This study investigates the application of Valence Aware Dictionary and Sentiment Reasoner (VADER), a rule-based sentiment analysis tool, for rapidly analyzing patient complaints in healthcare environments. By leveraging VADER's efficiency in processing free-text data, the research examines how sentiment analysis can support digital transformation and enhance patient experience management. The study assesses VADER's effectiveness in detecting emotional tone within patient feedback, enabling early identification of systemic issues and informing data-driven decision-making. The dataset comprises 63 written complaints collected throughout 2024 from a key surgery service in a 10 000-employee public hospital in Portugal. Sentiment analysis was conducted using Orange text mining tools integrated with VADER. Three core findings highlight VADER's value: (a) its balance of efficiency and accuracy in sentiment classification, (b) its contribution to patient-centered care strategies, and (c) its support for process optimization, particularly in triage and prioritization workflows. These insights demonstrate how sentiment analysis can help build smarter, more responsive healthcare systems through enhanced digital capabilities.

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Natural language processing,Sentiment analysis,Text classification,VADER sentiment analysis,Healthcare,Patient experience,Patient complaints