Teaching clinical decision making is the cornerstone of medical education. Although this teaching often occurs during direct patient care activities, students interact with only a limited number of patients. Therefore, simulation with online cases has been recommended. One type of simulation, the branched-narrative virtual patient (VP) contains multiple pathways through the case, and the pathways are determined by on the learners’ choices. Learners base their therapeutic decisions on the VP’s history, physical examination, and laboratory findings. They see the results of their decisions, make new choices based on the results, receive feedback, and thereby learn and practice clinical decision making skills. Although branched-narrative VPs are difficult to create, they have real-time decision-making features that may improve the learning of clinical decision making. To date, there have been no studies that compare branched-narrative VPs to traditional linear-narrative computer-assisted instruction with only one pathway (linear- narrative VP). The main objective of this project is to compare the effectiveness of using a branched-narrative VP versus a linear-narrative VP to simulate a patient with diabetic ketoacidosis. Funds requested will be used to complete the development of a branched-narrative VP and to develop a linear-narrative VP, including case vignettes, questions with feedback, explanatory instructional material, pretests, posttests, surveys, and reports on scores. Groups of third-year medical students will be randomly assigned to complete one or the other VP, and the effectiveness of the VPs will be assessed with validated posttests consisting of case-based, single-best-answer, multiple-choice questions that measure clinical decision making and with Likert-scale surveys that assesses student perceptions of the learning experience.

Virtual patients and high-fidelity simulation-based medical education (SBME) are both attractive means of meeting pediatric clerkship goals. The purpose of our project is to determine the benefits of integrating virtual patients (Computer-assisted Learning in Pediatrics, CLIPP) with SBME. The SBME course at MUSC consists of stations involving skills training and scenarios (basic airway, intravenous access, and lumbar puncture). CLIPP cases 19, 23 and 25 are the CLIPP cases most closely matched with our stations. Eighty students will complete these CLIPP cases before their SBME sessions; the remaining 80 students will complete these CLIPP cases after SBME. We will measure the student’s acquisition of medical knowledge, clinical reasoning, comfort and confidence. Medical knowledge will be measured with a multiple-choice exam. Clinical reasoning will be measured with multiple-choice questions and by testing the student’s ability to form differential diagnosis lists. Comfort and confidence (with skills and with forming differential diagnoses) will be measured with surveys. We will administer Survey 1 and Test 1 before the first SBME session (1/2 the students will have completed CLIPP cases). We will administer Survey 2 and Test 2 after SBME session 2. Student’s t-test, paired t-test, Wilcoxon Mann Whitney, Wilcoxon signed rank tests and multiple linear regression will be used. We hypothesize that students exposed to the virtual patients before SBME will have higher medical knowledge, clinical reasoning, comfort and confidence than students exposed to SBME alone. Finally, focus groups will be held to further explore the best means of integration.

Background/Rationale: Pattern recognition is a hallmark of clinical expertise. However, currently available cases are not designed to cultivate mastery of these skills in a systematic approach that provides feedback to reinforce learning and retention. We propose to develop a game-based learning system (iPAL-GAME), that integrates perceptual learning (PL) strategies to improve mastery of pattern recognition, with an adaptive learning (AL) technology that optimizes an individual’s learning capacity.

Project Goals: Our primary goal is to build a compelling online game platform and user interface for iPAL-GAME. Specifically, we will (1) develop perceptual learning practice modules for pattern discrimination among disease manifestations, physical exam findings and clinical test results and (2) accelerate learning and enhance long-term retention using an adaptive learning algorithm, game ramping, scaffolding, and reward mechanics.

Project Work: A team of clinicians, basic scientists, cognitive psychologists, game designers, programmers, instructional design experts and simulation educators will collaboratively (1) assemble a content library; (2) develop question banks and program the AL system; (3) design program the game user interface including the game ramping and reward structure; (4) link the game interface with AL system; and (5) pilot test iPAL-GAME with two complete case modules. iPAL-GAME has the potential to revolutionize the way we train our future healthcare providers in the current digital world.