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A model for forecasting intermittent skilled home nursing needs.

S Johansen1, S Bowles, G Haney

  • 1Columbia University School of Public Health, New York, NY.

Research in Nursing & Health
|December 1, 1988
PubMed
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Forecasting demand for skilled home nursing services is crucial. A statistical model predicts patient needs and costs for cancer and myocardial infarction survivors, aiding healthcare planning.

Area of Science:

  • Healthcare Management
  • Health Economics
  • Nursing Services

Background:

  • Accurate forecasting of post-discharge healthcare needs is essential for resource allocation.
  • Skilled home nursing services are a critical component of patient recovery and management.
  • The rising costs and demand for healthcare necessitate efficient predictive models.

Purpose of the Study:

  • To present a statistical model for forecasting the need and cost of post-discharge skilled home nursing services.
  • To illustrate the model's application using data from cancer and myocardial infarction patients.
  • To simulate the impact of healthcare system changes on service demand and costs.

Main Methods:

  • Development of a simple statistical model to predict service utilization and associated expenses.

Related Experiment Videos

  • Application of the model to a dataset of 7598 cancer patients and 2337 myocardial infarction patients.
  • Simulation analysis to assess the effects of varying patient severity on future healthcare demands.

    • Main Results:

    • The model provides projections for the number of patients requiring skilled home nursing care.
    • Estimated costs associated with providing post-discharge nursing services for specific patient cohorts.
    • Quantification of the potential impact of healthcare system shifts on service needs.

    Conclusions:

    • The statistical model offers a practical tool for healthcare administrators and policymakers.
    • Understanding future demand and cost is vital for effective planning of home nursing resources.
    • The model's flexibility allows for scenario analysis to adapt to evolving healthcare delivery systems.