Challenges of biological realism and validation in simulation-based medical education

Simulation, both physical and computer-based, has a rich history in support of medical education. Essentially all these efforts have been aimed at instilling concrete measurable skills, akin to vocational training. They present learners with choices, facilitating a degree of learning by doing. The sets of learner choices are usually limited, with choices clearly classified into “right” and “wrong”. But much of medicine is not much like a multiple-choice test. The realm of choices is broad and not always easily converted to a short list. The “correct” answer is not always known by the experienced physician beforehand, sometimes not even after the die is cast and the future unfolds. Computer simulation of human disease and its treatment can in principle be tremendously useful in the education of both basic and clinical scientists. This paper describes some challenges in the construction of simulation-based “liberal arts” biomedical education. The educator attempting to develop a learning environment based on simulation of biology faces some special challenges. The challenges addressed in this paper are: face validity and deep validity; finding the right degree of realism; authoring biomedical models efficiently; managing randomness. To illustrate the issues, we trace the history of the Oncology Thinking Cap throughout several versions and expansions of educational objectives, and describe the detection and remediation of shortcomings related to these issues. Dealing effectively with issues of validity and realism can be accomplished if the acquisition of information driving and justifying the model development choices is documented, preferably automatically, during the process. Efficiency in authoring is greatly enhanced by judicious modularity to encourage re-use, and by the use of templated statements rather than raw code or exotic graphical components to represent the instructions driving the model. Randomness can be used to familiarize learners with the true relative proportions of types of cases, or to enrich the encountered cases with rarer but more instructive cases. When a learner repeats an encounter with a scenario while changing a single option, proper management of randomness is essential to avoid artifacts of random number generators. Otherwise an outcome change caused by a shift in random number streams may masquerade as an outcome change due to the changed option. Effective use of computer simulation of human disease and its treatment for biom