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Reumatol Clin. 2013 Nov-Dec;9(6):353-8. doi: 10.1016/j.reuma.2013.01.005. Epub 2013 Apr 13.

Rheumatology in the community of Madrid: current availability of rheumatologists and future needs using a predictive model.

[Article in English, Spanish]

Author information

  • 1Técnicas Avanzadas de Investigación en Servicios de Salud (TAISS), Madrid, España.

Abstract

OBJECTIVES:

To: 1) describe the distribution of the public sector rheumatologists; 2) identify variables on which the workload in Rheumatology depends; and 3) build a predictive model on the need of rheumatologists for the next 10 years, in the Community of Madrid (CM).

METHODOLOGY:

The information was obtained through structured questionnaires sent to all services/units of Rheumatology of public hospitals in the CM. The population figures, current and forecasted, were obtained from the National Statistics Institute. A predictive model was built based on information about the current and foreseeable supply, current and foreseeable demand, and the assumptions and criteria used to match supply with demand. The underlying uncertainty in the model was assessed by sensitivity analysis.

RESULTS:

In the CM in 2011 there were 150 staff rheumatologists and 49 residents in 27 centers, which is equivalent to one rheumatologist for every 33,280 inhabitants in the general population, and one for every 4,996 inhabitants over 65 years. To keep the level of assistance of 2011 in 2021 in the general population, it would be necessary to train more residents or hire more rheumatologists in scenarios of demand higher than 15%. However, to keep the level of assistance in the population over 65 years of age it would be necessary to train more residents or hire more specialists even without increased demand.

CONCLUSIONS:

The model developed may be very useful for planning, with the CM policy makers, the needs of human resources in Rheumatology in the coming years.

Copyright © 2012 Elsevier España, S.L. All rights reserved.

KEYWORDS:

Community of Madrid; Comunidad de Madrid; Human resources; Modelos predictivos; Needs forecast; Predicción de necesidades; Predictive models; Recursos humanos

PMID:
23587550
[PubMed - indexed for MEDLINE]
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