RESUMEN
Starting in the 50s, healthcare workforce planning became a major concern for researchers and policy makers, since an imbalance of health professionals may create a serious insufficiency in the health system, and eventually lead to avoidable patient deaths. As such, methodologies and techniques have evolved significantly throughout the years, and simulation, in particular system dynamics, has been used broadly. However, tools such as stochastic agent-based simulation offer additional advantages for conducting forecasts, making it straightforward to incorporate microeconomic foundations and behavior rules into the agents. Surprisingly, we found no application of agent-based simulation to healthcare workforce planning above the hospital level. In this paper we develop a stochastic agent-based simulation model to forecast the supply of physicians and apply it to the Portuguese physician workforce. Moreover, we study the effect of variability in key input parameters using Monte Carlo simulation, concluding that small deviations in emigration or dropout rates may originate disparate forecasts. We also present different scenarios reflecting opposing policy directions and quantify their effect using the model. Finally, we perform an analysis of the impact of existing demographic projections on the demand for healthcare services. Results suggest that despite a declining population there may not be enough physicians to deliver all the care an ageing population may require. Such conclusion challenges anecdotal evidence of a surplus of physicians, supported mainly by the observation that Portugal has more physicians than the EU average.
Asunto(s)
Predicción/métodos , Fuerza Laboral en Salud/tendencias , Médicos/provisión & distribución , Envejecimiento , Emigración e Inmigración , Necesidades y Demandas de Servicios de Salud , Humanos , Método de Montecarlo , Políticas , Crecimiento Demográfico , Portugal , JubilaciónRESUMEN
BACKGROUND: Planning the health-care workforce required to meet the health needs of the population, while providing service levels that maximize the outcome and minimize the financial costs, is a complex task. The problem can be described as assessing the right number of people with the right skills in the right place at the right time, to provide the right services to the right people. The literature available on the subject is vast but sparse, with no consensus established on a definite methodology and technique, making it difficult for the analyst or policy maker to adopt the recent developments or for the academic researcher to improve such a critical field. METHODS: We revisited more than 60 years of documented research to better understand the chronological and historical evolution of the area and the methodologies that have stood the test of time. The literature review was conducted in electronic publication databases and focuses on conceptual methodologies rather than techniques. RESULTS: Four different and widely used approaches were found within the scope of supply and three within demand. We elaborated a map systematizing advantages, limitations and assumptions. Moreover, we provide a list of the data requirements necessary to implement each of the methodologies. We have also identified past and current trends in the field and elaborated a proposal on how to integrate the different methodologies. CONCLUSION: Methodologies abound, but there is still no definite approach to address HHR planning. Recent literature suggests that an integrated approach is the way to solve such a complex problem, as it combines elements both from supply and demand, and more effort should be put in improving that proposal.
Asunto(s)
Atención a la Salud , Personal de Salud , Planificación en Salud , Política de Salud , Servicios de Salud , Administración de Personal , Necesidades y Demandas de Servicios de Salud , Humanos , Recursos HumanosRESUMEN
In this paper, we evaluate the effect of demand uncertainty on hospital costs. Since hospital managers want to minimize the probability of not having enough capacity to satisfy demand, and since demand is uncertain, hospitals have to build excess capacity and incur the associated costs. Using panel data comprising information for 43 Portuguese public hospitals for the period 2007-2009, we estimate a translog cost function that relates total variable costs to the usual variables (outputs, the price of inputs, some of the hospitals' organizational characteristics) and an additional term measuring the excess capacity related to the uncertainty of demand. Demand uncertainty is measured as the difference between actual and projected demand for emergency services. Our results indicate that the cost function term associated with the uncertainty of demand is significant, which means that cost functions that do not include this type of term may be misspecified. For most of our sample, hospitals that face higher demand uncertainty have higher excess capacity and higher costs. Furthermore, we identify economies of scale in hospital costs, at least for smaller hospitals, suggesting that a policy of merging smaller hospitals would contribute to reducing hospital costs.