Assessing the role of clinical summarization and patient chart review within communications, medical management, and diagnostics
Chanseo Lee, Kimon-Aristotelis Vogt, Sonu Kumar
TL;DR
This review assesses how clinical summarization of unstructured EHR data influences diagnostic accuracy, communication, and patient management, emphasizing the substantial time burden and risk of information overload faced by clinicians. It surveys evidence on chart review effectiveness and safety implications, and analyzes AI-enabled approaches—particularly large-language models and NLP frameworks—that can automate or augment summarization. Key contributions include demonstrations that AI can match or exceed clinician performance on chart summarization in controlled settings, methodological advances like the SPeC framework, and the use of datasets such as MIMIC for training and evaluation. The paper also identifies critical challenges, including privacy, data quality, system integration, and ethical considerations, and outlines directions to advance AI-assisted clinical summarization toward improving patient outcomes and reducing clinician burden.
Abstract
Effective summarization of unstructured patient data in electronic health records (EHRs) is crucial for accurate diagnosis and efficient patient care, yet clinicians often struggle with information overload and time constraints. This review dives into recent literature and case studies on both the significant impacts and outstanding issues of patient chart review on communications, diagnostics, and management. It also discusses recent efforts to integrate artificial intelligence (AI) into clinical summarization tasks, and its transformative impact on the clinician's potential, including but not limited to reductions of administrative burden and improved patient-centered care.