by Allen McLean, RN, MN, MSc, PhD(c)
Allen is currently a PhD student in Computer Science at the University of Saskatchewan in the Computational Epidemiology and Public Health Informatics Lab. His research interests include the development of computer modeling and simulation software for addressing health systems challenges, chronic diseases and health inequities at the population level, as well as machine learning techniques applied to large health datasets. Allen previously attended the University of Victoria earning an MN and MSc (Health Information Science) in a unique dual degree program for Nursing Informatics professionals. Allen has over 20 years experience in healthcare as an ultrasound technologist, clinical educator, team leader and community health RN.
All nurses will be familiar with simulation to some degree – typically the mannequins used to teach a variety of clinical nursing skills. Simulation labs offer a fabulous learning opportunity, but are often restricted to a limited number of mannequins, and experiences nurses might only encounter in acute care settings. Just imagine the scenarios we could create and explore if we had the resources to operate a simulation lab with dozens, hundreds, or even thousands of mannequins, simulating not only patients, but any population we chose to represent, in a wide variety of settings, from hospital maternity wards to community health centres across Canada.
Welcome to the virtual world of simulation modeling software, a collection of tools and techniques I believe have tremendous potential to address the many nursing and health challenges facing society today. The topic is far too broad to cover here in any depth, but I would encourage anyone interested to contact me – this is the subject of my doctoral research and I am always happy to discuss.
Dynamic simulation models (DSMs) are computer models that are simplified representations of the real world. Used successfully in engineering, ecology, defence, and business since the 1950’s, DSMs enable decision-makers and researchers to map complex problems by bringing together a variety of evidence sources such as research, expert knowledge, practice experience, and data. The resulting DSM is used as a ‘what-if’ tool that can simulate various practice or policy scenarios to see which is likely to have the most effect. Simulation is used when conducting experiments on a real system would be impossible or impractical (for example, because of the high cost of prototyping and testing, because the fragility of the system will not support extensive tests, or because of the duration of the experiment in real time is impractical). DSMs allow us to test a range of possible solutions in a low-risk and robust way before implementing them in the real world. Advances in modeling software and computer hardware capabilities make this possible. Imagine creating virtual worlds where nurses explore the effects of staffing or scheduling changes on a unit, the effects of various service delivery models, or the effect of policy changes on a particular population. The applications in nursing practice, policy, and research are vast.
Examples of dynamic simulation modeling projects our group has been involved in include childhood obesity, hospital out-patient care pathways, gestational diabetes, alcohol harms, tobacco reduction, suicide prevention, and emergency room planning to name only a few. For those interested, I recommend starting with Insight Maker, an excellent interactive tool with many examples. Enjoy!
AnyLogic. (2016). http://www.anylogic.com/
Insight Maker. (2016). https://insightmaker.com/
NetLogo. (2016). https://ccl.northwestern.edu/netlogo/
RunTheModel. (2016). http://www.runthemodel.com/
Simul8. (2016). http://www.simul8.com/