The application of artificial intelligence (A.I) using patient reported outcomes (PROs) to predict benefits, risks, benefits and likelihood of improvement following surgery presents a new frontier in shared decision-making. The purpose of this study was to assess the impact of an A.I-enabled decision aid versus patient education alone on decision quality in patients with knee OA considering total knee replacement (TKR). Secondarily we assess impact on shared decision-making, patient satisfaction, functional outcomes, consultation time, TKR rates and treatment concordance. We performed a randomized controlled trial involving 130 new adult patients with OA-related knee pain. Patients were randomized to receive the decision aid (intervention group, n=65) or educational material only (control group, n=65) along with usual care. Both cohorts completed patient surveys including PROs at baseline and between 6–12 weeks following initial evaluation or TKR. Statistical analysis included linear mixed effect models, Mann-Whitney U tests to assess for differences between groups and Fisher's exact test to evaluate variations in surgical rates and treatment concordance.Introduction
Methods
The American Joint Replacement Registry (AJRR) is the largest registry of total hip and knee arthroplasty (THA and TKA) procedures performed in the U.S. The National (Nationwide) Inpatient Sample (NIS) is a public database containing demographic estimates based on more than seven million hospitalizations annually. The purpose of this study was to analyze whether AJRR data is representative of the national experience with TJA as represented in NIS Cohen's d effect sizes were computed to ascertain the magnitude of differences in demographics, hospital volume (in 50 patient increments), and geographic characteristics between the AJRR and NIS databases.Introduction
Methods
Meaningful clinical improvement as demonstrated through patient-reported outcome measures (PROMs) are increasingly used to evaluate success of total hip arthroplasty (THA) procedures. This patient perspective can provide a full picture when used with clinical data to best evaluate surgical outcomes. All primary THA procedures reported to the American Joint Replacement Registry from 2012–2018 with linked pre-operative and 1-year post-operative functional or anatomical PROMs were included. The achievement of minimal clinically-important difference (MCID) was calculated using the distribution method. Logistic regression models with covariate adjustment for patient demographics, American Society of Anesthesiologists (ASA) score, and body mass index (BMI) were constructed to identify associations with PROMs. Results were analyzed based on hospital size (small, medium and large) and teaching type (non-teaching, minor and major) based on the American Hospital Association Survey (2015).Introduction
Methods
