## Modelling in HealthcareHow many patients will require admission to my hospital in two days? How widespread will influenza be in my community in two weeks? What will the changing demographics of our community do to affect demand for medical services in our region in two years? These and similar questions are the province ofModelling in Healthcare. This new volume, presented by the Complex Systems Modelling Group at Simon Fraser University in Canada, uses plain language, sophisticated mathematics and vivid examples to guide and instruct. Sage advice on the benefits and limitations of the modeling process and model predictions is generously distributed so that the reader comes away with an understanding not only of the process but also on the practical uses (and misuses!) of models. Perhaps the most important aspect of this book is that the content and the logic are readily understandable by modelers, administrators and clinicians alike. This volume will surely serve as their common and thus preferred reference for modeling in healthcare for many years. --Timothy G. Buchman, Ph.D., M.D., FACS, FCCM Modelling in Healthcareadds much-needed breadth to the curriculum, giving readers the introduction to simulation methods, network analysis, game theory, and other essential modeling techniques that are rarely touched upon by traditional statistics texts. --Ben Klemens, Ph.D. Mathematical and statistical modeling has tremendous potential for helping improve the quality and efficiency of health care delivery and as a tool for decision making by health care professionals. This book provides many relevant and successful applications of modeling in health care and can serve as an important resource and guide for those working in this exciting new field. --Reinhard Laubenbacher, Ph.D. |

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aﬀect algorithms analysis approach attributable risk Behavioural Model beneﬁts coeﬃcients conﬁdence intervals data set deﬁned deﬁnitions descriptive statistics developed diﬀerent diﬀerential equations diﬃcult discussed disease drug users eﬀect eﬃcacy equilibrium errors examine example ﬁeld Figure ﬁnal ﬁnd ﬁnite ﬁrst ﬁt ﬁxed ﬂow function game theory given graph Health Belief Model healthcare system hospital human capital models individual individual’s interactions intervention linear regression logistic regression Markov assumption Markov models mathematical methods Microsoft Windows modelling in healthcare modelling technique node objects optimization problems outcome parameters patient payoﬀ perceived player population Pr(D Pr(D|Fc Pr(F predictions predictor variables probability distribution programming psychosocial models publication bias question queueing models queueing theory random Reference relative risk represent response variable risk factor risk modelling self-eﬃcacy signiﬁcant solution speciﬁc strategy Subsection 4.2 surgery survey system dynamics system dynamics model systems thinking traﬃc waitlist