Opportunities to improve feasibility, effectiveness and costs associated with a total joint replacements high-volume hospital registry

Abstract

Background. Clinical registries are powerful tools for collecting uniform data longitudinally, thus making it possible to evaluate the outcome of patients affected by a specific pathology. In the context of total joint arthroplasty, registries serve also as postmarket surveillance. Adoption of registries is a heavy burden for clinical settings in terms of resources and infrastructures. Excessive workload leads to incomplete data collection which undermines the effectiveness of a registry and consequently the workload needs to be optimised. Methods.Starting from the use case of the Istituto Ortopedico Galeazzi, the time and personnel dedicated to the registry was estimated. Analysis of the data collected in the first years enabled us to propose a methodology for workload reduction. Different Machine Learning models were leveraged to predict patients with excellent satisfaction to reduce the number of assessments in their clinical post-operative follow-up. Moreover, feature selection was used to identify any unnecessary clinical scale to collect. Results.Given an acceptance rate of 3,500 patients per year, 22 doctors and 6 nonmedical employees were required to adopt a registry properly. Among the tested models, the Naïve Bayes gave the best performance (AUPRC=0.81) in predicting patient satisfaction at six months. Moreover, we found that the 12-item Short Form was poorly informative in predicting satisfaction at six-months. Conclusions.In this study machine learning was leveraged to provide a methodology to reduce workload in the use of pathology registries. Such workload reduction can have a considerable impact at a larger scale, and improve registry feasibility in high-volume hospitals.

Remote Management of Patients after Total Joint Arthroplasty via a Web-Based Registry during the COVID-19 Pandemic

“…On the other hand, the inclusion in the registry of all patients undergoing arthroplasties at the different Units of the Institute (i.e., treated by different surgeons/equips) allows for a partial generalization of the results obtained. In this context, a continuous and granular analysis of internal-registry-generated data is helpful and sensitive enough not only to understand and predict operational burdens linked to followup activities, as we reported in a previous publication [30], but also to detect specific variations in PROMs. Whether these observed differences, though statistically significant, are truly clinically important is not known yet.…”

Italian Translation, Adaptation, and Validation of the Novel Satisfaction Measure Assessment after Primary Total Joint Arthroplasty: The Goodman Score Questionnaire

“…At our institute, all patients undergoing total joint arthroplasty are enrolled and followed through the H&K registry [4]. This Registry has also recently proved to be flexible and sensitive enough to be able to detect variations in outcome scores even in extraordinary conditions such as remote patient follow-up, which was put in place during the recent nationwide lockdown in Italy as a consequence of the COVID-19 pandemic [5].…”