Applying Artificial Intelligence to Electronic Health Record Data to Advance Symptom Phenotyping: A Brief Practical Guide

Symptom science has long been central to nursing practice, as symptoms are often the primary reason patients seek care. Nurses have historically relied on emerging research and clinical intuition to predict patient outcomes. Artificial intelligence (AI) and machine learning (ML) can now assist in developing phenotypes and may help nurses operationalize patients at risk. As AI technologies become more integrated into healthcare, the use of electronic health record (EHR) data for symptom phenotyping is emerging as a promising area for advancing care. However, nursing documentation remains underutilized in AI model development. This article provides a practical guide for nurses interested in using AI and ML to develop symptom phenotypes, covering key areas such as data availability, patient safety, model building, and commercialization. It highlights the potential of AI to accelerate symptom science, shorten diagnostic timelines, and improve patient outcomes by uncovering new treatment targets. While nurses typically lack formal training in AI, they possess valuable clinical expertise and are key collaborators in AI-driven research. This guide aims to empower nurses to engage in the development and implementation of AI solutions, ultimately contributing to more precise, individualized care and transformative advancements in healthcare.