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Modelling a progressive care system using a coloured-timed Petri net

Centre for Measurement and Information in Medicine, City University, Northampton Square, London EC1V 0HB, UK, M.L.W.HughesKcity.ac.uk

E. R. Carson

Centre for Measurement and Information in Medicine, City University, Northampton Square, London EC1V 0HB, UK

M. Makhlouf

Centre for Measurement and Information in Medicine, City University, Northampton Square, London EC1V 0HB, UK, MITRE Corporation, 202 Burlington Road, MS B248 Bedford, Massachusetts, USA

C. J. Morgan

Centre for Measurement and Information in Medicine, City University, Northampton Square, London EC1V 0HB, UK, Department of Anaesthesia, Royal Brompton and Harefield NHS Trust, Sydney Street, London SW3 6NP, UK

R. Summers

Centre for Measurement and Information in Medicine, City University, Northampton Square, London EC1V 0HB, UK, Department of Information Science, Loughborough University, Loughborough LE11 3TU, UK

In a progressive care system, patients are transferred between different healthcare units within a healthcare facility, depending on the different stages in the patients’ care. Although a progressive care system allows for improved delivery of treatment and monitoring through the division and specialization of labour and other resources, it is also very problematic to manage effectively with high levels of interdependence between the different units and unpredictable levels of demand that are placed on resources. The solution proposed in this paper is improved scheduling of admissions through the development of a model which may be used as a basis of schedule optimization. The model which will be proposed is designed to assist a human operator to evaluate a schedule by simulating the flow of patients around a progressive care system. The modelling methodology used is coloured-timed Petri nets (CTPNs). It will be argued that the CTPN formalism is particularly suited to the problem since it allows the dynamics of the system to be sensitive to different instantiations of system variables such as processing time. That is, sensitive to different patients having different lengths of stays and admissions requirements.

Key Words: clinical prediction • coloured-timed Petri nets • healthcare modelling • simulation

Transactions of the Institute of Measurement and Control, Vol. 22, No. 3, 271-283 (2000)
DOI: 10.1177/014233120002200305


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