Robot-assisted surgery research framework

Published: October 2024. Next review: 2027.

Robot-assisted surgery in NSW public hospitals is undertaken within a research framework.

Robot-assisted surgery is recognised as a new technology. It has been subject to evidence reviews on its utility and broader use across NSW. These include reviews of clinical, economic and efficiency outcomes in line with the principles of value-based care. The robot-assisted surgery framework is based on the outcomes of these reviews.

The framework aims to:

create consistency in research methods for hospitals using robot-assisted surgery to contribute to evidence on utility and best uses of this technology
advance the evidence base on robot-assisted surgery in NSW.

The framework outlines the research priority areas for robot-assisted surgery within NSW Health. A set of key principles and a minimum dataset provide best practice guidance for all sites in NSW using robot-assisted surgery programs.

Research priority areas

1. Scan and share emerging evidence on robot-assisted surgery

The NSW Health Critical Intelligence Unit is reviewing and synthesising the evidence for robot-assisted surgery.

The currency of the living evidence table will ensure that emerging outcomes and uses, including broader indications, are quickly captured, synthesised and shared in a consistent way.

2. Contribute to the evidence base for robot-assisted surgery in NSW

NSW Health commits to:

developing an approach to assess the potential ergonomic benefits of robot-assisted surgery
a cost-benefit analysis to assess the true costs, opportunity costs and/or cost effectiveness of robot-assisted surgery
hosting a community of practice for clinicians using robot-assisted surgery to share learning, outcomes and experiences, as well as discuss potential priority areas
assessing the impact of robot-assisted surgery programs on training, recruitment and retention of workforce
assessing the impact of robot-assisted surgery programs on clinical trials and research programs
working with local health districts and sites on their local programs to assess and build robot-assisted surgery, where indicated.

To address knowledge gaps and build evidence, research could focus on:

acceptability implementation
comparative effectiveness
cost effectiveness and cost benefit analysis
impact on system flow and performance
longer-term follow up, including post-operative surveillance
potential new clinical indications.

3. Strengthen ongoing governance of robot-assisted surgery in NSW

To make sure robot-assisted surgery programs progress under a research and evaluation framework, local programs need strong, ongoing governance, even after they start. This should be managed as per local governance and approval processes.

Ongoing considerations for local robot-assisted surgery programs:

Update ethics committees and ethics applications when there are changes to the use of robot-assisted surgery, including new indications and procedures. ‘New’ refers to the introduction of procedures or indications that have not yet been performed at that site.
Respond to changes in the evidence base learnt from published literature, evidence reviews commissioned by NSW Health and/or learning from other NSW sites.
Consult with stakeholders who are impacted by the expanded scope, including executive, theatre management and division of surgery.
Be aware of any unintended impacts of changes to the research program, such as longer procedure times which impact operating theatre use and efficiency.
Plan for, and include new fields in, data processes so that process, experience and outcome measures can be collected and reported.
Share proposed changes to the local approach with community of practice members.
Develop and share an annual report, or equivalent, including local outcomes (aligned to the minimum dataset), improvement activities and a list of research publications (published or pending publication).

Note: Orthopaedic robots and navigations systems are excluded from the research framework.

Key principles for introduction of robot-assisted surgery

Robot-assisted surgery in NSW is based on a set of evidence-based key principles. The principles will be applied at all sites using robot-assisted surgery in NSW (including public private arrangements) to support consistent practice and reduce unwarranted variation.

Table 1: A summary of key principles for the introduction of robot-assisted surgery
Domain	Operational elements
Governance Staff, patient and organisational safety is supported by using robust governance arrangements to introduce a new health technology.	Clinical: Establish governance model and processes in line with NSQHS Standards. Corporate: Document financial, legal, ethical, risk and reporting requirements at the organisational level. Business: Document and endorse cost-benefit analysis and resource requirements.
Health technology The processes, resources and functional relationships of the proposed care pathway supporting the new technology are defined.	Establish clinical pathways to support implementation, including cohort identification, patient selection criteria, links to associated health services (rehabilitation, perioperative care, outpatient clinics, etc.) Secure required infrastructure, equipment and consumables. Consider equity of access to optimise availability of new health technologies across patient populations.
Patient-centred care Healthcare is delivered in partnership with patients with understanding and respect for individual goals, values and preferences.	Set up mechanisms to engage patients in shared decision making and capture patient feedback. Make tools available for individualised assessment of benefits and risk. Use consistent consent processes and patient information.
Skilled and capable workforce Staff are competent, confident and skilled in using the new technology.	Support staff to learn new skills with competency-based training program. Set up a credentialling process or pathway. Identify opportunities for ongoing education to maintain skills and develop advanced practice capabilities. Establish change management plans to facilitate introduction of new technology.
Evidence generation New health technologies are implemented at the local level within a standardised research framework, with a view to strengthening the body of supporting evidence.	Predetermine research study designs (questions, methods, controls and analyses) to generate evidence at a large scale. Use standardised data collection and analysis methods to ensure outcomes are comparable between sites. Use open-source software for data collection platforms and analytic methods to allow transparency and replication. Align patient privacy and confidentiality with the Health Records Information and Privacy Act 2002.
Learning systems for continuous improvement Commitment to quality and safety in health technology adoption is demonstrated through ongoing engagement in knowledge sharing and continuous improvement activities.	Design and agree mechanisms to share lessons learnt around implementation, evaluation and outcomes of the technology. Embed continuous improvement strategies in the program. Refer adverse events to the coordinating committee for clinical review and action. Use regular reporting, communication and feedback mechanisms to support participating sites in transitioning from research to business as usual.

Robot-assisted surgery data collection

All public hospitals using robot-assisted surgery in NSW are asked to collect the data outlined in the minimum dataset below. This contributes to the consistent assessment of outcomes in NSW.

Sites will be asked to share information quarterly to contribute to the statewide evidence base on robot-assisted surgery.

Participating sites will establish data collection processes. This may occur through local processes, or via a centralised data collection, depending on the consensus of sites.

Over time, data collection should reflect changes in indications and procedures for robot-assisted surgery, and contribute to system, experience and outcome measures in line with value-based care.

In addition to the minimum dataset of process indicators and clinical outcomes, data should also be collected on:

experiential data from clinicians and patients
total use of robot-assisted surgery
cost effectiveness
opportunity costs.

Table 2: An overview of the minimum dataset required by NSW hospitals for robot-assisted data collection
Domain	Data to collect
Demographics	Medical record number Date of birth Sex Body mass index Allergies Comorbidities Smoking status Diabetes status Postcode Financial classification Private, public or insurance status Priority populations, e.g. Aboriginal status
Process measures	Facility name Procedure name American Society of Anaesthesiologists classification Anaesthesia type Operating surgeon Principal anaesthetist Expected procedure duration Date of operation Surgical urgency classification Principal diagnosis code Principal procedure code Prostheses code Conversion to other procedure approach (laparoscopic or open) Reason for conversion (equipment, patient, instrument, time, other) Admission type (planned, emergency, planned same day, other) Admission specialty Robotic instruments used (checklist) Blood transfusion required Discharge specialty Total bed days Total national weighted activity units Admission start date Separation date Mode of separation Time stamps: Patient in operating room Anaesthesia start Robot docking Console start Console finish Docking finish Surgery finish Patient out of operating room Patient in recovery Patient out of recovery
Outcome measures	Intensive care unit admission date and duration Close observation unit admission date and duration Hospital-acquired complications Unplanned readmission to hospital within 30 days Reason for readmission Unplanned return to theatre within 30 days Cancer flag Pain (e.g. quality of life tool) Morbidity measures (e.g. Clavien-Dindo classification) — in hospital and/or 30 days Mortality (30, 90, 365 day)
Outcome measures — procedure and disease specific (to be developed for each procedure)	For example, for procedures performed for cancer Date patient deceased Overall survival time (interval between operation and mortality) Disease recurrence (date identified) Quality of recovery (e.g. performance quality rating scale) Quality of life measures (e.g. urology procedures may include impotency, incontinence, return to work, return to normal activity)
Operator measures	Completion of ergonomic assessment by operating surgeon, e.g. using a tool such as the rapid entire body or the rapid upper limb assessments
Patient-reported measures	Experience and outcome
Staff measures	Clinician experience
Cost	Cost from manufacturer for procurement for the surgical robot Consumable costs Maintenance costs of surgical robot per annum Staff costs for staff involved in using the robot

Background

Robot-assisted surgery has been available in the NSW public health system for several years as part of local research programs. Five public hospitals in NSW have this technology. Some also have access through a public private arrangement, usually via use of a robot located in a nearby private hospital. In private facilities, robot-assisted surgery is being used across a range of different indications and sub-specialty areas.

NSW context

Sites participating in robot-assisted surgery have contributed to the evidence base in NSW, both locally and via statewide processes.

Challenges
Opportunities

Challenges

Strong commercial influences contribute to variation in implementation between hospitals and disparity in access to the technology for clinicians and patients. This can impact the production of information about clinical outcomes.
Inconsistent training, training opportunities and recency of practice can impact suitable credentialling and assurance of continued competency.
Staggered and varied implementation and variation in data collection between hospitals, can make data comparison challenging.
Hospital-specific approaches to training can limit workforce capability and skill development across the multidisciplinary team, restricting capacity to contribute to research activity.
Varied use of surgical robots and high costs can impact the cost effectiveness of the technology.

Opportunities

Expand the use of robot-assisted surgery to new indications or procedures where there is evidence of it providing value-based care.
Create greater transparency of outcomes including safety, efficacy and cost across sites using robot-assisted surgery in NSW.
create consistency in data collection and evidence generation across sites to add to the evidence base for robot-assisted surgery.
Inform approaches to introducing new technologies for surgery in the future.

Developing the minimum dataset

In 2020, as part of national work on robot-assisted surgery, a minimum dataset was proposed. A draft outlined 100 fields across five domains focusing on process indicators and clinical outcomes. Feedback at a national workshop informed a second draft of the minimum dataset to include economic data, procedure-specific information, and training and credentialing.

The minimum dataset has been refined based on feedback and current evidence. The dataset may be further updated based on emerging evidence and the advice of clinicians working in robot-assisted surgery in NSW.

Building the evidence base in NSW

This framework has been informed by the below:

NSW Health hosted a national workshop in November 2020 to discuss approaches to developing a standardised training and credentialling pathway for robot-assisted surgery. This identified several enablers, such as the establishment of a governing body and strategies to ensure equity of access to training.
NSW Ministry of Health Specialty Services and Technology Evaluation Unit commissioned an evidence check in March 2023, to identify best available comparative evidence reporting on long-term patient outcomes for urological indications. The outcomes supported NSW Health's ongoing position on robot-assisted surgery.
The 2023 report complements the initial evidence check completed in 2019, which found that robot-assisted surgery may be as safe and effective as conventional surgical approaches; however, high cost is not offset by improved patient outcomes and long-term data around benefits, risks and cost effectiveness was insufficient.
The evidence to date shows that a significant proportion of studies are case series or single hospitals and lack the strength to inform system-wide decisions.
The local health districts which have been early adopters of robot-assisted surgery have also contributed to this evidence base via publications and service reports. They have invested in local frameworks, such as the evaluation framework commissioned by the Sydney Local Health District.