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Related Concept Videos

Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
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Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Systematic Sampling Method01:17

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

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Related Experiment Video

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Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Simulating waiting list management.

John Andrew Bowers1

  • 1Stirling Management School, University of Stirling, Stirling, FK9 4LA, UK. j.a.bowers@stir.ac.uk

Health Care Management Science
|June 23, 2011
PubMed
Summary

This study introduces a new model for National Health Service (NHS) waiting list management, accounting for patient priority. The model simulates realistic waiting times, highlighting the impact of seasonal variations on patient access to care.

Area of Science:

  • Health Services Research
  • Operational Research
  • Health Economics

Background:

  • Patient waiting times in the UK's National Health Service (NHS) have seen significant changes, with reduced mean wait times but altered distribution characteristics.
  • Existing simulation models often assume a first-in-first-out (FIFO) system, which does not accurately reflect empirical waiting time distributions or current waiting list management practices.

Purpose of the Study:

  • To develop a more realistic simulation model for NHS waiting list management that incorporates patient priority.
  • To create a model capable of generating diverse waiting time distributions reflecting different management strategies.

Main Methods:

  • Development of a novel waiting list management model incorporating an explicit measure of patient priority relative to a target wait time.

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Published on: February 1, 2020

  • Utilisation of simulation experiments to explore model behaviour and assess the impact of factors like seasonal variations in demand and supply.

    • Main Results:

    • The developed model can replicate a spectrum of waiting time distributions, from near-exponential to those characteristic of rigid FIFO systems.
    • Simulation experiments demonstrated that seasonal variations in healthcare demand and supply lead to predictable seasonality in patient waiting times.

    Conclusions:

    • The new model provides a more accurate representation of NHS waiting list dynamics than traditional FIFO models.
    • Findings underscore the importance of considering seasonal fluctuations when setting and monitoring waiting time targets to ensure realistic progress assessment.