ABSTRACT
PURPOSE: During the coronavirus disease 2019 pandemic, face-to-face teaching has been severely disrupted and limited for medical students internationally. This study explores the views of medical students and academic medical staff regarding the suitability and limitations, of a bespoke chatbot tool to support medical education. METHODS: Five focus groups, with a total of 16 participants, were recruited using a convenience sample. The participants included medical students across all year groups and academic staff. The pre-determined focus group topic guide explored how chatbots can augment existing teaching practices. A thematic analysis was conducted using the transcripts to determine key themes. RESULTS: Thematic analysis identified five main themes: (1) chatbot use as a clinical simulation tool; (2) chatbot use as a revision tool; (3) differential usefulness by medical school year group; (4) standardisation of education and assessment; (5) challenges of use and implementation. CONCLUSIONS: Both staff and students have clear benefits from using chatbots in medical education. However, they documented possible limitations to their use. The creation of chatbots to support the medical curriculum should be further explored and urgently evaluated to assess their impact on medical students training both during and after the global pandemic.
ABSTRACT
Introduction: We report a pathfinder study of AI/knowledge engineering methods to rapidly formalise COVID-19 guidelines into an executable model of decision making and care pathways. The knowledge source for the study was material published by BMJ Best Practice in March 2020. Methods: The PROforma guideline modelling language and OpenClinical.net authoring and publishing platform were used to create a data model for care of COVID-19 patients together with executable models of rules, decisions and plans that interpret patient data and give personalised care advice. Results: PROforma and OpenClinical.net proved to be an effective combination for rapidly creating the COVID-19 model; the Pathfinder 1 demonstrator is available for assessment at https://www.openclinical.net/index.php?id=746. Conclusions: This is believed to be the first use of AI/knowledge engineering methods for disseminating best-practice in COVID-19 care. It demonstrates a novel and promising approach to the rapid translation of clinical guidelines into point of care services, and a foundation for rapid learning systems in many areas of healthcare.