Forecasting future needs and optimal allocation of medical residency positions: the Emilia-Romagna Region case study

Abstract

ObjectivesItalian 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.MethodsWe 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.ResultsTo 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.Electronic supplementary materialThe online version of this article (doi:10.1186/1478-4491-13-7) contains supplementary material, which is available to authorized users.

The factors affecting the obstetricians-gynecologists workforce planning: A systematic review

“…Of all selected papers, 22 were from the US, 17,24,25,27,28,[31][32][33]36,38,39,[43][44][45][46]49,51,[54][55][56]58,59 5 from Canada, 30,40,52,53,57 4 from Australia, 18,19,26,34 3 from UK, 22,23,48 and 10 from Ireland, Japan, Mexico, Oman, Italy, and Iran. 15,16,20,21,29,35,37,41,42,47,50 A quantitative model had been used in 40 papers to estimate the OB-GYNs requirement, and 5 papers had qualitatively or descriptively investigated the factors affecting the supply and demand of the workforce in this field. The results showed that 28 and 12 papers were related to national and regional studies, respectively.…”

“…The growth of OB-GYN graduates' participating in specialized fields was the main trend affecting the number of available OB-GYNs. 17,21,22,30,39,40,47,50 One of the important factors in evaluating the supply of OB-GYNs was the participation and contribution of other physicians in providing specialized services in this field. 60 According to the results, maternity services provided by OB-GYNs interference and overlap by primary care physicians, general practitioners, internal medicine and urology specialists.…”

“…The amount and type of women's insurance coverage (affecting economic access) [23][24][25][26]28,32,33,39,46,[54][55][56] Changing the level of women's expectations and expectations of women's health services 18,22,28,29,34,40,45,46,53, Geographical accessibility to outpatient and inpatient centers 23,28,30,46,52,55,56, utilization rate parameters Average waiting time for outpatient and inpatient services 18,19,28,46,48,52 Outpatient visits to gynecologists 15,19,20,[22][23][24][25][26][27]32,33,36,[40][41][42][43]46,48,50,51,53,[55][56][57] Per capita hospitalized services related to gynecology 15,18,20,22,…”

A Simulation Approach to Evaluate the Effect of Demographic Changes on Healthcare Demand

“…The forecasted level of demand was estimated based on historical patient data and adjusted for population growth rates. A similar approach was applied by Senese et al [19] for Italy and Barber and Lopez-Valcarcel [20] for Spain. Both research groups elaborated an SD model and included regional population projections developed by the regional statistical o ce to simulate future demand for specialists at the regional level.…”

Cost Optimization Estimation of Medical Institutions in Grading Diagnosis and Treatment based on System Dynamics Model

“…Wang L C et al established a new hospital inventory demanddriven replenishment model that determines the optimal replenishment time and the total inventory cost [11]. The model developed by Senese F et al predicts the evolution of the supply of Italian medical experts and combines demographics, service utilization and hospital beds to measure the potential effective allocation of medical allocations [12]. Diaz R et al demonstrates the utility of the system dynamics approach to model and simulate US demand for ambulatory health care service both for the general population and for specific cohort subpopulations over the 5-year period, from 2003 to 2008.…”