Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: autoimmune diseases; economic evaluations; health services research; quality indicators.

Figure 1

Structure of comprehensive workforce prediction studies. The figure illustrates the logic of workforce prediction planning and the factors that should be considered in a low risk of bias model. Planning should adopt an integrated model that includes a number demand/need and supply factors. Prediction should be optimally made for 5–15 years’ horizon, with regular updates and performance assessment. Baseline imbalance between need/demand and supply should be taken into account. Uncertainty analyses should be done to test the critical assumptions. Relevant stakeholders should be consulted throughout the process. Results of the prediction should be convertible to headcounts and full-time equivalents (FTEs) to facilitate decision-making process at different levels.