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HYPOTHESIS AND THEORY article

Front. Med.
Sec. Healthcare Professions Education
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1504532
This article is part of the Research Topic Innovations in Teaching and Learning for Health Professions Educators View all 8 articles

A Guide to Prompt Design: Foundations and Applications for Healthcare Simulationists

Provisionally accepted
Sara Maaz Sara Maaz 1,2Janice Palaganas Janice Palaganas 2Gerry Palaganas Gerry Palaganas 3Maria Bajwa Maria Bajwa 2*
  • 1 Alfaisal University, Riyadh, Saudi Arabia
  • 2 MGH Institute of Health Professions, Boston, United States
  • 3 AAXIS Group Corporation, California, United States

The final, formatted version of the article will be published soon.

    Large Language Models (LLMs) like ChatGPT, Gemini, and Claude gain traction in healthcare simulation; this paper offers simulationists a practical guide to effective prompt design. Grounded in a structured literature review and iterative prompt testing, this paper proposes best practices for developing calibrated prompts, explores various prompt types and techniques with use cases, and addresses the challenges, including ethical considerations for using LLMs in healthcare simulation.This guide helps bridge the knowledge gap for simulationists on LLM use in simulation-based education, offering tailored guidance on prompt design. Examples were created through iterative testing to ensure alignment with simulation objectives, covering use cases such as clinical scenario development, OSCE station creation, standardized patient scripting, and debriefing facilitation. These use cases provide easy-to-apply methods to enhance realism, engagement, and educational alignment in simulations. Key challenges associated with LLM integration, including bias, privacy concerns, hallucinations, lack of transparency, and the need for robust oversight and evaluation, are discussed alongside ethical considerations unique to healthcare education. Recommendations are provided to help simulationists craft prompts that align with educational objectives while mitigating these challenges. By offering these insights, this paper contributes valuable, timely knowledge for simulationists seeking to leverage generative AI's capabilities in healthcare education responsibly.

    Keywords: Prompt, Prompt Engineering, Healthcare simulation, ChatGPT, artificial intelligence, Large language models, LLM, Generative AI

    Received: 30 Sep 2024; Accepted: 17 Dec 2024.

    Copyright: © 2024 Maaz, Palaganas, Palaganas and Bajwa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Maria Bajwa, MGH Institute of Health Professions, Boston, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.