Precision Orthopedics: Multimodal Data Infrastructure for Personalized Surgical Decisions in Joint Arthroplasty

ABSTRACT Total joint arthroplasty (TJA) is among the most common elective surgeries in the United States with approximately 1.2 million procedures performed annually. Despite the high volume, TJA research, surgical practice and policy are stalled by a lack of robust real-world evidence, particularly concerning new technologies such as indications for new implants and robotic surgery. Multimodal clinical and imaging data that inform surgical decisions and also essential for rigorous evaluation of new technologies remain embedded in unstructured electronic health records (EHR) and radiographs, making it challenging to identify patient heterogeneity and surgical outcomes comprehensively. Building upon accomplishments from our previous funding period, including developing and externally validating natural language processing algorithms, constructing extensive radiographic registries through advanced deep learning techniques, and assessing machine learning models for predicting surgical complications, this renewal application aims to advance the rigor of orthopedics research and address two pressing surgical practice questions. Our overarching goal is to improve surgical decision-making and TJA outcomes using advanced analytics and large multimodal datasets from high-volume referral and low-volume community hospitals, as well as nationwide data from American Joint Replacement Registry (AJRR). We propose three independent but complementary aims: (1) address methodological challenges (unmeasured confounding, selection bias, misclassification bias, algorithmic bias) of real-world evidence generation in TJA; (2) establish personalized TJA implant decision support accounting for heterogeneity of implant effects; and (3) determine the effectiveness of robotic TJA surgery for a range of surgeon-hospital volumes. The proposed research integrates large longitudinal datasets and modern analytical approaches from epidemiology and data science to address surgical decision-making questions in TJA. Deliverables include an extendable methodological framework, reusable computational tools, and a prototype TJA Research Copilot for methodology support to orthopedic investigators. Successful completion of these aims will enhance the rigor of TJA research and improve surgical decision-making with respect to new implants and robotic technologies. The project will likely have a sustained and transformative impact on evaluation of surgical technologies and healthcare policies in TJA with broad applicability across other surgical domains.