Professionalism Pulse: Development and Validation of a Natural Language Processing Pipeline and Dashboard for Safety Culture Surveillance in NYC Health + Hospitals

Background: Professionalism and effective communication are foundational determinants of patient safety and quality of care. Unprofessional behaviors frequently serve as active precursors to adverse clinical events. However, proactive organizational surveillance is often hindered because incident feedback exists primarily as unstructured, free-text data. This study aimed to develop and validate a Natural Language Processing (NLP) pipeline and interactive dashboard to proactively monitor the "professionalism climate" within NYC Health + Hospitals, the largest municipal healthcare delivery system in the United States. Methods: A high-fidelity synthetic dataset (N=400) was computationally generated to safely mirror historical incident logs across 11 acute facilities without utilizing Protected Health Information (PHI). A rule-based NLP pipeline was developed in R utilizing the tidytext package. Unstructured narrative feedback was tokenized and classified into three core domains: Respect, Safety, and Communication. To validate the pipeline's accuracy, a 25% random stratified sample (n=100) was evaluated against independent, blinded manual coding performed by two reviewers, with inter-rater reliability measured via Cohen's Kappa. Finally, an interactive Tableau dashboard was developed to operationalize and visualize these metrics for ongoing surveillance. Results: The NLP algorithm achieved an overall accuracy of 85.8% (95% CI: 79.0-92.6), with 81.2% sensitivity and 88.9% specificity. The highest domain-specific performance was observed in Communication (88.0% accuracy). Manual validation demonstrated strong inter-rater reliability (k=0.84). Operational analysis via the dashboard revealed that 61.8% of reports occurred during the Tour 2 shift (15:00 to 23:00), aligning with peak operational volume. Furthermore, Respect-related feedback was reported at a disproportionately high frequency during the Tour 3 shift (23:00 to 07:00), accounting for over 50.7% of overnight feedback submissions. Conclusion: Rule-based NLP successfully transforms qualitative healthcare feedback into structured, actionable intelligence with high specificity. Integrating this pipeline into operational dashboards transitions safety culture surveillance from a reactive, manual exercise to a proactive, scalable system, enabling targeted, data-driven interventions by hospital leadership.