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Updated: Jun 1, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Published on: June 1, 2022

Development of modelling method selection tool for health services management: from problem structuring methods to

Gyuchan T Jun1, Zoe Morris, Tillal Eldabi

  • 1Loughborough Design School, Loughborough University, Loughborough, LE11 3TU, UK.

BMC Health Services Research
|May 21, 2011
PubMed
Summary
This summary is machine-generated.

A new tool helps health services decision-makers choose the best modelling and simulation methods for planning and operations. It addresses knowledge gaps and resource limitations for effective healthcare policy development.

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Last Updated: Jun 1, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

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Published on: June 1, 2022

Area of Science:

  • Health services research
  • Operations research
  • Health policy analysis

Background:

  • Modelling and simulation (M&S) are increasingly recognized for their potential in designing healthcare policies, strategies, and operations.
  • Current utilization of M&S in healthcare is limited, with underdeveloped guidance on method selection and application.
  • Significant knowledge gaps exist regarding the applicability of specific M&S approaches to health services management.

Purpose of the Study:

  • To develop a mechanism for decision-makers to compare and select appropriate M&S methods for health services planning and management.
  • To facilitate better selection and utilization of M&S methods or commissioning of relevant M&S work.
  • To address the challenge of choosing the most suitable M&S method for specific healthcare management problems.

Main Methods:

  • A comprehensive literature review, expert team knowledge, and user input were used to develop a comparison and selection tool.
  • Twenty-eight distinct M&S methods were identified and characterized.
  • Methods were assessed based on application areas, project life cycle stages, output types, insight levels, and required resources (time, money, knowledge, data).

Main Results:

  • A matrix-based characterization of M&S methods enables quick comparison and selection.
  • The study identified significant knowledge gaps in applying M&S to health services management, exacerbated by scarce resources like time, money, and skilled personnel.
  • The tool provides insights into expected outcomes and resource requirements for each method.

Conclusions:

  • A novel tool has been developed to aid in selecting M&S methods for healthcare decision-making processes.
  • The tool clarifies which method is most appropriate for specific health services management problems, expected outcomes, and necessary inputs.
  • This contributes valuable resources to the limited existing literature on M&S method comparison and selection in healthcare.