AI in Orthopaedics 2024 - Fundamentals for AI & ML in Orthopaedics and MSK
Introduction: Current data collection in hospitals and for the National Joint Registry fails to capture key areas of patient and implant factors that affect outcomes following knee replacement surgery and fail to provide real-time information to patients, healthcare providers and implant manufactures.
The aim of the ADOPT (Advanced Data for Orthopaedic PaThways) project, is to create the most definitive set of patient, implant and treatment data which truly reflects the patient pathway.
Patients and methods: Previous literature and current unpublished Institutional data provides the rationale for this project and its key aims
Proposed new methodology: The set-up and functionality of the clinical data collection system created by the data analytics company TCC-Casemix for a pilot project will be outlined. This includes creating patient phenotypes, based on layers of potentially interacting complexities, the principle of more comprehensive patient, surgical and implant related information and the availability in real-time of individual and pooled data to the surgeon and hospital, with individual enhanced data to the patient and primary care.
The key benefits of this work is that it will identify which knee osteoarthritis patients, in the context of increasing clinical complexity, do well after knee replacement surgery and post-operative therapies to provide the best possible information for all key stakeholders as the data is accumulated.
Conclusion: An integrated platform collecting detailed standardised patient information from the time of diagnosis through their surgical treatment and beyond provides patients and healthcare systems the best chance of improving outcomes, tracking patient complexities and implant performance.
