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 post-market 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 3500 patients per year, 22 doctors and 6 non-medical 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.
Keywords: Clinical-based outcome measures; High volume hospital; Machine learning; Optimisation; Patient reported outcome measures; Registry; Total joint replacements; Workload.
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