Simulation model for studying impact of demographic, temporal, and geographic factors on hospital demand

Mielczarek, Bożena; Zabawa, Jacek

doi:10.1109/wsc.2017.8248178

Cited by 5 publications

(2 citation statements)

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“…The next set of source data included demographic projections formulated separately for 36 cohorts of the WR population through 2030. For this purpose we used the results of our earlier work [23,24] during which we simulated, among others, three main demographic scenarios (Table 1): the baseline scenario assumes a slight increase in life expectancy and a high increase in fertility; the high scenario assumes a high increase in life expectancy and a high increase in fertility; and the medium scenario assumes a small increase in life expectancy and a very high increase in fertility. Our study focuses on the number of patients who will be admitted to hospitals in the region over the next few years, broken down by age-gender cohorts and diagnosis groups.…”

Section: Data Collection and Model Parametersmentioning

confidence: 99%

“…Motivated by these observations we propose a DES model to investigate the impact of population ageing on the demand for inpatient hospital services in a region extending to the south-western part of Poland. In our model, we used demographic projections created for the region in question during our previous research [23,24]. As part of our earlier studies, we have developed an approach called 'hierarchical cohorting' based on an SD simulation paradigm.…”

Section: Introductionmentioning

confidence: 99%

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

Mielczarek

2020

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Background: The world’s changing demographic trends inevitably lead to new challenges in the delivery of healthcare services. As the population ages, the consequent demand for various types of medical treatments changes as well, but the process is not uniform, neither for particular age–gender cohorts nor for different diagnoses’ groups. Medium and long-term forecasts of future healthcare needs, which are crucial at the level of regional health policy decision making, should therefore be built taking into account different scenarios that describe possible alternative changes in population structure.Methods: A discrete event simulation (DES) model was built to forecast future healthcare demand based on population projections for one Polish region. Demographic projections were formulated based on simulation experiments carried out in previous studies. Population forecasts were inputted into the DES model, together with patient data taken from the regional health fund. A simulation was run up to 2030 to predict future healthcare needs of older age–gender groups across different diagnosis categories. An additional experiment was performed with the hypothesis that admission rates for older cohorts will gradually increase starting from 2021.Results: Three scenarios involved different assumptions regarding forecasted population changes as published by the Polish Government Council. The fourth scenario assumed gradually increasing admission rates expressed by the older age–gender groups. The results indicate that the expected small increase in the region's overall population will translate into a bigger increase in the number of patients. The largest increase of hospitalisations is projected for the age groups 7579 and older. The number of patients in the oldest women’s group will be much higher than in the corresponding men’s group. Meanwhile, the forecasted changes for particular disease categories are characterised by a high degree of variation, both in terms of gender and age.Conclusions: A simulation provides wide possibilities for studying the impact of demographic trends in healthcare demand, as it allows conducting analyses in various cross sections. The results show that a change in the demographic structure and in particular, in the population’s ageing, will lead to significant shifts in the distribution of health needs for hospital treatment.

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Section: Data Collection and Model Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%