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Using MCDA to evaluate robotic surgery in the NHS

The Health Economics Unit was funded by the Midlands and Lancashire Commissioning Support Unit (MLCSU) to explore the use of multi-criteria decision analysis (MCDA) to evaluate the economic implications of robotic surgery in the NHS, beyond traditional cost-effectiveness analysis.

Robotic surgery is a type of minimally invasive surgery where a surgeon operates on a patient using interactive, mechanical arms. The system is considered to offer more precision, flexibility and control than manual surgery. It is ergonomically beneficial too, removing the need for a surgeon to be hunched over an operating table for hours on end. For patients, robot-assisted surgery can result in faster recovery times and less time spent in hospital.

This type of surgery has evolved into a global industry and more versatile models are now arriving on the market, resulting in an increasing number of procedures being performed by robotic surgery in the NHS.

Our objective was to create a case study on using MCDA to evaluate a particular application of robotic surgery in the NHS.

Our approach

Existing economic evaluations of robotic surgery have mostly relied on traditional frameworks, such as cost effectiveness analysis or budget impact analysis. These frameworks provide decision makers with a limited range of decision criterion, such as the incremental cost effectiveness ratio.

MCDA, on the other hand, readily accommodates a wide range of criterion, such as those characteristics of medical devices below:

  • The ‘learning curve’. The performance of a robotic device depends in part on how familiar the surgeon is with the equipment; performance may be expected to increase as the user gains more experience
  • Surgeon/device interaction. Surgeons spend a significant amount of time interacting with robotic devices, meaning that the usability and comfort provided by the devices is an important consideration
  • Organisational impact. The introduction of a surgical robot may require substantial amounts of training or investment in infrastructure.

We worked with subject matter experts on this project to help us gather judgments on the importance of different decision criteria, allowing scores in these criteria to be combined into a total score for each option. Total scores were then ranked, leading to a justified final verdict on the most valuable option.


This project tests the scalability of MCDA and the use of associated tools in evaluating innovation, such as medical technology. It has also given our economists an opportunity to apply alternative decision analytic techniques for complex decision making in healthcare and enabled us to develop new relationships with academic colleagues and other collaborators.

Our work will contribute towards the literature on demonstrating the applicability of MCDA to evaluating technology and more broadly, the application of MCDA in healthcare. We also produced and published a literature review, focusing on criteria to be used by stakeholders for assessing the value of robotics in the MCDA framework.

We aim to publish our findings from the full MCDA in an appropriate journal and will share findings across our network.

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