Precision Learning in Medical Education: Virtual Patient in Training for Clinical Reasoning
Tsuen-Chiuan Tsai (Charity)
Co-Founder, Landseed Asia Meducation Company
Clinical diagnosis has been always considered a key factor for healthcare quality and patient safety. With the advances in biomedical technology, providing healthcare became risky. Teaching and assessment on clinical reasoning for healthcare providers have been considered important. Clinical reasoning underlying diagnosis is a complex system, which involves not only medical knowledge and knowledge structure, but also the problem-solving strategies that are applied. Assessing students’ capacities of making clinical diagnosis and its reasoning has been considered difficult and expensive. Virtual patient (VP) incorporating technology of natural language processing (NLP) can “understand and responds” toward player’s inquiry, plus presenting information for physical examination, laboratory tests and/or image studies. The system records all the data and diagnosis derived from the doctor-VP interaction, and candidates are asked to categorize those data at the end of examination. The VP-system have now been introduced to simulate clinical encounters to trigger the interests on learning medical diagnosis. Further, the VP-based teaching or examination can be delivered to a large group, and generate scores and feedback in an individual level. Therefore, with the uses of VPs, the teachers can be empowered to teach proactively, and precision learning on personal skills became possible. The validation of VP-based learning revealed good results, being effective and time/cost saving.