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Estimating Need and Demand for Prehospital Care

Author

Listed:
  • Ricardo D. Kamenetzky

    (University of Pittsburgh, Pittsburgh, Pennsylvania)

  • Larry J. Shuman

    (University of Pittsburgh, Pittsburgh, Pennsylvania)

  • Harvey Wolfe

    (University of Pittsburgh, Pittsburgh, Pennsylvania)

Abstract

Models estimating demand and need for emergency transportation services are developed. These models can provide reliable estimates which can be used for planning purposes, by complementing and/or substituting for historical data. The model estimating demand requires only four independent variables, population in the area, employment in the area, and two indicators of socioeconomic status which can be obtained from census data. The model can be used to estimate demand according to 4 operational categories and 11 clinical categories. The parameters of the model are calibrated with 1979 data from 82 ambulance services covering over 200 minor civil divisions in Southwestern Pennsylvania. This model was tested with data from another 55 minor civil divisions, also in Southwestern Pennsylvania, and it provided good estimates of total demand. The model to estimate need evolves from the demand model. It enables planners to estimate unmet need occurring in the region. The effect of emergency transportation service (ETS) provider characteristics on demand was also investigated. Statistical tests show that, for purposes of forecasting demand, when the sociodemographic factors are taken into account, provider characteristics are not significant.

Suggested Citation

  • Ricardo D. Kamenetzky & Larry J. Shuman & Harvey Wolfe, 1982. "Estimating Need and Demand for Prehospital Care," Operations Research, INFORMS, vol. 30(6), pages 1148-1167, December.
    • Handle: RePEc:inm:oropre:v:30:y:1982:i:6:p:1148-1167
    DOI: 10.1287/opre.30.6.1148
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    Citations

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    Cited by:

    1. McArthur, David Philip & Gregersen, Fredrik A. & Hagen, Terje P., 2014. "Modelling the cost of providing ambulance services," Journal of Transport Geography, Elsevier, vol. 34(C), pages 175-184.
    2. Nabil Channouf & Pierre L’Ecuyer & Armann Ingolfsson & Athanassios Avramidis, 2007. "The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta," Health Care Management Science, Springer, vol. 10(1), pages 25-45, February.
    3. Hyunjin Lee & Taesik Lee, 2021. "Demand modelling for emergency medical service system with multiple casualties cases: k-inflated mixture regression model," Flexible Services and Manufacturing Journal, Springer, vol. 33(4), pages 1090-1115, December.

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