Quality over Speed - Robotics and Other Methods to Progress Healthcare System Outcomes

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The Big Updates from BASK 2023

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By Stuart Pearce & Stephen Key

This article is part of a series, you can find the previous three articles here:        
Maintaining Outcomes for Total Knee Arthroplasty – The Big Updates From BASK 2023 

Balance or Alignment – does it make a difference and what should be the priority? Functional and Procedural Outcome Differences      

Enabling Technology - How do we achieve an aligned and balanced knee?

Quality over speed- Robotics and other methods to progress healthcare system outcomes

In the final instalment of this series, discussing the talking points raised at BOA and BASK, we are going to summarise and develop the emanating topics from our previous discussions whilst hypothesising what this could mean for the wider healthcare system. Our look back into the data of how a total knee arthroplasty (TKA) is performed and the impact this has on outcomes will be taken one step further, by looking at the impact this can have on an overall healthcare system, whether this be through theatre productivity or resource utilisation efficiencies.      

Quality over speed      

As discussed previously, if we are aiming for high quality joints with lower reoperation rates, even if the initial procedure takes a similar amount of time and uses a similar level of resource it will have greater value if the clinical outcomes improve.  Beyond the intra-operative metrics, more important are the post-surgical metrics of success. Clinical outcomes will determine if a patient will re-enter the healthcare setting sooner or more frequently. Reducing the reintervention rate following the primary procedure can have a significant impact on resource utilisation and tackling waiting lists.  How long a patient remains within the healthcare setting has a relational impact on both pathway cost and capacity limitations short term, so if length of stay (LOS) can be reduced by speedier recovery these can also benefit. Currently, research into the impact of robotics within TKA has illustrated that intra-operative changes are less drastic but the discharge and resource burden is significantly reduced; length of stay and  90-day readmission rates were reduced by 14% and 17% respectively, as well as a 74% greater likelihood of discharge to home, within this US based meta-analysis1. Financially this equates to a $4049 saving per patient at the 90-day point in the robotically assisted TKA group according to Pierce and Needham2, consistent with the trend seen by Hoeffel et al. who saw a 4% reduction in robotic TKA cost compared to manual at the same timepoint, although this difference wasn’t statistically significantly1.

What could this look like within the UK and what could this mean for healthcare as an overall system? With the volumes of orthopaedic patients on waiting lists and shortages in resources such as beds and finance, reduction in LOS and readmission would alleviate some of the economic and resource burden from the system allowing for more procedures to be done with the same number of bed spaces and staff, at a lower net cost.  

Value through precision and accuracy is at the heart of robotic pathways and other forms of pathway optimisation, with data suggesting that any increased time taken during theatre is repaid through quicker discharge, improved patient recovery and reduced cost in some instances also. This can be seen, for instance, in a study by Imielski et al. where thymectomy results were comparable across both open and robotic/thoracoscopic practises3, however changes were seen in the cost as general LOS and ICU stay with robotics were far less, which in turn reduced the healthcare burden. Reduction in the utilisation of these hospital facilities will allow for higher throughput of patients and enable greater procedural numbers with the same resource. Whilst at present speed in the operating room is comparable or in some cases longer, intra-operative efficiency can come with time and learning through experience. A robotic sleeve gastrectomy service described by Tousignant et al. is one such example4. Over 5 years there were some key learnings, with variability decreasing and time taken per procedure significantly reduced over time. Identification of the significant contributors to variation enabled this reduction, and refinement of the procedure during each identified element enabled these improvements. Some of these learnings are transferable to the orthopaedic arena, with learnings from experience of multiple cases  and identification of time-costing pinch points providing opportunity to reduce time taken per robotic procedure to bring this in line with current surgical times. So, even if initially open and robotic outcomes or timelines are longer or comparable, because robotic pathways remain in their infancy they could develop such as in this robotic gastrectomy service, and in turn produce more reproducible and reliable joints that also take equivalent or less time and incur less net health system cost?      

Utilisation of robotics is not the only way to improve the efficiency and value of a service. Assessment of an overall pathway, the team, training, environment and tools utilised in each step can undergo the same scrutiny as mentioned above within the analysis of the robotic gastrectomy service. Whilst we can identify key steps of a procedure that cause delay, it is also possible that late calling, bed-blocks, poor pre-operation preparation and other such steps can impact the ability of a patient to progress smoothly through a treatment pathway, or a team’s ability to run a full scheduled list. Work such as this has been done previously and produced significant improvements in pathways, including within the TKA and THA arena. A London trust was able to increase the complexity of patients and theatre efficiency whilst improving clinical outcomes and length of stay metrics through implementation of an 11-step protocol5. Having a team asses their behaviours led to identification of bottlenecks and through specific training for both patients and staff, as well as modification of perioperative procedures, the objectives could be achieved and implemented with positive impact on the service, healthcare team and consequentially the entire system.

In summary, we have highlighted the potential that harnessing data and technology could bring for not only patients within the TKA space, but also the healthcare system as a whole. At a time where the rate of change is so high within healthcare, and pressure continues to grow almost exponentially on a strained system, enabling technologies and the data behind them bring huge opportunities to improve and aid their evolution at every step of the way. Through utilisation of enabling technologies and data-driven value-based healthcare it is possible that we could see greater patient outcomes and productivity with the same level of resource currently at the health services’ disposal.       



Stuart PearceStuart Pearce PhD

Medical Affairs Johnson & Johnson MedTech

Stuart joined the organisation in 2020 following the end of his Doctorate studies at Barts and the London, University of London in 2019. Publishing on Aneurysms, Atherosclerosis and Medical Teaching during his studies he has continued to develop his knowledge of Medical Devices, Medical Technologies and their impact on the wider healthcare system. 


Stephen KeyStephen Key

UK and Ireland Medical Lead for DePuy Synthes

Formerly a consultant hip and knee surgeon in London, having completed undergraduate medical training at the University of Cambridge in 2004, specialist Orthopaedic training on The Royal London rotation, and fellowship training in hip and knee arthroplasty and soft tissue knee surgery in Australia and the UK.



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  1. Hoeffel D, Goldstein L, Intwala D, Kaindl L, Dineen A, Patel L, Mayle R. Systematic review and meta-analysis of economic and healthcare resource utilization outcomes for robotic versus manual total knee arthroplasty. J Robot Surg. 2023 Dec;17(6):2899-2910. doi: 10.1007/s11701-023-01703-x. Epub 2023 Oct 11. PMID: 37819597; PMCID: PMC10678833.
  2. Pierce J, Needham K, Adams C, Coppolecchia A, Lavernia C. Robotic arm-assisted knee surgery: an economic analysis. Am J Manag Care. 2020 Jul 1;26(7):e205-e210. doi: 10.37765/ajmc.2020.43763. PMID: 32672918.
  3. Imielski B, Kurihara C, Manerikar A, Chaudhary S, Kosterski S, Odell D, Kim S, Bharat A. Comparative effectiveness and cost-efficiency of surgical approaches for thymectomy. Surgery. 2020 Oct;168(4):737-742. doi: 10.1016/j.surg.2020.04.037. Epub 2020 Jul 5. PMID: 32641277; PMCID: PMC7816338.
  4. Tousignant MR, Liu X, Ershad Langroodi M, Jarc AM. Identification of Main Influencers of Surgical Efficiency and Variability Using Task-Level Objective Metrics: A Five-Year Robotic Sleeve Gastrectomy Case Series. Front Surg. 2022 May 2;9:756522. doi: 10.3389/fsurg.2022.756522. PMID: 35586509; PMCID: PMC9108208.
  5. Data on File