Modeling and Simulation: Paving New Ways for Medical Experimentations
Computer modeling and simulation techniques are playing an increasingly important role in changing both the way medicine is taught and the way it is practiced. With the help of art and science of modelling and simulation, the insight into system behaviour can be developed or enhanced from a model that adequately represents a selected subset of the system’s attributes. Simulation design considerations include:
- The provision of an appropriate user interface
- Determining optimal simulation complexity
- Defining a tutorial strategy
- Selecting authoring tools
- Assessing hardware requirements
- Identifying user needs
- Defining pedagogical goals
- Verification and validation
- System evaluation
Significant trends in simulation include the development of improved authoring tools, migration to desktop hardware platforms, and integration of simulation techniques with other types of anntications. Within the medical arena, simulation techniques are being utilized as:
- Means of educational training
- Method of gaining experience in a variety of skills, including the diagnosis (i.e. pattern recognition), prognosis, and treatment of disease
- Testing for clinical competence
- Decision support tools for the diagnosis of disease, forecasting, and decision-making in health care management
- Means of investigating complex processes, i.e., through hypothesis testing, experimental design, and sensitivity analysis.
The value of simulations in medical education lies in their ability to assist in the understanding and analysis of complex processes, in skills acquisition, in conducting experiments, and in augmenting the learning experience in relation to other forms of computer-based education support, e.g. text and other static knowledge retrieval.
Graphic skill-building simulations are extremely effective tools for teaching and manipulating concepts which can be readily conceptualized using visual models. The use of vivid, graphic analogs not only reinforces relationships and provides an intuitive notion of structure, but serves to provide mental models that aid in learning. Such visual models offer the benefits of extending the student’s memory capacity while presenting the significant information in a particularly usable form, thereby fostering a deeper understanding of the situation.
Simulations provide a means of gaining a variety of experiences, including skill building, pattern recognition, problem solving, diagnosis, and treatment. Simulations can be utilized to supplement, and in some instances, replace conventional learning experiences. Simulations can negate the need for a real laboratory or a real patient, reduce student inhibitions, and permit a wider range of possible experiences. Simulations can provide experimental settings in cases where it is either impossible or unethical to perform the actual experiment on real patients. Within a simulated environment, students can see response to changing a single parameter in a controlled manner not possible in living material. Furthermore, results of simulated experiments can suggest promising experiments on real patients.
The time compression afforded by simulations enables one to more easily trace the time course of a disease or other process under study. There is also evidence to suggest that simulated experiences are far superior to the learning of rules, especially during the early phases of learning.
When used as decision support tools, simulations provide a means of performing ‘what-if and sensitivity analysis. One of the primary tasks of the physician’s is to utilize available information for selecting appropriate patient diagnostic workup and therapy strategies. Simulations, being a highly compressed data bases, can assist this decision making process by providing a ready reference of clinical problems, patient presentations, and pathophysiologic mechanisms.
Moreover, actual patient data can be incorporated into the simulation to assist the physician in arriving at the proper diagnosis and in determining the appropriate therapy for a particular patient. Such simulation-based decision support tools, with output in the manner suited to the Clinician’s perception of the problem, are of benefit to both patient and physician. For those parameters that must be estimated, sensitivity analysis can disclose the extent to which model results depend on the parameter estimates.
As research tools, simulation methods allow investigation into complex processes, allowing the investigator to view simulation results with user-specified parameters. Simulation is the medium for experimentation and exploration. Often, simulation is the only technique that exists to explore models of complex, interconnected, and irregular biological systems.