty

**Fig. 2**
PRISMA flow diagram summarising the study selection process. *PRISMA* Preferred Reporting Items for Systematic Reviews and Meta-analyses

**Fig. 3**
Training stage and preferred surgical speciality (n = 43)

See this image and copyright information in PMC

References

1. Khalid S, Goldenberg M, Grantcharov T, Taati B, Rudzicz F. Evaluation of deep learning models for identifying surgical actions and measuring performance. JAMA Netw Open. 2020;3(3):e201664–e201664. - PubMed
1. Annual Report 2019 - Intuitive Surgical. Availanble from: https://isrg.gcs-web.com/static-files/31b5c428-1d95-4c01-9c85-a7293bac5e05. Accessed 24 Jan 2023
1. Intuitive Announces First Quarter Earnings | Intuitive Surgical. Available from: https://isrg.intuitive.com/news-releases/news-release-details/intuitive-...
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Utilising an accelerated Delphi process to develop consensus on the requirement and components of a pre-procedural core robotic surgery curriculum

Affiliations

Utilising an accelerated Delphi process to develop consensus on the requirement and components of a pre-procedural core robotic surgery curriculum

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

MeSH terms

LinkOut - more resources

Full Text Sources