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

Julia Unger, Polina Putrik, Frank Buttgereit, Daniel Aletaha, Gerolamo Bianchi, Johannes W.J. Bijlsma, Annelies Boonen, Nada Cikes, João Madruga Dias, Louise Falzon, Axel Finckh, Laure Gossec, Tore K. Kvien, Eric L. Matteson, Francisca Sivera, Tanja A. Stamm, Z. Szekanecz, Dieter Wiek, Angela Zink, Christian DejacoSofia Ramiro

Research output: Review article

4 Citations (Scopus)

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.

Original languageEnglish
Article numbere000756
JournalRMD Open
Volume4
Issue number2
DOIs
Publication statusPublished - dec. 1 2018

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Rheumatology
Databases
Physicians

ASJC Scopus subject areas

  • Rheumatology
  • Immunology and Allergy
  • Immunology

Cite this

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. / 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, Z.; Wiek, Dieter; Zink, Angela; Dejaco, Christian; Ramiro, Sofia.

In: RMD Open, Vol. 4, No. 2, e000756, 01.12.2018.

Research output: Review article

Unger, J, Putrik, P, Buttgereit, F, Aletaha, D, Bianchi, G, Bijlsma, JWJ, Boonen, A, Cikes, N, Dias, JM, Falzon, L, Finckh, A, Gossec, L, Kvien, TK, Matteson, EL, Sivera, F, Stamm, TA, Szekanecz, Z, Wiek, D, Zink, A, Dejaco, C & Ramiro, S 2018, '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', RMD Open, vol. 4, no. 2, e000756. https://doi.org/10.1136/rmdopen-2018-000756
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, Z. ; Wiek, Dieter ; Zink, Angela ; Dejaco, Christian ; Ramiro, Sofia. / 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. In: RMD Open. 2018 ; Vol. 4, No. 2.
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title = "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",
author = "Julia Unger and Polina Putrik and Frank Buttgereit and Daniel Aletaha and Gerolamo Bianchi and Bijlsma, {Johannes W.J.} and Annelies Boonen and Nada Cikes and Dias, {Jo{\~a}o Madruga} and Louise Falzon and Axel Finckh and Laure Gossec and Kvien, {Tore K.} and Matteson, {Eric L.} and Francisca Sivera and Stamm, {Tanja A.} and Z. Szekanecz and Dieter Wiek and Angela Zink and Christian Dejaco and Sofia Ramiro",
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T1 - Workforce requirements in rheumatology

T2 - A systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider

AU - Unger, Julia

AU - Putrik, Polina

AU - Buttgereit, Frank

AU - Aletaha, Daniel

AU - Bianchi, Gerolamo

AU - Bijlsma, Johannes W.J.

AU - Boonen, Annelies

AU - Cikes, Nada

AU - Dias, João Madruga

AU - Falzon, Louise

AU - Finckh, Axel

AU - Gossec, Laure

AU - Kvien, Tore K.

AU - Matteson, Eric L.

AU - Sivera, Francisca

AU - Stamm, Tanja A.

AU - Szekanecz, Z.

AU - Wiek, Dieter

AU - Zink, Angela

AU - Dejaco, Christian

AU - Ramiro, Sofia

PY - 2018/12/1

Y1 - 2018/12/1

N2 - 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.

AB - 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.

KW - Autoimmune diseases

KW - Economic evaluations

KW - Health services research

KW - Quality indicators

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