1. The learning curve theory in medicine
1.1. What is the learning curve theory?
The learning curve theory proposes that a learner’s efficiency in a task improves over time the more the learner performs the task. In other words, proficiency increases with practice (‘Uses promptos facit’ <lat.> = practice makes perfect). However, the learning curve that describes the association between the invested learning effort and the increase in performance has a sigmoid shape.
At the beginning (initial learning or latent phase) the learning effort is large with low performance-increase until the student reaches the exponential phase where the learning rate is rapid. Finally, learning reaches a plateau (asymptotic learning) when learning becomes again more effortful, slowly approaching a theoretical maximum performance (fig. 1/A).
The sigmoid shape of the learning curve is strongly dependent on the teaching/learning methods. Although the learning curve theory could be effectively utilized in medical education, leading to higher quality knowledge, it is seldom applied. Fundamental changes are needed to utilize the possible benefits (1).
On the other hand, the Ebbinghaus forgetting curve also much depends on properly timed repetitions (Fig. 2). To achieve durable learning in medical education appropriately timed repetitions are extremely important. This underlines the importance of physicians’ further training in continuing medical education.
1.2. Why is the learning curve important in medical education and in healthcare?
Consequences of the above statements described by the learning and the forgetting curves are especially important in medical and healthcare education due to limited resources and high error rates (1), especially regarding the correct diagnosis. The results of more effective learning manifests as less misdiagnosis leading to substantially lower healthcare costs. Taking cost-efficiency into account, we must also note that the earlier the young healthcare providers become more experienced, the more value they can provide. The challenge for clinicians and managers is to capitalize on learning theory in the development and implementation of clinical protocols (1).
According to a recent study, there is a strong need to develop longitudinal measures to track changes in learning for medical education (5). One solution suggested by Thompson et al. is that student’s self-assessment may be a useful method to explore medical students’ learning (5)
2. Learning by doing
According to old and recent evidence, the most effective way of learning is when someone learns from his own mistakes. As Oscar Wilde said: “Experience is simply the name we give our mistakes”. Learning occurs when improvement in an activity results from understanding gained from prior experience (1). The steps of “learning by doing” (fig. 3) include 1) Gaining experience (DO), 2) Reviewing results of the experience – optimally together with a teacher (FEEDBACK/REFLECT), 3) Drawing conclusions: learning from the experience (CONCLUDE), and 4) Finally planning the next experience based on what one has learned (DECIDE).
3. How can you deliver a repeated learning experience in diagnostics?
Present medical education is still largely textbook/lecture based, even in the clinical fields. Recent evidence suggests that case-based learning is more effective than traditional discipline-specific teaching (e.g., physiology, anatomy, pharmacology) (7). One of the applications of the learning curve theory is medicine. The classical example is surgical training where the surgeon is practicing the same repetitive tasks over and over. However, learning and improving diagnostic abilities is similarly repetitive.
The learning curve is useful to analyze individual development. To complete a task should take less effort with fewer mistakes the more the task has been repeated. The infinite number of virtual patients in the InSimu platform offers an excellent solution to train medical students or doctors on large number of cases and assess them objectively with new untouched patients. Just having the proper diagnosis once doesn’t mean that the student will establish it properly in a different setting. Thus, the infinite number of virtual patients offers the possibility for the student to repeat the same patient type with different typical symptoms and clinical data until he/she achieves the theoretical maximum performance.
4. How to incorporate virtual patients to accelerate the learning curve with InSimu?
Virtual patients (VP) offer an excellent way to accelerate the learning curve in medical education, in both preclinical studies and clinical years of medical school. According to a recent study performed at Karolinska, student engagement was superior in the case of virtual patients vs recorded lectures (8). Furthermore, another study concluded that “VP cases are an effective alternative to lecture-led small group teaching in terms of learning efficacy in the short and long-term as well as self-assessed competence growth and student satisfaction” (9). It may also serve as a handy and efficient supplementary tool in medical rotations for students to elaborate an even larger number of cases.
Available tools (useful by medical education course instructors) at InSimu are:
- Create regular assignments (e.g.: one course per week)
- Include repeating the same diseases within an assignment varying with other diseases of differential diagnostic importance (e.g.: appendicitis, celiac disease, appendicitis)
- Identify the most challenging diagnoses or tests the analytical tools of InSimu. e-assign similar cases to strengthen memory imprinting (effects of repeated experience). (e.g.: student results on each patient included in the course are summarised on the educator portal of InSimu)
About the Author
Peter Hamar, MD, PhD, is full professor at Semmelweis University. His scientific interests in understanding novel mechanisms induced by modulated electro hyperthermia in triple-negative breast cancer. Furthermore, initiating from a strong background in renal transplantation currently, we try to identify key mechanisms of post-ischemic renal fibrosis. Besides being a PI at Semmelweis, P. Hamar intensely collaborates with several research groups including the Immune Disease Institute at Harvard Medical School, Boston, USA. They were the first to harness RNA interference for the kidney (PNAS). Furthermore, they demonstrated endosomal escape of siRNA from lipid nanoparticles with high-resolution microscopy (Nat Genet). P. Hamar has co-authored 89 original papers (google scholar). Teaching activity: teaching pathophysiology, ECG, hematology, laboratory medicine and translational medicine for graduate students at Semmelweis and Pécs Medical Universities since 1994 regularly in Hungarian, English and German languages. Specialization: Clinical Laboratory Diagnostics (2001).
References
(1) Waldman, J. Deane, Yourstone, Steven A., Smith, Howard L.: Learning Curves in Health Care. Health Care Management Review 2003: 28/1, 41 – 54
(2) Pusic MV et al. Learning curves in health professions education. Acad Med. 2015;90(8): 1034-1042.
(3) Hermann Ebbinghaus (1885): Memory: A Contribution to Experimental Psychology.
(4) Rob Waring: In defense of learning words in word pairs: But only when doing it the ‘right’ way! 2004.
(5) Thompson, Britta M.; Rogers, John C.: Exploring the Learning Curve in Medical Education: Using Self-Assessment as a Measure of Learning. Academic Medicine: 2008, 83,10: 86-88. doi: 10.1097/ACM.0b013e318183e5fd
(6) Naomi Winstone, David Carless: A Learning-Focused Approach. in. Designing Effective Feedback Processes in Higher Education. 2019, ISBN 9780815361633.
(7) Sarah Waliany, Wendy Caceres, Sylvia Bereknyei Merrell, Sonoo Thadaney, Noelle Johnstone & Lars Osterberg: Preclinical curriculum of prospective case-based teaching with faculty- and student-blinded approach, BMC Medical Education
(8) Olivier Courteille, Madelen Fahlstedt, Johnson Ho, Leif Hedman, Uno Fors, Hans von Holst, Li Felländer-Tsai, Hans Möller: Learning through a virtual patient vs. recorded lecture: a comparison of knowledge retention in a trauma case. Int J Med Educ. 2018 Mar 28;9:86-92. doi: 10.5116/ijme.5aa3.ccf2.
(9) Lukas B. Seifert, Octavian Socolan, Robert Sader, Miriam Rüsseler & Jasmina Sterz: Virtual patients versus small-group teaching in the training of oral and maxillofacial surgery: a randomized controlled trial. BMC Medical Education volume 19, 2019.
