Analytical Models for Decision Making

By Colin Sanderson; Reinhold Gruen | Go to book overview

10
Modelling flows through
systems

Overview

In Chapter 1 you learned to distinguish between models representing effects (or influences), logical stages and flows. You were also introduced to system models, which involve both influences and flows. In this chapter and the next you will learn about a number of approaches to modelling systems. Such models can play useful roles in analysing the possible outcomes of different policy options.

In the first part of this chapter you will learn how flows through systems can be represented using simple 'tree' models. These can be useful for questions about the routes that flows of patients may take as they move through the system, if 'outcomes' can be measured at a fixed time after intervention. But if outcome events are spread out over time and can occur more than once, another approach may be needed. In the second part of this chapter you will learn about Markov models.

Another important issue is whether the situation in some 'downstream' parts of a system affects flow rates 'upstream'. The nature and extent of any such feature of a system, called 'feedback', can determine how the system as a whole behaves, and in the third part of the chapter you will learn more about this. Finally, you will learn about the limitations of modelling flows as flows, rather than the sum of the experiences of individuals. In Chapter 11 you will learn about modelling the experiences of individuals, known as microsimulation.


Learning objectives
By the end of this chapter, you will be better able to:
use representations of flows, states, events and influences to describe the
performance of systems
use tree structures to model flows through a system
explain the Markov property and Markov chains
explain the role of system dynamics and feedback in complex system
behaviour

Key terms

Feedback loop The causal loop formed when flow rates out of a process or state influence the
flow rates into it.

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Analytical Models for Decision Making
Table of contents

Table of contents

  • Understanding Public Health ii
  • Title Page iii
  • Contents v
  • Acknowledgements vii
  • Overview 1
  • Section 1 - Models and Decision Making in Health Care 5
  • 1: The Role of Models 7
  • 2: Building a Decision Support Model 28
  • 3: Strategic Options Development and Analysis 43
  • Section 2 - Methods for Clarifying Complex Decisions 63
  • 4: Many Criteria 65
  • 5: Uncertainty 84
  • 6: Risk 103
  • Section 3 - Models for Service Planning and Resource Allocation 119
  • 7: Population Need for a Specific Service 121
  • 8: Balanced Service Provision 142
  • 9: Hospital Models 164
  • Section 4 - Modelling for Evaluating Changes in Systems 179
  • 10: Modelling Flows Through Systems 181
  • 11: Irregular Flows Systems with Queues 202
  • 12: Outline Review 223
  • Glossary 227
  • Index 231
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