DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits

2025-07-18

AI Summary

Clinical progress notes are severely underdocumented in electronic health records (EHRs)—only 8.56% of encounters in MIMIC-III contain such notes—resulting in fragmented longitudinal clinical narratives. To address this, we propose DENSE, a novel framework that introduces clinician-aware temporal alignment and semantic fusion. DENSE employs fine-grained clinical note classification, timeline-driven cross-encounter alignment, and clinical-knowledge-guided retrieval-augmented generation (RAG) to emulate physicians’ retrospective documentation behavior. Built upon large language models (LLMs), it enables structured integration and conditional generation of heterogeneous, multi-source clinical notes. Evaluated on multi-visit patient cohorts, DENSE achieves a temporal alignment ratio of 1.089 for generated progress notes—significantly surpassing the continuity of original EHR entries—and effectively restores longitudinal narrative coherence in fragmented EHRs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Progress notes are among the most clinically meaningful artifacts in an Electronic Health Record (EHR), offering temporally grounded insights into a patient's evolving condition, treatments, and care decisions. Despite their importance, they are severely underrepresented in large-scale EHR datasets. For instance, in the widely used Medical Information Mart for Intensive Care III (MIMIC-III) dataset, only about $8.56%$ of hospital visits include progress notes, leaving gaps in longitudinal patient narratives. In contrast, the dataset contains a diverse array of other note types, each capturing different aspects of care. We present DENSE (Documenting Evolving Progress Notes from Scattered Evidence), a system designed to align with clinical documentation workflows by simulating how physicians reference past encounters while drafting progress notes. The system introduces a fine-grained note categorization and a temporal alignment mechanism that organizes heterogeneous notes across visits into structured, chronological inputs. At its core, DENSE leverages a clinically informed retrieval strategy to identify temporally and semantically relevant content from both current and prior visits. This retrieved evidence is used to prompt a large language model (LLM) to generate clinically coherent and temporally aware progress notes. We evaluate DENSE on a curated cohort of patients with multiple visits and complete progress note documentation. The generated notes demonstrate strong longitudinal fidelity, achieving a temporal alignment ratio of $1.089$, surpassing the continuity observed in original notes. By restoring narrative coherence across fragmented documentation, our system supports improved downstream tasks such as summarization, predictive modeling, and clinical decision support, offering a scalable solution for LLM-driven note synthesis in real-world healthcare settings.

Problem

• Generates progress notes from scattered clinical data
• Addresses lack of progress notes in EHR datasets
• Improves longitudinal patient narrative coherence

Innovation

• Fine-grained note categorization for structured inputs
• Temporal alignment mechanism for chronological organization
• Clinically informed retrieval strategy for relevant content