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International Conference on Complex Systems (ICCS2006)

An exploration into the uses of agent-based modeling to improve quality of health care

Ashok Kay (Kanagarajah)
ARC Centre for Complex systems -University of Queensland

Peter Lindsay
ARC Centre for Complex systems -University of Queensland

Anne Miller
Key Centre for Human Factors and Applied Cognitive Psychology - University of Queensland

David Parker
Business School - University of Queensland

     Full text: PDF
     Last modified: May 12, 2006

Abstract
At University of Queensland as part of a PhD project, we have embarked on developing a complex system engineering approach to improving quality of care within health care delivery systems. There is much research being conducted in several domains such as medical science, health care administration, social science, industrial engineering and information technology and statistics. Most of this work could be viewed as case studies which examine success stories as well as disasters. However direct applicability of the case studies from one part of the health care system to the other has been difficult or impossible. Even adoption of similar cases within the same environment at different timeframes is tricky due to the fact that systems have moved to a different operating regime from the previous experience. In effect these improvement activities or case studies are “experiments in progress”.

Health care delivery systems are defined as complex adaptive systems by various leading researchers in this field because they show the characteristics of being open and non linear. In addition due to the sociotechnical nature of health care, its boundaries are difficult to determine and the decisions are ultimately made by “observers”. The health care system itself is connected by formal and informal networks and continues to learn from various “experiments in progress” within the system. This highlights the need for requirement of a suitable simulation environment that will facilitate carrying out these experiments and allows faster learning of the systems.

Several simulations in health care have been reported in the literatures that are either discrete event simulation or simulations that examine system dynamics and they have been conducted in the following domains:

Health Systems: Strategy and policy studies at regional or national levels.
Systems: Strategic and policy studies, typically within organizations, at the regional or metropolitan area level
Clinical: Strategic and policy studies, typically within organizations or within a single facility.
Delivery Tactically focused studies, typically within a single facility or a department within a facility.
Prevention Studies focused on the prevention of illness, disease, or incidents and the impact of prevention strategies and tactics.
Epidemiology Studies focused on the spread of illness or disease or the physiological understanding of an illness or disease.

It is our observation that currently there is no modeling solution that has demonstrated capability to integrate the above mentioned scenarios. In addition as the starting conditions vary the existing simulations tend to require labor intensive modification or are invalid for new scenarios because of the original in-built assumptions of the models. This situation has motivated us to develop a better modeling framework that would enable us to develop an integrated simulation that would learn from its history as well as reflect the “integrated real world” scenarios”.

For the reasons explained in Table 1, we have chosen to develop an Agent Based Modeling (ABM) Framework to assist health care delivery in its “what if” scenario analysis as well as provide capability to predict future conditions. This will enable health care systems to analyze the impact of policy on tactical or operational situations or vice versa. There seems to be very little published literature on such applications of ABM in healthcare.

This paper will discuss the ABM framework we have developed to simulate an intensive care unit (ICU) using micro saintÒ software. This will be used to explore the conceptual dynamic safety model proposed by Cook and Rasmussen(2005). Cook and Rasmussen explain safety as a dynamic property of a socio technical system. The system is maintained within boundaries enveloped by pressures such as 1) safety, 2) workload and 3) economics. They further explain that in the past most healthcare delivery systems were loosely coupled—that is, activities and conditions in one part of the system had only limited effect on that elsewhere. Loose coupling allowed the system to buffer many conditions such as short term surges in demand. Modern management techniques and information systems have allowed facilities to reduce inefficiencies in operation. One side effect this improvement is the loss of buffers that previously accommodated demand surges. As a result, situations occur in which activities in one area of the hospital become critically dependent on seemingly insignificant events in seemingly distant areas.

With the use of our ICU simulation we will demonstrate the complex dynamic behavior of a health care system. We will show computationally that the effect on safety depends on:
· short time scale fluctuations in workload.
· loose coupling of “resource buffers”
· efficiency improvements within the organization led by managerial and technical changes.
· work saturation (such as gridlocked ICU)

In conclusion the paper will demonstrate the need for Agent Based Modeling technique for simulation of health care delivery system. We further demonstrate a modeling framework and an evolutionary development methodology to simulate an ICU operation and apply a conceptual case study proposed by the Cook and Rasmussen safety model. Because this model addresses the dynamic aspects of safety, it is particularly suited to understand the complex conditions in modern healthcare delivery and the way these conditions may lead to accidents.

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Table 1: Why we are adopting Agent Based modeling or (complex systems engineering) approach
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*** Reviewers are kindly requested to follow to the web link at http://necsi.org/events/iccs6/abstract/ashok341.doc to review the Table. This arrangement has been made because abstract submission system can not properly format tables; a full document of this paper has been uploaded to http://necsi.org/events/iccs6/abstract/ashok341.doc or contact authors.
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1. Morecroft (2005)
2 Authors
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References

1) Morecroft, John and Stewart Robinson (2005). Explaining puzzling dynamics: Comparing the use of system dynamics and discrete event simulation. In Proceedings of the 2005 International Conference of the System Dynamics Society.

2) Cook, R.I. & Rasmussen, J. (2005). Going solid: a model of system dynamics and consequences for patient safety. Quality and Safety in Health Care 14; 130-134

Authors resume:

i) Ashok Kay
Ashok is currently a PhD candidate at University of Queensland attached to ARC centre for complex systems. He holds B.E & M.Eng Sc from University of Queensland and Graduate Diploma in Management and MBA degrees. His industrial work experience covers 10 years with BHP Steel in the capacity of systems engineering, application development, simulation modeling and operational strategist roles. Subsequent work experience covers more than 5 years in a senior management consulting positions with PwC Consulting (now IBM Consulting) and Aspen Technology a software solution provider. Ashok’s expertises are in supply chain management, lean and simulation modeling.

ii) Peter Lindsay PhD
Peter Lindsay joined the University of Queensland in 1991 after holding academic and research positions at the University of New South Wales, the University of Manchester and the University of Illinois at Urbana-Champaign. He has more than eighteen years experience in formal aspects of systems and software engineering. He is co-author of two books on formal specification and verification of software systems, and over 50 refereed papers. In recent years he has provided system safety expertise to government and industry on a large number of safety-critical applications in the areas of defence, aerospace and transport. Peter currently holds the position of Boeing Professor of Systems Engineering and the Director of ARC centre for complex systems. His research interest covers engineering of complex systems; trusted computer system development & assurance; Safety Critical Systems; Formal Methods; Hazard and Risk Analysis; mathematical foundations of systems and software engineering; configuration & change management.

iii) Anne Miller PhD
Anne is the group Leader for Patient Safety Research Group, Key Centre for Human Factors, University of Queensland. Anne provides human factors expertise in the area of distributed cognition theory and research methods; human computer interaction design and development and human technology interaction evaluation and lecture at University of Queensland.

iv) David Parker PhD
David Parker has held positions as Reader in Business Operations Management and PhD Research Director at the Centre for Organisational Effectiveness, Business School, Bournemouth University, UK; he was Project Director for the Smart Region Initiative, a consortium comprising Unisys, Microsoft, Bay Technologies: research funded by the Queensland government. He has held academic positions at: School of Management, Cranfield University; also Head of Schools of Operations Management at the Business School, University of the West of England, and at Manchester Metropolitan University. His research interests include SME transitioning to e-commerce capability, and strategic service operations management. He has presented at international conferences and published widely in academic journals that include: Logistics Focus; Journal of Productivity Science; The Journal of Clothing Technology and Management; Logistics Today; International Journal of Business Process Management; Management Services Journal; International Journal of Electronic Business.




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