Generative Simulation and Summarization of Neonatal Patient Data

In the Neonatal Intensive Care Unit (NICU), clinicians must balance the demands of constant patient monitoring with the need for precise documentation and clear communication with colleagues and families. To address the clinical burden of documenting patient care and health status, this paper presents two complementary AI-based systems. First, a GAN-driven NICU Patient Simulator is developed to generate realistic neonatal vital sign data and discrete clinical intervention events, typical of care in the NICU. While useful for a variety of research goals, this simulator provides a safe and controllable data source essential for the development and validation of the second system: the LLM-powered Neonatal Patient Status Summarizer (NPSS). The NPSS fuses the output of multiple machine learning systems, each extracting specific aspects of patient care and health, together with vital sign data from a patient monitor. Leveraging Retrieval-Augmented Generation (RAG) to incorporate neonatal-specific reference data, the NPSS enables several key use cases, including generating parent-friendly updates, summarizing patient status for clinician handovers, and automatically populating patient records for charting. Simulator validation demonstrates the high fidelity of the simulated data relative to available infant data in Physionet. The NPSS is evaluated using an automated LLM-as-judge framework across repeated test scenarios. To mitigate self-preference bias, evaluations were conducted using three distinct LLM judges (OpenAI o3-mini, Llama-3, and Mistral). Across judges, the NPSS achieved consistently high relevance scores (0.95–0.99) and strong groundedness scores (0.80–0.91), indicating that generated summaries remain on-topic and faithful to the underlying simulator data. Once validated, the NPSS will reduce charting workload, improve shift handover efficiency, and streamline parental updates, addressing key clinical bottlenecks in NICU data workflows.