Learning to diagnose accurately through virtual patients: do reflection phases have an added benefit?

Simulation-based learning with virtual patients is a highly effective method that could potentially be further enhanced by including reflection phases. The effectiveness of reflection phases for learning to diagnose has mainly been demonstrated for problem-centered instruction with text-based cases, not for simulation-based learning. To close this research gap, we conducted a study on learning history-taking using virtual patients. In this study, we examined the added benefit of including reflection phases on learning to diagnose accurately, the associations between knowledge and learning, and the diagnostic process.

A sample of N = 121 medical students completed a three-group experiment with a control group and pre- and posttests. The pretest consisted of a conceptual and strategic knowledge test and virtual patients to be diagnosed. In the learning phase, two intervention groups worked with virtual patients and completed different types of reflection phases, while the control group learned with virtual patients but without reflection phases. The posttest again involved virtual patients. For all virtual patients, diagnostic accuracy was assessed as the primary outcome. Current hypotheses were tracked during reflection phases and in simulation-based learning to measure diagnostic process.

Regarding the added benefit of reflection phases, an ANCOVA controlling for pretest performance found no difference in diagnostic accuracy at posttest between the three conditions, F(2, 114) = 0.93, p = .398. Concerning knowledge and learning, both pretest conceptual knowledge and strategic knowledge were not associated with learning to diagnose accurately through reflection phases. Learners' diagnostic process improved during simulation-based learning and the reflection phases.

Reflection phases did not have an added benefit for learning to diagnose accurately in virtual patients. This finding indicates that reflection phases may not be as effective in simulation-based learning as in problem-centered instruction with text-based cases and can be explained with two contextual differences. First, information processing in simulation-based learning uses the verbal channel and the visual channel, while text-based learning only draws on the verbal channel. Second, in simulation-based learning, serial cue cases are used to gather information step-wise, whereas, in text-based learning, whole cases are used that present all data at once.