A guide to prompt design: foundations and applications for healthcare simulationists
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 develop...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1504532/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832581903742402560 |
|---|---|
| author | Sara Maaz Sara Maaz Janice C. Palaganas Gerry Palaganas Maria Bajwa |
| author_facet | Sara Maaz Sara Maaz Janice C. Palaganas Gerry Palaganas Maria Bajwa |
| author_sort | Sara Maaz |
| collection | DOAJ |
| description | 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, simulated person 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. |
| format | Article |
| id | doaj-art-7c2ebe4063c348b19444bf805a5ee748 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-7c2ebe4063c348b19444bf805a5ee7482025-01-30T09:47:39ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011110.3389/fmed.2024.15045321504532A guide to prompt design: foundations and applications for healthcare simulationistsSara Maaz0Sara Maaz1Janice C. Palaganas2Gerry Palaganas3Maria Bajwa4Department of Clinical Skills, College of Medicine, Alfaisal University, Riyadh, Saudi ArabiaDepartment of Health Professions Education, MGH Institute of Health Professions, Boston, MA, United StatesDepartment of Clinical Skills, College of Medicine, Alfaisal University, Riyadh, Saudi ArabiaDirector of Technology, AAXIS Group Corporation, Los Angeles, CA, United StatesDepartment of Clinical Skills, College of Medicine, Alfaisal University, Riyadh, Saudi ArabiaLarge 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, simulated person 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.https://www.frontiersin.org/articles/10.3389/fmed.2024.1504532/fullpromptprompt engineeringhealthcare simulationChatGPTartificial intelligencelarge language models |
| spellingShingle | Sara Maaz Sara Maaz Janice C. Palaganas Gerry Palaganas Maria Bajwa A guide to prompt design: foundations and applications for healthcare simulationists Frontiers in Medicine prompt prompt engineering healthcare simulation ChatGPT artificial intelligence large language models |
| title | A guide to prompt design: foundations and applications for healthcare simulationists |
| title_full | A guide to prompt design: foundations and applications for healthcare simulationists |
| title_fullStr | A guide to prompt design: foundations and applications for healthcare simulationists |
| title_full_unstemmed | A guide to prompt design: foundations and applications for healthcare simulationists |
| title_short | A guide to prompt design: foundations and applications for healthcare simulationists |
| title_sort | guide to prompt design foundations and applications for healthcare simulationists |
| topic | prompt prompt engineering healthcare simulation ChatGPT artificial intelligence large language models |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1504532/full |
| work_keys_str_mv | AT saramaaz aguidetopromptdesignfoundationsandapplicationsforhealthcaresimulationists AT saramaaz aguidetopromptdesignfoundationsandapplicationsforhealthcaresimulationists AT janicecpalaganas aguidetopromptdesignfoundationsandapplicationsforhealthcaresimulationists AT gerrypalaganas aguidetopromptdesignfoundationsandapplicationsforhealthcaresimulationists AT mariabajwa aguidetopromptdesignfoundationsandapplicationsforhealthcaresimulationists AT saramaaz guidetopromptdesignfoundationsandapplicationsforhealthcaresimulationists AT saramaaz guidetopromptdesignfoundationsandapplicationsforhealthcaresimulationists AT janicecpalaganas guidetopromptdesignfoundationsandapplicationsforhealthcaresimulationists AT gerrypalaganas guidetopromptdesignfoundationsandapplicationsforhealthcaresimulationists AT mariabajwa guidetopromptdesignfoundationsandapplicationsforhealthcaresimulationists |