This module will introduce you to the concepts of modelling for healthcare and will explore models for both:

• Planning and Management of Healthcare Resources
• Epidemiology and Effective Treatment of Disease

Examples of models might include those for the prevention, early detection and treatment of disease, such as cancer, HIV/AIDS and diabetes.  Resource models would include those for the planning and management of hospital beds, operating theatres, ambulances, and provision of resources in outpatient clinics and critical care units. 

A historical outline will be given on the use, practicalities and limitations of both deterministic and stochastic models.  Differential equation models, Markov models, decision trees and simulation techniques will be discussed and case studies on various topics presented. You will also be introduced to the importance of health economics in conjunction with OR models, for example in providing cost-utility and cost-effectiveness models for health policy evaluation.

Computer lab sessions will enable you to develop and run healthcare Markov and simulation models of your own. 

The unit aims to encourages you to consider what makes a mathematically robust, necessarily detailed and practical model for use by healthcare professionals, and how such tools can help influence health policy.

1. derive analytical models of healthcare processes;
2. assess the role of health economics and its application in healthcare models;
3. construct and adapt computational models for healthcare systems and disease progression;
4. evaluate and critique healthcare modelling case studies and literature;
5. develop an awareness of the different applications in this field;
6. evaluate the characteristics of mathematically robust, necessarily detailed and practical models for use by healthcare professionals.

You will be guided through learning activities appropriate to your module, which may include:

• Weekly face to face classes (e.g. labs, lectures, exercise classes)
• Electronic resources to support the learning (e.g. videos, exercise sheets, lecture notes, quizzes)

Students are also expected to undertake at least 150 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.

OR and analytics: derivation of analytical models and simulation of stochastic systems.

Mathematical reasoning: understanding the theory and assumptions that underpin mathematical models.

Use of simulation, optimisation and statistical computer packages.

1. Introduction and overview of healthcare modelling
   Concepts of modelling for healthcare; patient-flow and patient-progress applications; common elements and differences; benefits and pitfalls of healthcare modelling.
2. Analytical methods
   Deterministic and stochastic modelling; analytical models: differential equations applied to epidemic modelling; state transition modelling: Markov models; decision trees; applications of techniques to healthcare problems.
3. Health Economics
   Definitions of cost-utility and cost-effectiveness; measurements of quality of life; QALYs; case studies.
4. Simulation approaches
   Healthcare simulation; fundamentals of the Discrete Event and System Dynamics approaches; comparison of the two approaches for healthcare modelling.
5. Planning and management models
   Demand and capacity modelling for hospitals and other healthcare systems; applications and case-studies.
6. Patient classification techniques
   Benefits of classification; statistical approaches including tree-based algorithms (CART).
7. Survival Analysis Censored data: survival curves: Kaplain-Meier estimates: comparisons of multiple survival curves: hazard rations.