Published: 2026-01-27 09:05

AI and optimisation enhance operating theatre efficiency for arthroplasty

The demand for elective surgery, particularly arthroplasty procedures such as hip and knee replacements, continues to place significant pressure on NHS resources. Operating theatre efficiency is a critical factor in managing waiting lists, optimising resource allocation, and ensuring timely patient access to care. Emerging research explores how artificial intelligence (AI) and advanced optimisation techniques could offer a transformative approach to surgical scheduling and theatre management.

Addressing Operating Theatre Bottlenecks

Operating theatres are complex environments where multiple variables interact, including patient readiness, surgical team availability, equipment, and post-operative care capacity. Traditional scheduling methods often rely on historical averages and manual adjustments, which can be prone to inaccuracies. Unforeseen delays, such as longer-than-expected cases, late starts, or equipment issues, frequently lead to theatre overruns, cancellations, and inefficient use of valuable theatre time.

For high-volume procedures like arthroplasty, even minor inefficiencies can accumulate, contributing to extended waiting lists and increased operational costs. The challenge lies in creating a dynamic schedule that can adapt to real-time changes while maximising theatre utilisation and minimising idle time or overtime.

The Role of AI and Machine Learning in Scheduling

Machine learning (ML) algorithms offer a sophisticated way to predict surgical durations more accurately than conventional methods. By analysing vast datasets of past surgical cases, including patient demographics, co-morbidities, specific surgical approaches, and surgeon experience, ML models can identify complex patterns that influence procedure length. This predictive capability is crucial for creating more realistic and robust surgical schedules.

Beyond predicting case duration, AI can also be applied to forecast patient flow through the entire surgical pathway, from pre-operative assessment to post-anaesthetic care unit (PACU) and ward discharge. This holistic view allows for better allocation of all necessary resources, including nursing staff, anaesthetists, and recovery beds, reducing bottlenecks at various stages. Dynamic scheduling systems powered by AI could potentially adjust schedules in real-time based on unfolding events, offering alternative pathways or re-prioritising cases to maintain efficiency.

Optimisation Algorithms for Resource Allocation

Complementing machine learning’s predictive power are optimisation algorithms. These computational models are designed to find the “best” possible solution from a vast number of options, given a set of defined constraints and objectives. In the context of operating theatres, an optimisation algorithm might aim to:

• Maximise the number of cases completed within a theatre session.
• Minimise theatre idle time and overtime.
• Balance workload across surgical teams.
• Prioritise patients based on clinical need or waiting time.
• Ensure adequate staffing and equipment availability for all scheduled procedures.

By integrating predictive insights from AI with the strategic capabilities of optimisation, hospitals could move towards a more proactive and adaptive scheduling system. This approach allows for scenario planning, enabling managers to understand the potential impact of different decisions, such as adding an extra theatre session or reallocating staff.

Specific Applications in Arthroplasty

Arthroplasty procedures are particularly well-suited for AI and optimisation interventions due to their high volume, relatively standardised nature, and significant impact on patient quality of life and healthcare costs. The benefits could extend across the entire patient journey:

• Pre-operative Planning: AI could assist in identifying patients most likely to benefit from surgery, predicting potential complications, and streamlining pre-operative assessments.
• Theatre Scheduling: More accurate prediction of surgical times for total hip and knee replacements allows for tighter, yet realistic, scheduling, potentially fitting more cases into a day without increasing overtime.
• Resource Management: Ensuring that specialised instruments, prosthetics, and recovery beds are available precisely when needed, reducing delays and cancellations.
• Post-operative Care: Optimising discharge planning and rehabilitation schedules based on individual patient recovery trajectories.

The ability to reduce variability and improve predictability in arthroplasty scheduling could have a substantial positive impact on NHS waiting lists, which have grown significantly in recent years.

Potential Benefits for NHS Trusts

The adoption of AI and optimisation in operating theatre management holds several promising benefits for UK healthcare providers:

These improvements could translate into better patient access to vital orthopaedic procedures, enhanced financial sustainability for trusts, and a more positive working environment for surgical teams.

Challenges and Implementation Considerations

While the potential is significant, implementing AI and optimisation solutions within the NHS presents several challenges.

Firstly, the quality and availability of data are paramount. AI models are only as good as the data they are trained on. NHS trusts would need robust data collection systems, ensuring accuracy, completeness, and interoperability with existing IT infrastructure. Data governance, privacy, and security must also be rigorously addressed.

Secondly, clinical validation and safety are non-negotiable. Any AI-driven scheduling system must be thoroughly tested and validated in real-world clinical settings to ensure it delivers promised benefits without compromising patient safety or clinical outcomes.

This requires close collaboration between data scientists, clinicians, and operational managers.

Thirdly, there are ethical considerations, such as potential biases in algorithms if the training data reflects historical inequalities. Transparency in how AI makes decisions is also crucial for building trust among healthcare professionals and patients.

Finally, successful adoption requires significant change management. Healthcare professionals need to understand how these tools work, trust their outputs, and be trained in their use. AI should be viewed as a decision-support tool that augments human expertise, rather than replacing it.

The Future Landscape for UK Healthcare

The integration of AI and optimisation into operating theatre management aligns with the NHS’s broader digital transformation agenda. As the healthcare system continues to grapple with increasing demand and resource constraints, innovative technological solutions will be essential. Pilot programmes and collaborative research across NHS trusts will be vital to demonstrate the efficacy, safety, and scalability of these approaches.

The ultimate goal is to create a more resilient, efficient, and patient-centred healthcare system. While AI and optimisation offer powerful tools to enhance operational efficiency, their successful deployment will depend on careful planning, robust validation, and a commitment to continuous improvement, always keeping the needs of patients and healthcare professionals at the forefront.

Source: BMJ