Medical education has long grappled with the challenge of transforming book knowledge into clinical expertise. When I was a student on my first clinical rotation in medical school back in 1996, I was ready to put my knowledge to work caring for real patients. Only weeks before, I remember walking out of this monster exam called Step 1 feeling like I had all the knowledge of medicine in my brain, ready to hit the clinical years. After my first day of clinical rotations, fumbling with my stethoscope, exam notes, and pocket references, I realized I knew nothing.
I could list every component of a CBC and chem-7 and what each test measured, but I had little understanding of when to use which test, or whether each test might add to the diagnosis…or just waste time and money. After 2 years of seemingly non-stop studying and exams, I could not believe that I was starting all over again as a learner. I was crestfallen Had all my effort been wasted?
That moment was the beginning of my 20+ year career in medical education with a singular focus: to learn how to make the path to competency and clinical expertise for the next generation of medical students. In this article, I share the realization that set my compass in my continued journey to transform medical education.
Here is the TL;DR key to expertise: it’s less about what or how much you know and more about how accurately and efficiently you use that knowledge.
No matter the discipline, there are two fundamental types of knowledge: declarative and procedural. Understanding the difference and how simulation-based learning bridges the gap between them fuels your growth from novice to expert.
Declarative Knowledge: the foundation of medical understanding
Declarative knowledge represents the “what” of medicine. It’s the explicit, factual information that can be consciously recalled and verbally expressed. This knowledge includes anatomical structures, disease pathophysiology, drug mechanisms, diagnostic criteria, and treatment protocols. Students spend countless hours memorizing these facts, building an extensive database of theoretical knowledge that forms the foundation of medical practice.
For instance, a student might learn that myocardial infarction (MI), or heart attack, happens when tissue death occurs due to blocked coronary arteries, and treatment involves antiplatelet therapy, anticoagulation, and possible revascularization.
This knowledge is explicit-based and can easily be tested through traditional examinations. For example:
“Which is the best choice for treating a patient with an acute coronary syndrome…?”
“Describe the generation of a coronary thrombosis starting with plaque rupture and how specific medications are used to treat this event in a patient having an acute MI.”
While vital, this knowledge is only the building blocks of expertise; students then need to assemble a functional network of mental concepts to support real-world application in a real patient.
Procedural Knowledge: the art of clinical application
While it sounds like it refers to medical procedures themselves, procedural knowledge is much more than that! It is the cornerstone of how experts apply knowledge in medical decision-making to improve the quality and efficiency of the clinical care they deliver.
Procedural knowledge represents the “how” of medicine. It’s the implicit, experiential knowledge that guides skilled performance. This type of knowledge encompasses clinical reasoning patterns, diagnostic intuition, manual dexterity, and the ability to make rapid decisions under pressure. Procedural knowledge is tacit, difficult to verbalize, and develops through repeated practice and experience.
Taking from the example above, consider the difference between knowing the pathophysiology and treatment goals of MI (declarative) versus seamlessly performing the history, exam, testing, and treatment on a patient having a heart attack (procedural).
It requires (1) pattern recognition (history, exam and ECG), (2) situational awareness (is the patient stable or unstable), (3) anticipation (what are potential decompensation events that need to be anticipated), (4) resource deployment (what consult needs to be called, and when in the time course), and (5) the ability to adapt to changes in the patient’s critical condition in real-time—capabilities that cannot be acquired through textbooks alone.
Expertise comes from the systematic conversion of declarative knowledge into procedural knowledge through practice. My training bridged the era of “see one, do one, teach one” and the beginning of manikin simulations for CPR training.
During my career in medical education, I integrated simulation training into a curriculum for emergency medicine, using it to teach initial concepts and perform competency assessments for residents.
Research in cognitive neuroscience reveals that expertise development involves fundamental changes in brain organization—how concepts are connected to each other and to actions and emotions. As learners progress, their knowledge becomes increasingly organized into meaningful patterns (called heuristics) and their performance becomes more automated and efficient. Daniel Kahneman popularized this evolution of thinking with the concept of “System 1” and “System 2 “thinking—or, thinking “fast” and “slow.”
The use of simulations as a form of practice accelerates this evolution of thinking by providing the same intensive and focused practice necessary for neural pathway optimization. Virtual simulations uniquely facilitate knowledge acquisition by combining the engagement of gamified technology with unlimited opportunities for skill use that physical simulators cannot provide.
Unlike brick-and-mortar simulation, labs requiring scheduling, equipment, and physical presence, virtual medical simulation operates like sophisticated video games accessible from any device. Students can navigate complex clinical scenarios through interactive interfaces, make diagnostic decisions, select treatments, and observe realistic patient responses, preparing them for real-world interactions. These game-like environments provide privacy and accessibility unmatched by physical simulators, allowing learners to fail, experiment, and repeat scenarios without the pressure of being observed or judged.
For educators, virtual simulation enables infinite scenario generation. These platforms can instantly generate new cases with varying complexity, patient demographics, and complications. Advanced algorithms can adapt scenarios based on student performance, providing personalized learning paths designed to target individual knowledge gaps.
Accelerating the path forward
The transformation from medical student to expert clinician requires more than memorizing facts—it demands the development of sophisticated procedural knowledge that optimizes the neural pathways needed for skilled performance under pressure (e.g. heuristics). As medical education continues to evolve, the integration of virtual simulation that is accessible anytime, anywhere, into curricula will be essential for fostering healthcare professionals capable of delivering safe, effective patient care.
Over the past decade, our ever-growing team of expert clinicians and medical educators has used everything we know to create the Full Code Simulator with a singular goal of transforming medical education to align with the neuroscience of learning.
As we continue on this journey, we are thrilled to have you join us! Together, we are creating the future of medical education grounded in mastering how to acquire and use knowledge well. Today’s learners will become experts through the dedicated, limitless practice only virtual simulations can provide.
References:
Kahneman, D. Thinking, fast and slow. Farrar, Straus and Giroux, 2011. https://psycnet.apa.org/record/2011-26535-000
Pelaccia T, Tardif J, Triby E, Charlin B. An analysis of clinical reasoning through a recent and comprehensive approach: the dual-process theory. Medical Education Online. 2011;16(1):5890. https://pmc.ncbi.nlm.nih.gov/articles/PMC3060310/
Schmidt HG, Boshuizen HPA. On acquiring expertise in medicine. Educational Psychology Review. 1993;5(3):205-221. https://link.springer.com/article/10.1007/BF01323044
