Improving clinical communication education for medical undergraduates using AI simulation: development and evaluation of Totipotent Interactive Patient Simulation (TIPS)

Outline of work:

This study presents and evaluates Totipotent Interactive Patient Simulation (TIPS), an AI tool aimed at enhancing medical undergraduate clinical communication skills. TIPS is able to simulate a wide range of patient profiles, enabling students to engage in interactive, low-risk medical history-taking scenarios.

Background:

Effective clinical communication is a fundamental skill for medical students, but traditional training methods often face challenges, including limited access to diverse patient interactions and the risk of errors in real-world settings. Advances in AI provide new opportunities to overcome these challenges. TIPS utilises AI technology to create scalable, personalised simulations that foster skill development in a safe, controlled environment.

Objective:

To evaluate the effectiveness of TIPS in enhancing medical students' communication skills.

Participants: Approximately 180 undergraduate medical students recruited through email, in-person announcements and social media.

Methods:

A quantitative study will be conducted using pre- and post-intervention questionnaires, students' assessment results, and software analytics. Data will be stored in pseudonymised form to enable pairing with future datasets and to accommodate participant withdrawal.

Key anticipated outcomes:

Improved communication skills, greater familiarity with AI tools and enhanced telehealth proficiency among students. The study also aims to provide valuable insights into the integration of AI-based training tools within medical curricula and their wider impact on healthcare education. TIPS is expected to enhance clinical communication skills, such as history-taking and telehealth practices.