TY - JOUR
T1 - Conversational Artificial Intelligence Assistant for Generating Structured Quality Improvement Reports from Incident Narratives, Safety Event Classifications, Root-Cause Analysis Notes, and Performance Metrics
AU - Ivan Petrov
AU - Olga Ivanova
AU - Dmitry Smirnov
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2025
VL - 5
IS - 1
DO - 10.68159/q456290795
SP - 143
N2 - Quality improvement reports distill incident narratives, safety classifications, root-cause analyses, and performance metrics into actionable learning documents. However, compiling these materials remains a manual, cognitively burdensome task that can delay organizational learning after safety events. Healthcare organizations often hold rich safety data across reporting systems, RCA documents, dashboards, and governance records. Yet these inputs are rarely transformed into standardized QI reports through a single coherent workflow. This article proposes a conversational artificial intelligence assistant that engages quality officers in a structured dialogue, retrieves relevant safety-event evidence, and generates a draft QI report following a pre-specified template. The assistant is conceptualized as a human-supervised system rather than an autonomous decision-maker. The proposed assistant includes an incident narrative NLP module, safety classification aligner, RCA note retriever, performance metric trend summarizer, and template-guided large language model. These components would support structured reporting while preserving human review and organizational accountability. The assistant could shorten the time from incident review to report drafting, improve reproducibility across QI documentation, and reduce administrative burden for patient safety teams. Its value would depend on careful grounding, privacy protection, verification workflows, and user trust. Conversational AI offers a pathway toward AI-augmented safety reporting that supports, rather than replaces, human expertise. The proposed model emphasizes structured synthesis, transparent evidence use, and a learning culture in healthcare quality improvement.
UR - https://cirpublications.com/q456290795
ER -