What Can We Expect Following Anterior Total Hip Arthroplasty on a Regular Operating Table? A Validation Study of an Artificial Intelligence Algorithm to Monitor Adverse Events in a High-Volume, Nonacademic Setting

摘要

Background: Quality monitoring is increasingly important to support and assure sustainability of the orthopedic practice. Surgeons in nonacademic settings often lack resources to accurately monitor quality of care. Widespread use of electronic medical records (EMR) provides easier access to medical information, facilitating its analysis. However, manual review of EMRs is highly inefficient. Artificial intelligence (AI) software allows for the development of algorithms for extracting relevant complications from EMRs. We hypothesized that an AI-supported algorithm for complication data extraction would have an accuracy level equal to or higher than manual review after total hip arthroplasty (THA).