Exploring the Relationship Between Nurse Empathy and Patient Satisfaction in Acute Care Settings

Posted: Jun 02, 2022

• Kayden Pierce
• Luna Matthews
• Daisy Grant

Abstract

This research investigates the complex relationship between nurse empathy and patient satisfaction in acute care environments through a novel computational empathy assessment framework. Traditional studies in healthcare have relied on self-report measures and patient surveys, which are subject to recall bias and social desirability effects. Our study introduces an innovative multimodal approach that combines natural language processing of nurse-patient interactions, facial expression analysis, and physiological synchrony measurements to quantify empathy in real-time clinical settings. We developed the Computational Empathy Assessment Tool (CEAT), which captures both verbal and non-verbal empathy indicators during clinical encounters. The research was conducted across three acute care units in urban hospitals, involving 45 nurses and 210 patient encounters. Our findings reveal that empathy manifests in distinct patterns across different phases of clinical interactions, with the highest correlation to patient satisfaction occurring during information delivery and procedural explanations rather than during initial assessments. Surprisingly, we discovered that excessive verbal empathy expressions during critical decision-making moments correlated negatively with patient confidence in care. The study also identified specific linguistic markers and paralinguistic features that predict patient satisfaction with 87