Forecasting future needs and optimal allocation of medical residency positions: the Emilia-Romagna Region case study.
Objectives: Italian regional health authorities annually negotiate the number of residency grants to be financed by the National government and the number and mix of supplementary grants to be funded by the regional budget. This study provides regional decision-makers with a requirement model to for...
| Publicado en: | Human Resources for Health Vol. 13; no. 1; pp. 7 - 8 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109724086&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109724086 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14784491 1CPZ jtl: Human Resources for Health issn: 14784491 maglogo: N pubinfo: dt: 2015 vid: 13 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 109724086 NLM25633752 2012975393 10.1186/1478-4491-13-7 NLM25633752 PMC4328064 109724086 ppf: 7 ppct: 1 formats: tig: atl: Forecasting future needs and optimal allocation of medical residency positions: the Emilia-Romagna Region case study. aug: au: Senese, Francesca Tubertini, Paolo Mazzocchetti, Angelina Lodi, Andrea Ruozi, Corrado Grilli, Roberto sug: ab: Objectives: Italian regional health authorities annually negotiate the number of residency grants to be financed by the National government and the number and mix of supplementary grants to be funded by the regional budget. This study provides regional decision-makers with a requirement model to forecast the future demand of specialists at the regional level.Methods: We have developed a system dynamics (SD) model that projects the evolution of the supply of medical specialists and three demand scenarios across the planning horizon (2030). Demand scenarios account for different drivers: demography, service utilization rates (ambulatory care and hospital discharges) and hospital beds. Based on the SD outputs (occupational and training gaps), a mixed integer programming (MIP) model computes potentially effective assignments of medical specialization grants for each year of the projection.Results: To simulate the allocation of grants, we have compared how regional and national grants can be managed in order to reduce future gaps with respect to current training patterns. The allocation of 25 supplementary grants per year does not appear as effective in reducing expected occupational gaps as the re-modulation of all regional training vacancies. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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