This study focuses on exploring the effectiveness of using VADER (Valence Aware Dictionary and sEntiment Reasoner), a rule-based sentiment analysis tool, in swiftly analyzing patient complaints within healthcare settings aiming to develop overall digital capabilities. Currently, the use of sentiment analysis in the healthcare space is considered to be under-explored. Free-text complaints from patients often contain valuable insights into their experiences. However, analyzing large volumes of textual feedback can be time-consuming and prone to subjective interpretation. Sentiment analysis tools such as VADER can provide meaningful solutions by automatically extracting and quantifying the emotional tone of textual data. The dataset used in the study comprised the total number and written free-text of patient complaints collected during 2024, in a key surgery service of a 10,000 employee public hospital located in Portugal. Three key study findings demonstrate the benefits of using VADER to enhance healthcare patient experience management: (a) its ability to balance efficiency and accuracy in sentiment analysis without compromising precision, (b) its role in fostering a culture of patient-centered care decision-making, and (c) its support for process optimization and digitalization efforts, namely in terms of case triage and prioritization.

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Artificial Intelligence,Machine Learning,Customer Experience,Healthcare

This publication is associated with the following record:

• Sentiment analysis of patient complaints in Healthcare systems using Vader: Can it contribute to a better service?