Unger Julia, Putrik Polina, Buttgereit Frank, Aletaha Daniel, Bianchi Gerolamo, Bijlsma Johannes W J, Boonen Annelies, Cikes Nada, Dias João Madruga, Falzon Louise, Finckh Axel, Gossec Laure, Kvien Tore K, Matteson Eric L, Sivera Francisca, Stamm Tanja A, Szekanecz Zoltan, Wiek Dieter, Zink Angela, Dejaco Christian, Ramiro Sofia
Department of Health Studies, Institute of Occupational Therapy, FH JOANNEUM University of Applied Sciences, Bad Gleichenberg, Austria.
Department of Internal Medicine, Division of Rheumatology, Maastricht University Medical Center and CAPHRI Research Institute, Maastricht, The Netherlands.
RMD Open. 2018 Dec 5;4(2):e000756. doi: 10.1136/rmdopen-2018-000756. eCollection 2018.
To summarise the available information on physician workforce modelling, to develop a rheumatology workforce prediction risk of bias tool and to apply it to existing studies in rheumatology.
A systematic literature review (SLR) was performed in key electronic databases (1946-2017) comprising an update of an SLR in rheumatology and a hierarchical SLR in other medical fields. Data on the type of workforce prediction models and the factors considered in the models were extracted. Key general as well as specific need/demand and supply factors for workforce calculation in rheumatology were identified. The workforce prediction risk of bias tool was developed and applied to existing workforce studies in rheumatology.
In total, 14 studies in rheumatology and 10 studies in other medical fields were included. Studies used a variety of prediction models based on a heterogeneous set of need/demand and/or supply factors. Only two studies attempted empirical validation of the prediction quality of the model. Based on evidence and consensus, the newly developed risk of bias tool includes 21 factors (general, need/demand and supply). The majority of studies revealed high or moderate risk of bias for most of the factors.
The existing evidence on workforce prediction in rheumatology is scarce, heterogeneous and at moderate or high risk of bias. The new risk of bias tool should enable future evaluation of workforce prediction studies. This review informs the European League Against Rheumatism points to consider for the conduction of workforce requirement studies in rheumatology.
总结有关医师劳动力建模的现有信息,开发一种用于评估风湿病劳动力预测偏倚风险的工具,并将其应用于风湿病领域的现有研究。
在主要电子数据库(1946 - 2017年)中进行了一项系统文献综述(SLR),其中包括对风湿病领域SLR的更新以及其他医学领域的分层SLR。提取了有关劳动力预测模型类型以及模型中考虑因素的数据。确定了风湿病劳动力计算中的关键一般因素以及特定需求/需求和供应因素。开发了劳动力预测偏倚风险工具,并将其应用于风湿病领域现有的劳动力研究。
总共纳入了14项风湿病领域的研究和10项其他医学领域的研究。这些研究基于一组异质的需求/需求和/或供应因素使用了多种预测模型。只有两项研究尝试对模型的预测质量进行实证验证。基于证据和共识,新开发的偏倚风险工具包括21个因素(一般因素、需求/需求和供应因素)。大多数研究表明,大多数因素存在高或中度偏倚风险。
关于风湿病劳动力预测的现有证据稀缺、异质且存在中度或高度偏倚风险。新的偏倚风险工具应能使未来对劳动力预测研究进行评估。本综述为欧洲抗风湿病联盟在进行风湿病劳动力需求研究时应考虑的要点提供了参考。