James B. McGee, MD February 2015
Much attention has been paid lately to the skill of “decision-making” in healthcare. This trend is driven by a number of factors including cost, patient safety, and the increasing complexity of the healthcare environment. Not only are there more and more diagnostic and therapeutic options, there are more regulations, insurance restrictions, and higher patient expectations than ever before.
Complex decision-making, as described by Croskerry, Eva, Kassirer and others, can be practiced and improved-upon through didactic, simulated and bedside teaching (Croskerry, 2009; Eva, 2005; Kassirer, 2010). One recommended approach is the use of clinical cases to develop and refine a clinician’s mental “illness scripts.” These illness scripts associate clinical findings with diagnoses and therapeutic decisions and encode them in memory as “stories” for retrieval later.
However, this approach is not limited to the healthcare field. In fact, illness scripts are patterns of facts and events that are remembered through the context of stories. Research shows that a wide variety of practitioners use pattern recognition to solve problems—including, but not limited to, psychotherapists, architects, engineers, and technicians (Jonassen, 2011; Jonassen, & Hernandez-Serrano, 2002; Polkinghorne, 1998). These practitioners develop narrative case histories that allow them to recognize patterns and thereby identify solutions.
Traditionally clinicians built their library of scripts through structured specialty training and then decades of personal experience caring for patients. While experience does improve clinical skills, there are at least two problems with relying on experience alone. First, it takes a long time to see enough patients with enough clinical variety to build powerful illness scripts and, second, not all scripts are necessarily accurate. For example, the gastroenterologist who sees patients with complications of gastric bypass surgery may inaccurately associate gastric bypass with frequent ulceration, bleeding and malabsorption. However, the vast majority of bypass patients never has complications or has to see a gastroenterologist.
One method to structure, standardize and even accelerate the development of valid illness scripts, or case histories in other fields, is through simulation. Within the healthcare field, an online simulation can efficiently present the relevant clinical data and images and challenge clinicians to make decisions about diagnostics and therapy. These not only provide exposure to a greater number of accurate illness scripts in less time, they can offer tailored feedback when clinicians make suboptimal decisions. With this instructional approach, learners from a variety of fields can gain new experiences as they encounter situations that are new to them, and discover solutions that they will add to their mental library of case histories.
For more information on how to apply these concepts, please see the recent article, Twelve tips to support the development of clinical reasoning skills using virtual patient cases.
Croskerry, P. (2009). A universal model of diagnostic reasoning. Academic medicine, 84, 1022-1028.
Eva, K. (2005). What every teacher needs to know about clinical reasoning. Medical Education, 39, 98-106.
Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. New York: Routledge.
Jonassen, D. H. & Hernandez-Serrano, J. (2002). Case-based reasoning and instructional design: Using stories to support problem solving. Educational Technology: Research and Development, 50(2), 65-77.
Kassirer, J.P. (2010). Teaching clinical reasoning: Case-based and coached. Academic Medicine, 85, 1118-1124.
Polkinghorne, D. E. (1988). Narrative knowing and the human sciences. New York: State University of New York Press.