Evidence generation plan for robot-assisted surgery for soft tissue procedures

6 Implementation considerations

The following considerations around implementing the evidence generation process have been identified through working with system partners:

Evidence generation

  • The companies could collect and analyse outcome data stratified by age to ensure that the evidence is informative in all clinically important subgroups, including children.

  • Accessing the Cancer Outcomes and Services Dataset (COSD) has an application process that may take many months. Companies should plan adequate time to apply to use the data.

  • Information about effectiveness of different robot-assisted surgery (RAS) systems across different types of procedures should be collected to enable comparison between them. When appropriate, steps should be taken to collect outcome information over a longer period beyond the 3-year evidence generation period.

Equalities

  • Distance travelled by patients could be a barrier to access to RAS technologies. This is because of RAS technologies being available in larger trust hospitals doing more complicated surgical procedures. Care should be taken to ensure equitable access to RAS technologies. The NHS England robot-assisted surgery steering group may be influential in moderating the geographical placement of additional robotic systems, and the availability of training, resources and staff to implement RAS services, with national strategy going forward. They are actively analysing and mapping current robot-assisted surgery provision in England. A key priority will be equitable provision of RAS based on need rather than current configuration.

System considerations

  • While designing the training curriculum for RAS, care should be taken to ensure comparable surgical skills such as laparoscopic and open surgical skills are not lost.

  • The evidence generation process is most likely to succeed with dedicated and incentivised research staff to reduce the burden on NHS staff, and by using suitable real-world data to collect information when possible.

ISBN: 978-1-4731-6924-1

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