Enhancing oncology nursing care planning for patients with cancer through Harnessing large language models

Abstract

Nursing care planning is a critical and daily task in managing and treating patients with cancer. It involves the systematic nursing assessment, diagnosis, planning, interventions, and evaluation of care plans tailored to the unique needs of patients with cancer.1,2 The advent of large language models (LLMs) signifies a leap in the field of natural language processing. These advanced models, like the generative pre-trained transformer-4 (GPT-4) developed by OpenAI, can generate human-like text based on the input they receive. GPT-4 is simply a special design that uses deep learning to anticipate and generate conversational language. Therefore, this presents a transformative opportunity for oncology nursing care planning. It could revolutionize decision-making, patient education, and care coordination, thereby elevating the standard of care provided.3 The incorporation of LLMs in nursing care planning is underpinned by the model's ability to analyze vast amounts of data rapidly and efficiently. GPT-4, for instance, can be trained to understand the latest evidence-based practices in oncology, which can facilitate timely and precise clinical decision-making.4 Consequently, nurses can provide personalized care based on the most up-to-date research and guidelines without manually sifting through the ever-growing medical and nursing literature.5 Additionally, LLMs can synthesize patient data and provide predictive insights. By integrating Electronic Health Records (EHRs), LLMs can assess the patient's history and current status and then predict potential complications or treatment responses, utilizing precise clinical decisions based on the retrieved information about the patient's condition.6 This enables the formulation of proactive care plans that address current needs and anticipate and mitigate future challenges. Moreover, patient education is an indispensable element of oncology nursing care planning. LLMs can generate tailored educational materials for patients and their families, adapted to their language proficiency and comprehension levels.7 Providing easy-to-understand information regarding their condition, treatment options, and self-care strategies empowers patients to participate in self-care management effectively, improving adherence and outcomes. Furthermore, LLMs can help streamline communication and coordination among multidisciplinary teams involved in the care of cancer patients.8 With their natural language processing capabilities, these models can facilitate the translation of complex medical jargon into layperson's terms or convert textual data into visual representations. This can enhance collaborative efforts by ensuring that all team members, including those from non-medical backgrounds, clearly understand the patient's condition and care plan.

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