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Related Experiment Videos

Effective demand forecasting in 9 steps.

Hugo J Finarelli1, Tracy Johnson

  • 1Health Strategies & Solutions, Inc., Philadelphia, USA.

Healthcare Financial Management : Journal of the Healthcare Financial Management Association
|November 24, 2004
PubMed
Summary

Accurate healthcare demand forecasting involves nine key steps, from analyzing historical data and drivers to modeling conditions and testing assumptions for population and provider needs. This systematic approach ensures reliable future healthcare service demand projections.

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Area of Science:

  • Health Services Research
  • Healthcare Management
  • Operations Research

Background:

  • Accurate forecasting of healthcare service demand is crucial for effective resource allocation and strategic planning.
  • Existing forecasting methods may not comprehensively address the multifaceted nature of healthcare demand drivers.
  • A structured, nine-step methodology is proposed to enhance the precision and reliability of healthcare demand predictions.

Purpose of the Study:

  • To outline a systematic, nine-step framework for forecasting demand in healthcare services.
  • To provide a clear methodology for assembling and analyzing historical data, identifying demand drivers, and establishing benchmarks.
  • To guide the development of assumptions for both population-based and provider-level demand, culminating in a baseline forecast and sensitivity analysis.

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Main Methods:

  • Data Assembly: Collection of comprehensive historical data on healthcare service utilization.
  • Trend and Driver Analysis: Examination of historical trends and identification of key factors influencing demand.
  • Modeling and Assumption Development: Creation of models for existing conditions, population-based demand, and provider-level demand, including the formulation of core assumptions.
  • Forecasting and Sensitivity Testing: Generation of a baseline future demand forecast and assessment of projection sensitivity to assumption changes.

Main Results:

  • A nine-step process is detailed for effective healthcare demand forecasting.
  • The methodology integrates historical data analysis, driver identification, condition modeling, and assumption-based projections.
  • Sensitivity testing is incorporated to evaluate the robustness of the forecast under varying conditions.

Conclusions:

  • The proposed nine-step framework provides a robust and systematic approach to forecasting healthcare service demand.
  • This structured methodology enhances the accuracy of future demand predictions, supporting better healthcare planning and resource management.
  • Implementing this comprehensive forecasting process is essential for healthcare organizations aiming to meet future service needs effectively.