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.

中文翻译：

---

与联合更换大批量医院登记册有关的可行性，有效性和成本的机会。

背景

临床注册表是用于纵向收集统一数据的强大工具，因此可以评估受特定病理影响的患者的预后。在全关节置换术的背景下，注册管理机构还可以作为上市后的监督。就资源和基础设施而言，采用注册表对于临床环境而言是沉重的负担。过多的工作负载会导致数据收集不完整，从而破坏注册表的有效性，因此需要优化工作负载。

方法

从Orstedico Galeazzi Istituto的用例开始，估计了注册表的时间和人员。对最初几年收集的数据的分析使我们能够提出减少工作量的方法。利用不同的机器学习模型来预测患者的满意度，以减少术后临床随访中的评估次数。此外，特征选择被用来识别任何不必要的临床量表。

结果

考虑到每年3500名患者的接受率，要求22名医生和6名非医疗雇员正确采用注册表。在测试的模型中，朴素贝叶斯模型在预测六个月的患者满意度方面表现最佳（AUPRC = 0.81）。此外，我们发现12个项目的简短表格无法预测六个月的满意度。

结论

在这项研究中，利用机器学习来提供一种减少使用病理学注册表的工作量的方法。这样的工作量减少可以在更大范围内产生可观的影响，并提高大型医院中注册表的可行性。