Background. Surgical quality improvement has received increasing attention in recent years, yet it isn't clear where orthopaedic surgeons should focus their efforts for the greatest impact on peri-operative safety and quality. We sought to guide these efforts by prioritising orthopaedic procedures according to their relative contribution to overall morbidity, mortality, and excess length of stay. Methods. We used data from the American College of Surgeons' National Surgery Quality Improvement Program (ACS-NSQIP) to identify all patients undergoing an orthopaedic procedure between 2005 and 2007 (n=7,970). Patients were assigned to 44 unique procedure groups based on Current Procedural and Terminology codes. We first assessed the relative contribution of each procedure group to overall morbidity and mortality in the first 30 days, and followed with a description of their relative contribution to excess length of stay. Results. Ten procedures accounted for 70% of adverse events and 64% of excess hospital days. Hip fracture repair accounted for the greatest share of adverse events, followed by total knee arthroplasty, total hip arthroplasty, revision total hip arthroplasty, knee arthroscopy, laminectomy, lumbar/thoracic arthrodesis, and femur fracture repair. In contrast, no other procedure group accounted for more than 2% of morbidity and mortality. Conclusion. Only a few procedures account for the vast majority of morbidity and mortality in orthopaedic surgery. Concentrating quality improvement efforts on these procedures may be an effective way for surgeons and other stakeholders to improve peri-operative care and reduce cost in orthopaedic surgery

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Cite this article: Bone Joint Res 2023;12(7):447–454.