Aims
To develop a learning health system for orthopaedic surgery within the Scottish NHS.
Rationale - A learning health system is a system of healthcare that is designed to continuously learn and improve over time by using data and technology to inform clinical practice and research. This includes integrating data from various sources, such as electronic health records, patient registries, audits and clinical trials, and using it to improve patient care and outcomes.
Methods - A learning health system incorporates the use of artificial intelligence (AI) and machine learning (ML) techniques to analyse and extract insights from the data, which can then be used to inform decision-making and improve healthcare processes. The goal of a learning health system is to create a closed-loop solution where data is collected, analysed, and used to inform clinical practice, which in turn generates more data, and the cycle continues. This allows healthcare providers to continuously learn from the data and improve the care they provide to patients. Additionally, the data generated by a learning health system can be used for research purposes, helping to advance our understanding of diseases and treatments.
Specifically, this project will utilise the following national datasets held within Public Health Scotland:
• Primary Care - Electronic Frailty Index
• National Theatre Data - procedural Information
• Arthroplasty Rehabilitation In Scotland Endeavour (ARISE) - Patient level In-hospital demographic and process measures
• Scottish Arthroplasty Project - Long term outcome and complication data
• Scan for Safety - Implant Registry data
These data sources are all held centrally and are readily available for data linkage via CHI (Community Health Index) unique identifier. However, currently no formal linkage and more importantly analysis and learning is undertaken at either a national, hospital or patient level.
Outcomes
This project therefore has the potential to benefit NHS Scotland and society by developing a unique and world first whole system data picture that can inform clinical practice today at a national level and more widely the orthopaedic surgery evidence base could benefit from this learning health system in several ways, such as:
• Improving patient outcomes: A learning health system could be used to analyze patient data, such as medical images and electronic health records, to predict surgical outcomes and identify patients at high risk for complications. This information could be used to tailor treatment plans and improve patient outcomes.
• Optimizing surgical procedures: A learning health system could be used to analyze data from previous surgeries to identify best practices and optimize surgical procedures. This could lead to improved efficiency in the operating room and reduced complication rates.
• Personalized medicine: A learning health system could be used to analyze patient data, such as genetic information and medical images, to develop personalized treatment plans for patients. This would allow orthopaedic surgeons to tailor treatment to the individual patient, leading to better outcomes and reduced complications.
• Real-time monitoring: A learning health system could be used to monitor various aspects of the surgical process in real-time, such as equipment usage, staff interactions, and patient outcomes. This would allow for realtime adjustments to be made during the surgery to optimize the outcome.
• Research: A learning health system could be used to collect and analyze large amounts of data from orthopaedic surgeries, which could be used for research purposes. This would help to advance our understanding of orthopaedic diseases and treatments, and could lead to the development of new surgical techniques and technologies.
In summary, a learning health system is an innovative approach to healthcare that uses data and technology to continuously learn and improve patient care, research, and overall healthcare system.
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