Aletaha, Daniel

Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.

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.

Objective Current methods used for forecasting workforce requirements in rheumatology are disparate, as are the parameters incorporated into workforce projection studies. The objective of these European League Against Rheumatism (EULAR points to consider (PTC) is to guide future workforce studies in adult rheumatology in order to produce valid and reliable manpower estimates. Methods The EULAR Standardised Operating Procedures were followed. A multidisciplinary task force with experts including patients with rheumatic diseases from 11 EULAR countries and the USA was assembled. A systematic literature review (SLR) was conducted to retrieve workforce models in rheumatology and other medical fields. PTC were based on expert opinion informed by the SLR, followed by group discussions with consensus obtained through informal voting. The level of agreement with the PTC was voted anonymously. Results A total of 10 PTC were formulated. The task force recommends models integrating supply (=workforce available in rheumatology), demand (=health services requested by the population) and need (=health services that are considered appropriate to serve the population). In general, projections of workforce requirements should consider all factors relevant for current and future workload in rheumatology inside and outside of direct patient care. Forecasts of workforce supply should consider demography and attrition of rheumatologists, as well as the effects of new developments in healthcare. Predictions of future need/demand should take demographic, sociocultural and epidemiological development of the population into account. Conclusion These EULAR-endorsed PTC will provide guidance on the methodology and the parameters to be applied in future national and international workforce requirement studies in rheumatology.