Interdisciplinary Journal of Nursing and Health (eISSN 3103-537X) continues Infermieristica Journal (eISSN 2785-7018).
Prompting for Healthcare Professionals: Enhancing Clinical Decision-Making with Artificial Intelligence
PDF

Keywords

Artificial Intelligence
Healthcare Professionals
Prompt Engineering

How to Cite

Prompting for Healthcare Professionals: Enhancing Clinical Decision-Making with Artificial Intelligence. (2025). Interdisciplinary Journal of Nursing and Health, 4(1), 33-39. https://doi.org/10.36253/if-3198

Abstract

Introduction. Generative Artificial Intelligence (AI), specifically through Large Language Models (LLMs), is progressively reshaping clinical documentation, decision support, patient education, and research synthesis in healthcare. Despite significant benefits, these models pose challenges such as inaccuracies (hallucinations) and inherent biases. This paper highlights prompt engineering as an emerging and critical skill for healthcare professionals and demonstrates how structured prompting techniques can improve the reliability, clinical relevance, and ethical compliance of AI-driven applications.

Methods. A systematic review of recent literature was conducted to present structured prompt engineering methodologies specifically tailored to healthcare settings. Advanced prompting techniques, including chain-of-thought reasoning, zero-shot and few-shot prompting, and self-consistency strategies, were examined.

Results. The proposed structured approach encompasses clear objective definition, precise contextualization, integration of domain-specific knowledge, iterative refinement, and ethical risk mitigation. Practical guidelines are provided for designing prompts suitable for clinical scenarios, such as diagnostic decisions, patient-specific therapeutic protocols, and administrative tasks. Notably, advanced techniques such as chain-of-thought reasoning and self-consistency effectively reduce inaccuracies and enhance clinical decision-making.

Discussion and Conclusion. The structured integration of prompt engineering optimizes clinical decision-making and supports adherence to evidence-based practices. Incorporating prompt engineering into healthcare educational programs and fostering interdisciplinary collaboration are crucial for the responsible implementation of generative AI. These advancements have far-reaching implications for improving clinical effectiveness, enhancing patient care quality, and elevating the standards of healthcare education and professional practice.

PDF

References

Shah K, Xu AY, Sharma Y, et al. Large language model prompting techniques for advancement in clinical medicine. J Clin Med. 2024;13(17):5101. doi:10.3390/jcm13175101

Patil R, Heston TF, Bhuse V. Prompt engineering in healthcare. Electronics. 2024;13:2961. doi:10.3390/electronics13152961

Meskó B. Prompt engineering as an important emerging skill for medical professionals: Tutorial. J Med Internet Res. 2023;25:e50638. doi:10.2196/50638

Nazi ZA, Peng W. Large language models in healthcare and medical domain: A review. Informatics. 2024;11:57. doi:10.3390/informatics11030057

Habib MM, Hoodbhoy Z, Siddiqui MAR. Knowledge, attitudes, and perceptions of healthcare students and professionals on the use of artificial intelligence in healthcare in Pakistan. PLoS Digit Health. 2024;3(5):e0000443. doi:10.1371/journal.pdig.0000443

Hendawi S, Kanan T, Elbes M, Mughaid A, AlZu’bi S. Automated prompt engineering pipelines: Fine-tuning LLMs for enhanced response accuracy [Preprint]. SSRN. Published 2024. doi:10.2139/ssrn.5004054

Chen CJ, Liao CT, Tung YC, Liu CF. Enhancing healthcare efficiency: integrating ChatGPT in nursing documentation. Stud Health Technol Inform. 2024;316:851-852. doi:10.3233/SHTI240545

Abhari S, Fatahi S, Saragadam A, Chumachenko D, Pelegrini Morita P. A road map of prompt engineering for ChatGPT in healthcare: A perspective study. Stud Health Technol Inform. 2024;316:998-1002. doi:10.3233/SHTI240578

Cai CJ, Wang H, Wang M, Agrawal RK, Bero N. Prompting is all you need: LLMs for systematic review screening. MedRxiv. Preprint. Published 2024. doi:10.1101/2024.06.01.24308323

Zaghir J, Naguib M, Bjelogrlic M, et al. Prompt engineering paradigms for medical applications: Scoping review. J Med Internet Res. 2024;26:e60501. doi:10.2196/60501

Huang M, Sangi-Haghpeykar H. Prompt engineering in medical education. Int Med Educ. 2023;2(3):198-205. doi:10.3390/ime2030019

Garante per la protezione dei dati personali. Decalogo per la realizzazione di servizi sanitari nazionali attraverso sistemi di Intelligenza Artificiale. October 10, 2023.

Hsieh CJ, Si S, Yu F, Dhillon IS. Automatic engineering of long prompts. In: Findings of the Association for Computational Linguistics: ACL 2024. Bangkok, Thailand: Association for Computational Linguistics; 2024:10672-1085.

Atil B, Chittams A, Fu L, Ture F, Xu L, Baldwin B. LLM stability: a detailed analysis with some surprises. arXiv preprint arXiv:2408.04667. Published 2024.

Zhuo J, Zhang S, Fang X, et al. ProSA: Assessing and understanding the prompt sensitivity of LLMs. In: Findings of the Association for Computational Linguistics: EMNLP 2024. Miami, Florida, USA: Association for Computational Linguistics; 2024:1950-1976.

Ho CN, Tian T, Ayers AT, et al. Qualitative metrics from the biomedical literature for evaluating large language models in clinical decision-making: a narrative review. BMC Med Inform Decis Mak. 2024;24:357. doi:10.1186/s12911-024-02757-z

Tam TYC, Sivarajkumar S, Kapoor S, et al. A literature review and framework for human evaluation of generative large language models in healthcare. arXiv preprint arXiv:2405.02559. Published 2024.

Zhang W, Shen Y, Wu L, et al. Self-Contrast: Better reflection through inconsistent solving perspectives. arXiv preprint arXiv:2401.02009. Published 2024.

Puerto H, Chubakov T, Zhu X, Tayyar Madabushi H, Gurevych I. Fine-tuning with divergent chains of thought boosts reasoning through self-correction in language models. OpenReview. Published September 24, 2024. Updated December 5, 2024. https://openreview.net/forum?id=u4whlT6xKO

Huang S, Ma Z, Du J, et al. Mirror-consistency: Harnessing inconsistency in majority voting. arXiv preprint arXiv:2410.10857. Published 2024.

Google Research. NotebookLM: AI-powered research and writing tool [Internet]. Google; 2023. Accessed February 7, 2025. https://notebooklm.google

OpenAI. ChatGPT: AI language model with custom knowledge retrieval [Internet]. OpenAI; 2023. Accessed February 7, 2025. https://openai.com/chatgpt

World Health Organization. Regulatory considerations on artificial intelligence for health. Geneva: WHO; 2023:26-31. https://iris.who.int/handle/10665/373421

Neha F, Bhati D, Shukla DK, Amiruzzaman M. ChatGPT: Transforming healthcare with AI. AI. 2024;5(4):2618-2650. doi:10.3390/ai5040126

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2025 Antonio Alemanno, Michele Carmone, Leonardo Priore

Interdisciplinary Journal of Nursing and Health (eISSN: 3103-537X) continues Infermieristica Journal (eISSN 2785-7018).